diff --git a/PAPER_Conditional_probability_quantile_original_MEDMI_4_variables_quarter.Rout b/PAPER_Conditional_probability_quantile_original_MEDMI_4_variables_quarter.Rout
deleted file mode 100644
index 032428c5c48177604c058d50756698f3cef6073a..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_MEDMI_4_variables_quarter.Rout
+++ /dev/null
@@ -1,1232 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> #The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> #library(Hmisc)
->
-> #list.of.packages <- c("xts")
-> #new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-> #if(length(new.packages)) install.packages(new.packages)
-> library(xts)
-Loading required package: zoo
-
-Attaching package: ‘zoo’
-
-The following object is masked from ‘package:timeSeries’:
-
- time<-
-
-The following objects are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-
-Attaching package: ‘xts’
-
-The following objects are masked from ‘package:pastecs’:
-
- first, last
-
->
->
->
->
-> ## Varaible file
->
-> variable_int<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> width<-30
-> width_char<-paste(width)
->
->
->
-> variable<-"Maximum_air_temperature"
-> #variable<-"daylength"
-> variable_y<-"Mean_Precipitation"
-> variable_x<-"Relative_humidity"
->
-> #variable_y<-"Mean_Precipitation"
-> #variable<-"daylength"
-> #variable<-"Mean_Precipitation"
-> #"Maximum_air_temperature",
-> #"Minimum_air_temperature",
-> #"Mean_wind_speed",
-> #"Mean_Precipitation",
-> #"Relative_humidity",
-> #"daylength"
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
->
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-> colnames(Env_laboratory_weekly)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
->
-> Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-> Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
->
->
-> quarter_year<-4
-> quarter_year_char<-paste("_",as.character(quarter_year),"-quarter_year",sep="")
->
-> if (quarter_year==1){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=1 & month(as.Date(Env_Campylobacter_data_all2$Date))<=3)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=1 & month(as.Date(Env_laboratory_weekly$Date))<=3)
-+ }
->
-> if (quarter_year==2){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=4 & month(as.Date(Env_Campylobacter_data_all2$Date))<=6)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=4 & month(as.Date(Env_laboratory_weekly$Date))<=6)
-+ }
->
-> if (quarter_year==3){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=7 & month(as.Date(Env_Campylobacter_data_all2$Date))<=9)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=7 & month(as.Date(Env_laboratory_weekly$Date))<=9)
-+ }
->
-> if (quarter_year==4){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=10 & month(as.Date(Env_Campylobacter_data_all2$Date))<=12)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=10 & month(as.Date(Env_laboratory_weekly$Date))<=12)
-+ }
->
->
->
-> ############# Not elegant but it works for the rest
->
-> Env_laboratory_weekly_sub<-Env_laboratory_int1
-> Env_Campylobacter_data_all2_sub<-Env_Campylobacter_data_int1
->
->
->
->
-> for (quarter in c(1:4)){
-+
-+ quarter_char<-paste("_",as.character(quarter),"-quarter",sep="")
-+
-+ breaks_daylength<-(as.numeric(quantile(na.omit(Env_Campylobacter_data_all2_sub$daylength), probs=seq(0,1, by=0.25), na.rm=TRUE)))
-+ breaks_daylength[length(breaks_daylength)]<-ceiling((as.numeric(quantile(na.omit(Env_Campylobacter_data_all2_sub$daylength), probs=seq(0,1, by=0.25), na.rm=TRUE))))[length(breaks_daylength)]
-+
-+ breaks_daylength[1]<-floor((as.numeric(quantile(na.omit(Env_Campylobacter_data_all2_sub$daylength), probs=seq(0,1, by=0.25), na.rm=TRUE))))[1]
-+
-+
-+
-+ if (quarter==1){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2_sub,Env_Campylobacter_data_all2_sub$daylength<=breaks_daylength[2])
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly_sub,Env_laboratory_weekly_sub$daylength<=breaks_daylength[2])
-+ hours_char<-paste("_",as.character(round(breaks_daylength[2])),"-quarter",sep="")
-+ }
-+
-+ if (quarter==2){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2_sub,Env_Campylobacter_data_all2_sub$daylength>breaks_daylength[2] & Env_Campylobacter_data_all2_sub$daylength<=breaks_daylength[3])
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly_sub,Env_laboratory_weekly_sub$daylength>breaks_daylength[2] & Env_laboratory_weekly$daylength<=breaks_daylength[3])
-+ hours_char<-paste("_",as.character(round(breaks_daylength[3])),"-quarter",sep="")
-+ }
-+
-+ if (quarter==3){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2_sub,Env_Campylobacter_data_all2_sub$daylength>breaks_daylength[3] & Env_Campylobacter_data_all2_sub$daylength<=breaks_daylength[4])
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly_sub,Env_laboratory_weekly_sub$daylength>breaks_daylength[3] & Env_laboratory_weekly$daylength<=breaks_daylength[4])
-+ hours_char<-paste("_",as.character(round(breaks_daylength[4])),"-quarter",sep="")
-+ }
-+
-+ if (quarter==4){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2_sub,Env_Campylobacter_data_all2_sub$daylength>breaks_daylength[4])
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly_sub,Env_laboratory_weekly_sub$daylength>breaks_daylength[4])
-+ hours_char<-paste("_",as.character(round(breaks_daylength[5])),"-quarter",sep="")
-+ }
-+
-+
-+
-+
-+ ################### include latitude and longitude
-+ Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-+
-+
-+ lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-+ colnames(lat_long_lab)<-c("PostCode","lat","long")
-+
-+ Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-+ Env_laboratory_int3<-data.frame(Env_laboratory_int2)
-+
-+ Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-+ Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
-+
-+
-+
-+ ######################## include daylength ##################
-+
-+ PC_df<-data.frame(All_PC,as.Date(dates))
-+ colnames(PC_df)<-c("PostCode","Date")
-+
-+
-+ Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
-+
-+
-+ daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
-+
-+ latitude<-Post_Codes_df$lat
-+ day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
-+
-+ daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-+ daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-+ colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-+ daylength_df$Date<-as.factor(daylength_df$Date)
-+ daylength_df$lat<-as.factor(daylength_df$lat)
-+ Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-+ Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
-+
-+ #Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-+ #Env_laboratory<-data.frame(Env_laboratory_int4)
-+ Env_laboratory<-data.frame(Env_laboratory_int3)
-+ Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-+ Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
-+
-+
-+ #Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-+ #Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-+ Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
-+
-+
-+
-+
-+
-+ ################### Divide the domains of the variables in bins according to quantiles
-+
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+ index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-+ index_x_C<-which (names(Env_Campylobacter_data)==variable_x)
-+
-+ index_res_C<-which (names(Env_Campylobacter_data)=="residents")
-+
-+
-+ index<-which (names(Env_laboratory)==variable)
-+ index_y<-which (names(Env_laboratory)==variable_y)
-+ index_x<-which (names(Env_laboratory)==variable_x)
-+ index_res<-which (names(Env_laboratory)=="residents")
-+
-+
-+ #########################
-+
-+
-+ breaks_z_lab<-function(variable,by_z)
-+ {
-+
-+ index<-which (names(Env_laboratory)==variable)
-+
-+
-+ breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-+ breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
-+ breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-+
-+
-+ return(breaks_z)
-+
-+ }
-+
-+
-+
-+ breaks_z<-function(variable,by_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+
-+ breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-+
-+ breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
-+ breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-+
-+
-+ return(breaks_z)
-+
-+ }
-+
-+
-+
-+
-+ breaks_y_lab<-function(variable,variable_y,by_z,by_y,j_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+
-+ index<-which (names(Env_laboratory)==variable)
-+ index_y<-which (names(Env_laboratory)==variable_y)
-+
-+
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+ wt<-(findInterval(Env_laboratory[,index],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_laboratory_some<-Env_laboratory[ww,]
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ breaks_y<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE))
-+ breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
-+ breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-+
-+ }else{
-+
-+ breaks_y<-c()
-+ }
-+
-+ return(breaks_y)
-+ }
-+
-+
-+
-+ breaks_y<-function(variable,variable_y,by_z,by_y,j_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+ index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-+
-+
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ breaks_y<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE))
-+ breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
-+ breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-+
-+ }else{
-+
-+ breaks_y<-c()
-+ }
-+
-+ return(breaks_y)
-+ }
-+
-+
-+
-+ breaks_x_lab<-function(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)
-+ {
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+ index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-+
-+
-+ index<-which (names(Env_laboratory)==variable)
-+ index_y<-which (names(Env_laboratory)==variable_y)
-+ index_x<-which (names(Env_laboratory)==variable_x)
-+
-+ if(is.na(breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y])=='FALSE'){
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_y_C],breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ wt<-(findInterval(Env_laboratory[,index_y],breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_laboratory_some<-Env_laboratory[ww,]
-+
-+
-+ breaks_x<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_x]), probs=seq(0,1, by=by_x), na.rm=TRUE))
-+ breaks_x[length(breaks_x)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_x]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[length(breaks_x)]
-+ breaks_x[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_x]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[1]
-+
-+ } else {
-+
-+ breaks_x<-c()
-+ } } else {
-+
-+ breaks_x<-c()
-+
-+ }
-+
-+ return(breaks_x)
-+ }
-+
-+
-+ breaks_x<-function(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)
-+ {
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+ index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-+ index_x_C<-which (names(Env_Campylobacter_data)==variable_x)
-+
-+ if(is.na(breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y])=='FALSE'){
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_y_C],breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ wt<-(findInterval(Env_Campylobacter_data_some[,index_y_C],breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_Campylobacter_data_some2<-Env_Campylobacter_data_some[ww,]
-+
-+
-+ breaks_x<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some2[,index_x_C]), probs=seq(0,1, by=by_x), na.rm=TRUE))
-+ breaks_x[length(breaks_x)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some2[,index_x_C]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[length(breaks_x)]
-+ breaks_x[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some2[,index_x_C]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[1]
-+
-+ } else
-+ { breaks_x<-c()
-+ }
-+ }
-+ else {
-+
-+ breaks_x<-c()
-+
-+ }
-+
-+ return(breaks_x)
-+ }
-+
-+
-+ #################
-+
-+
-+
-+
-+ var_x_loc_df<-data.frame(character(), character(),character(),numeric(),numeric(),numeric())
-+ colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
-+
-+ residents_i_var<-0
-+ residents_universal<-0
-+ #i_var_max<-length(breaks_var)
-+ #i_var_min<-1
-+ #i_var_max_x<-length(breaks_var_x)
-+ #i_var_min_x<-1
-+
-+
-+ #####################
-+ by_z<-0.25
-+ by_y<-0.25
-+ by_x<-0.1
-+
-+ #i_var_min<-breaks_z(variable,by_z)[1]
-+ #i_var_max<-breaks_z(variable,by_z)[length(breaks_z(variable,by_z))]
-+ j_z_min<-1
-+ j_z_max<-length(breaks_z(variable,by_z))-1
-+
-+
-+
-+ for (j_z in c(j_z_min:j_z_max))
-+ {
-+
-+ if (length(Env_Campylobacter_data[,index_C])!=0){
-+
-+ wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-+
-+ wt<-(findInterval((Env_laboratory[,index]),breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_laboratory_z<-Env_laboratory[ww,]
-+
-+ if (length(Env_Campylobacter_data_z[,1])!=0){
-+ if (length(breaks_y(variable,variable_y,by_z,by_y,j_z))!=0){
-+
-+ j_y_min<-1
-+ j_y_max<-length(breaks_y(variable,variable_y,by_z,by_y,j_z))-1
-+
-+
-+
-+ for (j_y in c(j_y_min:j_y_max))
-+ {
-+
-+ wt<-(findInterval((Env_Campylobacter_data_z[,index_y_C]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_Campylobacter_data_y<-Env_Campylobacter_data_z[ww,]
-+
-+ wt<-(findInterval((Env_laboratory_z[,index_y]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_laboratory_y<-Env_laboratory_z[ww,]
-+
-+
-+ if (length(breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y))!=0){
-+
-+ j_x_min<-1
-+ j_x_max<- length(breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y))-1
-+ for (j_x in c(j_x_min:j_x_max))
-+ {
-+
-+
-+ wt<-(findInterval((Env_Campylobacter_data_y[,index_x_C]),breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)))
-+ ww<-which(wt==j_x)
-+ Yt1<-Env_Campylobacter_data_y[ww,c(1:3,index_C,index_y_C,index_x_C,index_res_C)]
-+
-+ wt<-(findInterval((Env_laboratory[,index_x]),breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)))
-+ ww<-which(wt==j_x)
-+ Y_tot<-Env_laboratory_y[ww,c(1:2,index,index_y,index_x,index_res)]
-+
-+ Total_cases<-sum((as.numeric(na.omit(Yt1$Cases))))
-+ residents<-sum((as.numeric(na.omit(Yt1$residents))))
-+ residents_tot<-sum((as.numeric(na.omit(Y_tot$residents))))
-+
-+ data_df<-data.frame(
-+ breaks_z(variable,by_z)[j_z],
-+ breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y],
-+ breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)[j_x],
-+ Total_cases,
-+ residents,
-+ residents_tot)
-+
-+
-+
-+
-+
-+ colnames(data_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
-+ var_x_loc_df<-rbind(var_x_loc_df,data_df)
-+ print(c(j_x,j_y,j_z, Total_cases))
-+
-+ }}}
-+ }
-+
-+
-+ }}
-+ }
-+
-+ write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",variable_x,"_",width_char,hours_char,quarter_year_char,"_Simulated_for_rec_original_MEDMI_quantile.csv",sep=""))
-+ }
-[1] 1 1 1 641
-[1] 2 1 1 679
-[1] 3 1 1 513
-[1] 4 1 1 494
-[1] 5 1 1 387
-[1] 6 1 1 381
-[1] 7 1 1 355
-[1] 8 1 1 330
-[1] 9 1 1 389
-[1] 10 1 1 553
-[1] 1 2 1 422
-[1] 2 2 1 331
-[1] 3 2 1 285
-[1] 4 2 1 236
-[1] 5 2 1 227
-[1] 6 2 1 438
-[1] 7 2 1 553
-[1] 8 2 1 552
-[1] 9 2 1 752
-[1] 10 2 1 1070
-[1] 1 3 1 372
-[1] 2 3 1 317
-[1] 3 3 1 322
-[1] 4 3 1 494
-[1] 5 3 1 492
-[1] 6 3 1 374
-[1] 7 3 1 465
-[1] 8 3 1 444
-[1] 9 3 1 545
-[1] 10 3 1 1105
-[1] 1 4 1 465
-[1] 2 4 1 931
-[1] 3 4 1 951
-[1] 4 4 1 360
-[1] 5 4 1 279
-[1] 6 4 1 160
-[1] 7 4 1 233
-[1] 8 4 1 156
-[1] 9 4 1 211
-[1] 10 4 1 1218
-[1] 1 1 2 412
-[1] 2 1 2 471
-[1] 3 1 2 594
-[1] 4 1 2 533
-[1] 5 1 2 681
-[1] 6 1 2 590
-[1] 7 1 2 529
-[1] 8 1 2 510
-[1] 9 1 2 403
-[1] 10 1 2 211
-[1] 1 2 2 242
-[1] 2 2 2 624
-[1] 3 2 2 541
-[1] 4 2 2 557
-[1] 5 2 2 541
-[1] 6 2 2 493
-[1] 7 2 2 422
-[1] 8 2 2 541
-[1] 9 2 2 440
-[1] 10 2 2 448
-[1] 1 3 2 168
-[1] 2 3 2 789
-[1] 3 3 2 862
-[1] 4 3 2 730
-[1] 5 3 2 695
-[1] 6 3 2 467
-[1] 7 3 2 345
-[1] 8 3 2 341
-[1] 9 3 2 582
-[1] 10 3 2 334
-[1] 1 4 2 856
-[1] 2 4 2 291
-[1] 3 4 2 431
-[1] 4 4 2 604
-[1] 5 4 2 686
-[1] 6 4 2 604
-[1] 7 4 2 524
-[1] 8 4 2 306
-[1] 9 4 2 240
-[1] 10 4 2 458
-[1] 1 1 3 170
-[1] 2 1 3 413
-[1] 3 1 3 450
-[1] 4 1 3 436
-[1] 5 1 3 403
-[1] 6 1 3 455
-[1] 7 1 3 575
-[1] 8 1 3 602
-[1] 9 1 3 699
-[1] 10 1 3 459
-[1] 1 2 3 465
-[1] 2 2 3 571
-[1] 3 2 3 533
-[1] 4 2 3 489
-[1] 5 2 3 649
-[1] 6 2 3 846
-[1] 7 2 3 690
-[1] 8 2 3 667
-[1] 9 2 3 367
-[1] 10 2 3 81
-[1] 1 3 3 680
-[1] 2 3 3 483
-[1] 3 3 3 450
-[1] 4 3 3 583
-[1] 5 3 3 667
-[1] 6 3 3 781
-[1] 7 3 3 837
-[1] 8 3 3 422
-[1] 9 3 3 175
-[1] 10 3 3 78
-[1] 1 4 3 801
-[1] 2 4 3 221
-[1] 3 4 3 241
-[1] 4 4 3 656
-[1] 5 4 3 632
-[1] 6 4 3 578
-[1] 7 4 3 514
-[1] 8 4 3 402
-[1] 9 4 3 496
-[1] 10 4 3 625
-[1] 1 1 4 1158
-[1] 2 1 4 337
-[1] 3 1 4 453
-[1] 4 1 4 482
-[1] 5 1 4 560
-[1] 6 1 4 406
-[1] 7 1 4 443
-[1] 8 1 4 392
-[1] 9 1 4 404
-[1] 10 1 4 303
-[1] 1 2 4 457
-[1] 2 2 4 409
-[1] 3 2 4 396
-[1] 4 2 4 381
-[1] 5 2 4 228
-[1] 6 2 4 202
-[1] 7 2 4 542
-[1] 8 2 4 943
-[1] 9 2 4 992
-[1] 10 2 4 731
-[1] 1 3 4 120
-[1] 2 3 4 383
-[1] 3 3 4 531
-[1] 4 3 4 403
-[1] 5 3 4 384
-[1] 6 3 4 493
-[1] 7 3 4 635
-[1] 8 3 4 875
-[1] 9 3 4 925
-[1] 10 3 4 615
-[1] 1 4 4 160
-[1] 2 4 4 658
-[1] 3 4 4 428
-[1] 4 4 4 325
-[1] 5 4 4 555
-[1] 6 4 4 619
-[1] 7 4 4 646
-[1] 8 4 4 866
-[1] 9 4 4 728
-[1] 10 4 4 603
-[1] 1 1 1 596
-[1] 2 1 1 361
-[1] 3 1 1 286
-[1] 4 1 1 206
-[1] 5 1 1 283
-[1] 6 1 1 245
-[1] 7 1 1 339
-[1] 8 1 1 792
-[1] 9 1 1 834
-[1] 10 1 1 820
-[1] 1 2 1 573
-[1] 2 2 1 474
-[1] 3 2 1 353
-[1] 4 2 1 445
-[1] 5 2 1 493
-[1] 6 2 1 649
-[1] 7 2 1 512
-[1] 8 2 1 577
-[1] 9 2 1 839
-[1] 10 2 1 379
-[1] 1 3 1 515
-[1] 2 3 1 805
-[1] 3 3 1 1143
-[1] 4 3 1 1005
-[1] 5 3 1 776
-[1] 6 3 1 333
-[1] 7 3 1 165
-[1] 8 3 1 120
-[1] 9 3 1 71
-[1] 10 3 1 73
-[1] 1 4 1 349
-[1] 2 4 1 873
-[1] 3 4 1 872
-[1] 4 4 1 473
-[1] 5 4 1 559
-[1] 6 4 1 834
-[1] 7 4 1 850
-[1] 8 4 1 562
-[1] 9 4 1 212
-[1] 10 4 1 42
-[1] 1 1 2 299
-[1] 2 1 2 297
-[1] 3 1 2 408
-[1] 4 1 2 579
-[1] 5 1 2 551
-[1] 6 1 2 614
-[1] 7 1 2 585
-[1] 8 1 2 481
-[1] 9 1 2 701
-[1] 10 1 2 703
-[1] 1 2 2 454
-[1] 2 2 2 445
-[1] 3 2 2 406
-[1] 4 2 2 407
-[1] 5 2 2 490
-[1] 6 2 2 327
-[1] 7 2 2 300
-[1] 8 2 2 609
-[1] 9 2 2 765
-[1] 10 2 2 751
-[1] 1 3 2 367
-[1] 2 3 2 482
-[1] 3 3 2 219
-[1] 4 3 2 187
-[1] 5 3 2 284
-[1] 6 3 2 533
-[1] 7 3 2 750
-[1] 8 3 2 724
-[1] 9 3 2 697
-[1] 10 3 2 1369
-[1] 1 4 2 390
-[1] 2 4 2 717
-[1] 3 4 2 688
-[1] 4 4 2 792
-[1] 5 4 2 609
-[1] 6 4 2 530
-[1] 7 4 2 416
-[1] 8 4 2 547
-[1] 9 4 2 770
-[1] 10 4 2 693
-[1] 1 1 3 562
-[1] 2 1 3 690
-[1] 3 1 3 669
-[1] 4 1 3 606
-[1] 5 1 3 630
-[1] 6 1 3 682
-[1] 7 1 3 759
-[1] 8 1 3 451
-[1] 9 1 3 135
-[1] 10 1 3 160
-[1] 1 2 3 540
-[1] 2 2 3 536
-[1] 3 2 3 654
-[1] 4 2 3 563
-[1] 5 2 3 535
-[1] 6 2 3 600
-[1] 7 2 3 541
-[1] 8 2 3 452
-[1] 9 2 3 441
-[1] 10 2 3 624
-[1] 1 3 3 213
-[1] 2 3 3 330
-[1] 3 3 3 356
-[1] 4 3 3 463
-[1] 5 3 3 478
-[1] 6 3 3 533
-[1] 7 3 3 980
-[1] 8 3 3 938
-[1] 9 3 3 670
-[1] 10 3 3 431
-[1] 1 4 3 958
-[1] 2 4 3 456
-[1] 3 4 3 469
-[1] 4 4 3 464
-[1] 5 4 3 428
-[1] 6 4 3 476
-[1] 7 4 3 657
-[1] 8 4 3 709
-[1] 9 4 3 657
-[1] 10 4 3 812
-[1] 1 1 4 625
-[1] 2 1 4 847
-[1] 3 1 4 932
-[1] 4 1 4 945
-[1] 5 1 4 664
-[1] 6 1 4 561
-[1] 7 1 4 351
-[1] 8 1 4 307
-[1] 9 1 4 370
-[1] 10 1 4 136
-[1] 1 2 4 657
-[1] 2 2 4 323
-[1] 3 2 4 558
-[1] 4 2 4 619
-[1] 5 2 4 710
-[1] 6 2 4 794
-[1] 7 2 4 695
-[1] 8 2 4 351
-[1] 9 2 4 306
-[1] 10 2 4 457
-[1] 1 3 4 948
-[1] 2 3 4 353
-[1] 3 3 4 186
-[1] 4 3 4 225
-[1] 5 3 4 414
-[1] 6 3 4 749
-[1] 7 3 4 517
-[1] 8 3 4 585
-[1] 9 3 4 803
-[1] 10 3 4 824
-[1] 1 4 4 864
-[1] 2 4 4 373
-[1] 3 4 4 747
-[1] 4 4 4 475
-[1] 5 4 4 526
-[1] 6 4 4 458
-[1] 7 4 4 386
-[1] 8 4 4 413
-[1] 9 4 4 820
-[1] 10 4 4 705
-[1] 1 1 1 831
-[1] 2 1 1 1051
-[1] 3 1 1 733
-[1] 4 1 1 803
-[1] 5 1 1 775
-[1] 6 1 1 593
-[1] 7 1 1 338
-[1] 8 1 1 67
-[1] 9 1 1 84
-[1] 10 1 1 10
-[1] 1 2 1 483
-[1] 2 2 1 715
-[1] 3 2 1 656
-[1] 4 2 1 731
-[1] 5 2 1 580
-[1] 6 2 1 536
-[1] 7 2 1 486
-[1] 8 2 1 225
-[1] 9 2 1 528
-[1] 10 2 1 252
-[1] 1 3 1 413
-[1] 2 3 1 554
-[1] 3 3 1 736
-[1] 4 3 1 953
-[1] 5 3 1 634
-[1] 6 3 1 612
-[1] 7 3 1 558
-[1] 8 3 1 478
-[1] 9 3 1 210
-[1] 10 3 1 255
-[1] 1 4 1 570
-[1] 2 4 1 429
-[1] 3 4 1 511
-[1] 4 4 1 585
-[1] 5 4 1 762
-[1] 6 4 1 879
-[1] 7 4 1 881
-[1] 8 4 1 794
-[1] 9 4 1 462
-[1] 10 4 1 176
-[1] 1 1 2 532
-[1] 2 1 2 453
-[1] 3 1 2 109
-[1] 4 1 2 100
-[1] 5 1 2 180
-[1] 6 1 2 210
-[1] 7 1 2 512
-[1] 8 1 2 1154
-[1] 9 1 2 1434
-[1] 10 1 2 1316
-[1] 1 2 2 216
-[1] 2 2 2 348
-[1] 3 2 2 585
-[1] 4 2 2 566
-[1] 5 2 2 614
-[1] 6 2 2 579
-[1] 7 2 2 610
-[1] 8 2 2 704
-[1] 9 2 2 276
-[1] 10 2 2 1093
-[1] 1 3 2 395
-[1] 2 3 2 568
-[1] 3 3 2 714
-[1] 4 3 2 556
-[1] 5 3 2 659
-[1] 6 3 2 922
-[1] 7 3 2 730
-[1] 8 3 2 334
-[1] 9 3 2 229
-[1] 10 3 2 572
-[1] 1 4 2 1163
-[1] 2 4 2 1181
-[1] 3 4 2 841
-[1] 4 4 2 684
-[1] 5 4 2 759
-[1] 6 4 2 585
-[1] 7 4 2 375
-[1] 8 4 2 375
-[1] 9 4 2 117
-[1] 10 4 2 174
-[1] 1 1 3 646
-[1] 2 1 3 149
-[1] 3 1 3 415
-[1] 4 1 3 327
-[1] 5 1 3 486
-[1] 6 1 3 726
-[1] 7 1 3 998
-[1] 8 1 3 1417
-[1] 9 1 3 746
-[1] 10 1 3 93
-[1] 1 2 3 435
-[1] 2 2 3 680
-[1] 3 2 3 521
-[1] 4 2 3 355
-[1] 5 2 3 264
-[1] 6 2 3 469
-[1] 7 2 3 526
-[1] 8 2 3 516
-[1] 9 2 3 1267
-[1] 10 2 3 906
-[1] 1 3 3 716
-[1] 2 3 3 686
-[1] 3 3 3 752
-[1] 4 3 3 575
-[1] 5 3 3 673
-[1] 6 3 3 696
-[1] 7 3 3 370
-[1] 8 3 3 405
-[1] 9 3 3 738
-[1] 10 3 3 311
-[1] 1 4 3 777
-[1] 2 4 3 514
-[1] 3 4 3 993
-[1] 4 4 3 905
-[1] 5 4 3 581
-[1] 6 4 3 471
-[1] 7 4 3 668
-[1] 8 4 3 515
-[1] 9 4 3 223
-[1] 10 4 3 197
-[1] 1 1 4 126
-[1] 2 1 4 843
-[1] 3 1 4 565
-[1] 4 1 4 905
-[1] 5 1 4 812
-[1] 6 1 4 933
-[1] 7 1 4 867
-[1] 8 1 4 660
-[1] 9 1 4 37
-[1] 10 1 4 0
-[1] 1 2 4 786
-[1] 2 2 4 474
-[1] 3 2 4 489
-[1] 4 2 4 491
-[1] 5 2 4 500
-[1] 6 2 4 486
-[1] 7 2 4 657
-[1] 8 2 4 752
-[1] 9 2 4 538
-[1] 10 2 4 454
-[1] 1 3 4 401
-[1] 2 3 4 168
-[1] 3 3 4 153
-[1] 4 3 4 157
-[1] 5 3 4 228
-[1] 6 3 4 507
-[1] 7 3 4 717
-[1] 8 3 4 1067
-[1] 9 3 4 1431
-[1] 10 3 4 1245
-[1] 1 4 4 21
-[1] 2 4 4 20
-[1] 3 4 4 44
-[1] 4 4 4 50
-[1] 5 4 4 118
-[1] 6 4 4 176
-[1] 7 4 4 499
-[1] 8 4 4 1069
-[1] 9 4 4 1922
-[1] 10 4 4 2351
-[1] 1 1 1 83
-[1] 2 1 1 266
-[1] 3 1 1 169
-[1] 4 1 1 233
-[1] 5 1 1 561
-[1] 6 1 1 524
-[1] 7 1 1 600
-[1] 8 1 1 759
-[1] 9 1 1 1232
-[1] 10 1 1 1574
-[1] 1 2 1 102
-[1] 2 2 1 451
-[1] 3 2 1 559
-[1] 4 2 1 307
-[1] 5 2 1 309
-[1] 6 2 1 337
-[1] 7 2 1 451
-[1] 8 2 1 737
-[1] 9 2 1 947
-[1] 10 2 1 1521
-[1] 1 3 1 948
-[1] 2 3 1 861
-[1] 3 3 1 595
-[1] 4 3 1 607
-[1] 5 3 1 637
-[1] 6 3 1 367
-[1] 7 3 1 529
-[1] 8 3 1 620
-[1] 9 3 1 297
-[1] 10 3 1 77
-[1] 1 4 1 538
-[1] 2 4 1 151
-[1] 3 4 1 327
-[1] 4 4 1 307
-[1] 5 4 1 318
-[1] 6 4 1 396
-[1] 7 4 1 755
-[1] 8 4 1 1027
-[1] 9 4 1 1263
-[1] 10 4 1 831
-[1] 1 1 2 317
-[1] 2 1 2 609
-[1] 3 1 2 659
-[1] 4 1 2 466
-[1] 5 1 2 346
-[1] 6 1 2 440
-[1] 7 1 2 539
-[1] 8 1 2 757
-[1] 9 1 2 1155
-[1] 10 1 2 1082
-[1] 1 2 2 450
-[1] 2 2 2 434
-[1] 3 2 2 208
-[1] 4 2 2 198
-[1] 5 2 2 573
-[1] 6 2 2 824
-[1] 7 2 2 1087
-[1] 8 2 2 1054
-[1] 9 2 2 1030
-[1] 10 2 2 638
-[1] 1 3 2 1119
-[1] 2 3 2 482
-[1] 3 3 2 505
-[1] 4 3 2 414
-[1] 5 3 2 387
-[1] 6 3 2 639
-[1] 7 3 2 870
-[1] 8 3 2 552
-[1] 9 3 2 450
-[1] 10 3 2 278
-[1] 1 4 2 483
-[1] 2 4 2 550
-[1] 3 4 2 629
-[1] 4 4 2 568
-[1] 5 4 2 805
-[1] 6 4 2 900
-[1] 7 4 2 972
-[1] 8 4 2 587
-[1] 9 4 2 392
-[1] 10 4 2 230
-[1] 1 1 3 457
-[1] 2 1 3 721
-[1] 3 1 3 368
-[1] 4 1 3 395
-[1] 5 1 3 528
-[1] 6 1 3 584
-[1] 7 1 3 749
-[1] 8 1 3 1138
-[1] 9 1 3 872
-[1] 10 1 3 249
-[1] 1 2 3 713
-[1] 2 2 3 314
-[1] 3 2 3 268
-[1] 4 2 3 768
-[1] 5 2 3 1044
-[1] 6 2 3 1028
-[1] 7 2 3 794
-[1] 8 2 3 421
-[1] 9 2 3 341
-[1] 10 2 3 196
-[1] 1 3 3 450
-[1] 2 3 3 601
-[1] 3 3 3 734
-[1] 4 3 3 1046
-[1] 5 3 3 958
-[1] 6 3 3 542
-[1] 7 3 3 524
-[1] 8 3 3 415
-[1] 9 3 3 250
-[1] 10 3 3 537
-[1] 1 4 3 576
-[1] 2 4 3 996
-[1] 3 4 3 641
-[1] 4 4 3 634
-[1] 5 4 3 500
-[1] 6 4 3 529
-[1] 7 4 3 566
-[1] 8 4 3 399
-[1] 9 4 3 287
-[1] 10 4 3 1332
-[1] 1 1 4 865
-[1] 2 1 4 444
-[1] 3 1 4 674
-[1] 4 1 4 1021
-[1] 5 1 4 741
-[1] 6 1 4 861
-[1] 7 1 4 884
-[1] 8 1 4 356
-[1] 9 1 4 276
-[1] 10 1 4 143
-[1] 1 2 4 774
-[1] 2 2 4 695
-[1] 3 2 4 831
-[1] 4 2 4 943
-[1] 5 2 4 852
-[1] 6 2 4 792
-[1] 7 2 4 546
-[1] 8 2 4 230
-[1] 9 2 4 104
-[1] 10 2 4 10
-[1] 1 3 4 542
-[1] 2 3 4 799
-[1] 3 3 4 728
-[1] 4 3 4 410
-[1] 5 3 4 559
-[1] 6 3 4 528
-[1] 7 3 4 512
-[1] 8 3 4 559
-[1] 9 3 4 639
-[1] 10 3 4 732
-[1] 1 4 4 277
-[1] 2 4 4 194
-[1] 3 4 4 1117
-[1] 4 4 4 1053
-[1] 5 4 4 871
-[1] 6 4 4 654
-[1] 7 4 4 736
-[1] 8 4 4 705
-[1] 9 4 4 359
-[1] 10 4 4 471
-Warning messages:
-1: In Env_laboratory_weekly_sub$daylength > breaks_daylength[2] & Env_laboratory_weekly$daylength <= :
- longer object length is not a multiple of shorter object length
-2: In Env_laboratory_weekly_sub$daylength > breaks_daylength[3] & Env_laboratory_weekly$daylength <= :
- longer object length is not a multiple of shorter object length
->
-> proc.time()
- user system elapsed
-466.305 17.408 483.745
diff --git a/PAPER_Conditional_probability_quantile_original_MEDMI_quarters.R b/PAPER_Conditional_probability_quantile_original_MEDMI_quarters.R
deleted file mode 100644
index 3cab5a6ea5ee2db54d574097ae0bf3df8a922348..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_MEDMI_quarters.R
+++ /dev/null
@@ -1,490 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-#The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-#library(Hmisc)
-
-#list.of.packages <- c("xts")
-#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-#if(length(new.packages)) install.packages(new.packages)
-library(xts)
-
-
-
-
-## Varaible file
-
-variable_int<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-width<-30
-width_char<-paste(width)
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable<-"daylength"
-#variable_y<-"Mean_Precipitation"
-variable_y<-"Relative_humidity"
-
-#variable_y<-"Mean_Precipitation"
-#variable<-"daylength"
-#variable<-"Mean_Precipitation"
-#"Maximum_air_temperature",
-#"Minimum_air_temperature",
-#"Mean_wind_speed",
-#"Mean_Precipitation",
-#"Relative_humidity",
-#"daylength"
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-colnames(Env_laboratory_weekly)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
-
-quarter<-4
-quarter_char<-paste("_",as.character(quarter),"-quarter",sep="")
-
-if (quarter==1){
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=1 & month(as.Date(Env_Campylobacter_data_all2$Date))<=3)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=1 & month(as.Date(Env_laboratory_weekly$Date))<=3)
-}
-
-if (quarter==2){
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=4 & month(as.Date(Env_Campylobacter_data_all2$Date))<=6)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=4 & month(as.Date(Env_laboratory_weekly$Date))<=6)
-}
-
-if (quarter==3){
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=7 & month(as.Date(Env_Campylobacter_data_all2$Date))<=9)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=7 & month(as.Date(Env_laboratory_weekly$Date))<=9)
-}
-
-if (quarter==4){
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=10 & month(as.Date(Env_Campylobacter_data_all2$Date))<=12)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=10 & month(as.Date(Env_laboratory_weekly$Date))<=12)
-}
-
-
-
-
-################### include latitude and longitude
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-
-Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-Env_laboratory_int3<-data.frame(Env_laboratory_int2)
-
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
-
-
-
-######################## include daylength ##################
-
-PC_df<-data.frame(All_PC,as.Date(dates))
-colnames(PC_df)<-c("PostCode","Date")
-
-if (quarter==1){
-PC_df<-subset(PC_df,month(as.Date(PC_df$Date))>=1 & month(as.Date(PC_df$Date))<=3)
-}
-
-if (quarter==2}{
-PC_df<-subset(PC_df,month(as.Date(PC_df$Date))>=4 & month(as.Date(PC_df$Date))<=6)
-}
-
-if (quarter==3}{
-PC_df<-subset(PC_df,month(as.Date(PC_df$Date))>=7 & month(as.Date(PC_df$Date))<=9)
-}
-
-if (quarter==4}{
-PC_df<-subset(PC_df,month(as.Date(PC_df$Date))>=10 & month(as.Date(PC_df$Date))<=12)
-}
-
-Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
-
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Post_Codes_df$lat
-day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-daylength_df$Date<-as.factor(daylength_df$Date)
-daylength_df$lat<-as.factor(daylength_df$lat)
-Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
-
-#Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-#Env_laboratory<-data.frame(Env_laboratory_int4)
-Env_laboratory<-data.frame(Env_laboratory_int3)
-Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
-
-
-#Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-#Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
-
-
-
-
-
-################### Divide the domains of the variables in bins according to quantiles
-
-
-index_C<-which (names(Env_Campylobacter_data)==variable)
-index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-index_x_C<-which (names(Env_Campylobacter_data)==variable_x)
-
-index_res_C<-which (names(Env_Campylobacter_data)=="residents")
-
-
-index<-which (names(Env_laboratory)==variable)
-index_y<-which (names(Env_laboratory)==variable_y)
-index_x<-which (names(Env_laboratory)==variable_x)
-index_res<-which (names(Env_laboratory)=="residents")
-
-
-#########################
-
-
-breaks_z_lab<-function(variable,by_z)
-{
-
- index<-which (names(Env_laboratory)==variable)
-
-
- breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-
-breaks_z<-function(variable,by_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-
-
-breaks_y_lab<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- index<-which (names(Env_laboratory)==variable)
- index_y<-which (names(Env_laboratory)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval(Env_laboratory[,index],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_some<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-breaks_y<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
- index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-breaks_x_lab<-function(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)
-{
- index_C<-which (names(Env_Campylobacter_data)==variable)
- index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
- index<-which (names(Env_laboratory)==variable)
- index_y<-which (names(Env_laboratory)==variable_y)
- index_x<-which (names(Env_laboratory)==variable_x)
-
- if(is.na(breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y])=='FALSE'){
-
- wt<-(findInterval(Env_Campylobacter_data[,index_y_C],breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- wt<-(findInterval(Env_laboratory[,index_y],breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_laboratory_some<-Env_laboratory[ww,]
-
-
- breaks_x<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_x]), probs=seq(0,1, by=by_x), na.rm=TRUE))
- breaks_x[length(breaks_x)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_x]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[length(breaks_x)]
- breaks_x[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_x]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[1]
-
- } else {
-
- breaks_x<-c()
- } } else {
-
- breaks_x<-c()
-
- }
-
- return(breaks_x)
- }
-
-
-breaks_x<-function(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)
-{
- index_C<-which (names(Env_Campylobacter_data)==variable)
- index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
- index_x_C<-which (names(Env_Campylobacter_data)==variable_x)
-
- if(is.na(breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y])=='FALSE'){
-
- wt<-(findInterval(Env_Campylobacter_data[,index_y_C],breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- wt<-(findInterval(Env_Campylobacter_data_some[,index_y_C],breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_Campylobacter_data_some2<-Env_Campylobacter_data_some[ww,]
-
-
- breaks_x<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some2[,index_x_C]), probs=seq(0,1, by=by_x), na.rm=TRUE))
- breaks_x[length(breaks_x)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some2[,index_x_C]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[length(breaks_x)]
- breaks_x[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some2[,index_x_C]), probs=seq(0,1, by=by_x), na.rm=TRUE)))[1]
-
- } else
- { breaks_x<-c()
- }
- }
- else {
-
- breaks_x<-c()
-
- }
-
- return(breaks_x)
- }
-
-
-#################
-
-
-
-
-var_x_loc_df<-data.frame(character(), character(),character(),numeric(),numeric(),numeric())
-colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
-
-residents_i_var<-0
-residents_universal<-0
-#i_var_max<-length(breaks_var)
-#i_var_min<-1
-#i_var_max_x<-length(breaks_var_x)
-#i_var_min_x<-1
-
-
-#####################
-by_z<-0.25
-by_y<-0.25
-by_x<-0.1
-
-#i_var_min<-breaks_z(variable,by_z)[1]
-#i_var_max<-breaks_z(variable,by_z)[length(breaks_z(variable,by_z))]
-j_z_min<-1
-j_z_max<-length(breaks_z(variable,by_z))-1
-
-
-
-for (j_z in c(j_z_min:j_z_max))
-{
-
- wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval((Env_laboratory[,index]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_z<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_z[,1])!=0){
- if (length(breaks_y(variable,variable_y,by_z,by_y,j_z))!=0){
-
- j_y_min<-1
- j_y_max<-length(breaks_y(variable,variable_y,by_z,by_y,j_z))-1
-
-
-
- for (j_y in c(j_y_min:j_y_max))
- {
-
- wt<-(findInterval((Env_Campylobacter_data_z[,index_y_C]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_Campylobacter_data_y<-Env_Campylobacter_data_z[ww,]
-
- wt<-(findInterval((Env_laboratory_z[,index_y]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_laboratory_y<-Env_laboratory_z[ww,]
-
-
- if (length(breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y))!=0){
-
- j_x_min<-1
- j_x_max<- length(breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y))-1
- for (j_x in c(j_x_min:j_x_max))
- {
-
-
- wt<-(findInterval((Env_Campylobacter_data_y[,index_x_C]),breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)))
- ww<-which(wt==j_x)
- Yt1<-Env_Campylobacter_data_y[ww,c(1:3,index_C,index_y_C,index_x_C,index_res_C)]
-
- wt<-(findInterval((Env_laboratory[,index_x]),breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)))
- ww<-which(wt==j_x)
- Y_tot<-Env_laboratory_y[ww,c(1:2,index,index_y,index_x,index_res)]
-
- Total_cases<-sum((as.numeric(na.omit(Yt1$Cases))))
- residents<-sum((as.numeric(na.omit(Yt1$residents))))
- residents_tot<-sum((as.numeric(na.omit(Y_tot$residents))))
-
- data_df<-data.frame(
- breaks_z(variable,by_z)[j_z],
- breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y],
- breaks_x(variable,variable_y,variable_x,by_z,by_y,by_x,j_z,j_y)[j_x],
- Total_cases,
- residents,
- residents_tot)
-
-
-
-
-
- colnames(data_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
- var_x_loc_df<-rbind(var_x_loc_df,data_df)
- print(c(j_x,j_y,j_z, Total_cases))
-
- }}}
- }
-
-
- }}
-
-
-write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",variable_x,"_",width_char,quarter_char,"_Simulated_for_rec_original_MEDMI_quantile.csv",sep=""))
diff --git a/PAPER_Conditional_probability_quantile_original_MEDMI_quarters.Rout b/PAPER_Conditional_probability_quantile_original_MEDMI_quarters.Rout
deleted file mode 100644
index 51b9eb0401f590db2da06ce35ddfb7363f13f540..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_MEDMI_quarters.Rout
+++ /dev/null
@@ -1,204 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> #The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> #library(Hmisc)
->
-> #list.of.packages <- c("xts")
-> #new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-> #if(length(new.packages)) install.packages(new.packages)
-> library(xts)
-Loading required package: zoo
-
-Attaching package: ‘zoo’
-
-The following object is masked from ‘package:timeSeries’:
-
- time<-
-
-The following objects are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-
-Attaching package: ‘xts’
-
-The following objects are masked from ‘package:pastecs’:
-
- first, last
-
->
->
->
->
-> ## Varaible file
->
-> variable_int<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> width<-30
-> width_char<-paste(width)
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable<-"daylength"
-> #variable_y<-"Mean_Precipitation"
-> variable_y<-"Relative_humidity"
->
-> #variable_y<-"Mean_Precipitation"
-> #variable<-"daylength"
-> #variable<-"Mean_Precipitation"
-> #"Maximum_air_temperature",
-> #"Minimum_air_temperature",
-> #"Mean_wind_speed",
-> #"Mean_Precipitation",
-> #"Relative_humidity",
-> #"daylength"
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
->
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-> colnames(Env_laboratory_weekly)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
->
-> Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-> Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
->
-> quarter<-4
-> quarter_char<-paste("_",as.character(quarter),"-quarter",sep="")
->
-> if (quarter==1){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=1 & month(as.Date(Env_Campylobacter_data_all2$Date))<=3)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=1 & month(as.Date(Env_laboratory_weekly$Date))<=3)
-+ }
->
-> if (quarter==2){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=4 & month(as.Date(Env_Campylobacter_data_all2$Date))<=6)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=4 & month(as.Date(Env_laboratory_weekly$Date))<=6)
-+ }
->
-> if (quarter==3){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=7 & month(as.Date(Env_Campylobacter_data_all2$Date))<=9)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=7 & month(as.Date(Env_laboratory_weekly$Date))<=9)
-+ }
->
-> if (quarter==4){
-+ Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,month(as.Date(Env_Campylobacter_data_all2$Date))>=10 & month(as.Date(Env_Campylobacter_data_all2$Date))<=12)
-+ Env_laboratory_int1<-subset(Env_laboratory_weekly,month(as.Date(Env_laboratory_weekly$Date))>=10 & month(as.Date(Env_laboratory_weekly$Date))<=12)
-+ }
->
->
->
->
-> ################### include latitude and longitude
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
->
->
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
->
-> Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-> Env_laboratory_int3<-data.frame(Env_laboratory_int2)
->
-> Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-> Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
->
->
->
-> ######################## include daylength ##################
->
-> PC_df<-data.frame(All_PC,as.Date(dates))
-> colnames(PC_df)<-c("PostCode","Date")
->
-> if (quarter==1){
-+ PC_df<-subset(PC_df,month(as.Date(PC_df$Date))>=1 & month(as.Date(PC_df$Date))<=3)
-+ }
->
-> if (quarter==2}{
-Error: unexpected '}' in "if (quarter==2}"
-Execution halted
diff --git a/PAPER_Conditional_probability_quantile_original_one_variable_quantile.R b/PAPER_Conditional_probability_quantile_original_one_variable_quantile.R
deleted file mode 100644
index cf23dae1d24848ee25d6f01fc8bb2aafedb8eca5..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_one_variable_quantile.R
+++ /dev/null
@@ -1,251 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-#The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-#library(Hmisc)
-
-#list.of.packages <- c("xts")
-#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-#if(length(new.packages)) install.packages(new.packages)
-library(xts)
-
-
-
-
-## Variable file
-
-variable_int<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-width<-7
-width_char<-paste(width)
-
-
-
-variable<-"Mean_wind_speed"
-
-#variable<-"Maximum_air_temperature",
-#variable_y<-"Mean_Precipitation"
-#variable<-"daylength"
-#variable<-"Minimum_air_temperature"
-#variable<-"Relative_humidity",
-
-#variable<-"Mean_wind_speed",
-
-#variable_y<-"Cumul_Precipitation"
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-colnames(Env_laboratory_weekly)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
-
-
-
-################### include latitude and longitude
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-
-Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-Env_laboratory_int3<-data.frame(Env_laboratory_int2)
-
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
-
-
-
-######################## include daylength ##################
-
-PC_df<-data.frame(All_PC,as.Date(dates))
-colnames(PC_df)<-c("PostCode","Date")
-
-Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
-
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Post_Codes_df$lat
-day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-daylength_df$Date<-as.factor(daylength_df$Date)
-daylength_df$lat<-as.factor(daylength_df$lat)
-Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
-
-#Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-#Env_laboratory<-data.frame(Env_laboratory_int4)
-Env_laboratory<-data.frame(Env_laboratory_int3)
-Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
-
-
-#Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-#Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
-
-
-
-
-
-################### Divide the domains of the variables in bins according to quantiles
-
-
-index_C<-which (names(Env_Campylobacter_data)==variable)
-
-
-index<-which (names(Env_laboratory)==variable)
-
-
-#########################
-
-
-breaks_z_lab<-function(variable,by_z)
-{
-
- index<-which (names(Env_laboratory)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-breaks_z<-function(variable,by_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-
-#################
-
-
-
-
-var_x_loc_df<-data.frame(character(),numeric(),numeric(),numeric())
-colnames(var_x_loc_df)<-c(variable,"counts","residents","residents_tot")
-
-residents_i_var<-0
-residents_universal<-0
-#i_var_max<-length(breaks_var)
-#i_var_min<-1
-#i_var_max_x<-length(breaks_var_x)
-#i_var_min_x<-1
-
-
-#####################
-by_z<-0.05
-
-j_z_min<-1
-j_z_max<-length(breaks_z(variable,by_z))-1
-
-
-
-for (j_z in c(j_z_min:j_z_max))
-{
-
- wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval((Env_laboratory[,index]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_z<-Env_laboratory[ww,]
-
-
-
- Total_cases<-sum((as.numeric(na.omit(Env_Campylobacter_data_z$Cases))))
- residents<-sum((as.numeric(na.omit(Env_Campylobacter_data_z$residents))))
- residents_tot<-sum((as.numeric(na.omit(Env_laboratory_z$residents))))
-
- data_df<-data.frame(
- breaks_z(variable,by_z)[j_z],
- Total_cases,
- residents,
- residents_tot)
-
-
-
-
-
- colnames(data_df)<-c(variable,"counts","residents","residents_tot")
- var_x_loc_df<-rbind(var_x_loc_df,data_df)
- print(c(j_z, Total_cases))
- }
-
-
-write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
diff --git a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI.R b/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI.R
deleted file mode 100644
index 057e32acef8e219d9a846ecd416739b900526435..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI.R
+++ /dev/null
@@ -1,364 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-#The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-#library(Hmisc)
-
-#list.of.packages <- c("xts")
-#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-#if(length(new.packages)) install.packages(new.packages)
-library(xts)
-
-
-
-
-## Varaible file
-
-variable_int<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-width<-30
-width_char<-paste(width)
-
-
-variable_y<-"Difference_air_temperature"
-#variable_y<-"Maximum_air_temperature"
-variable<-"daylength"
-#variable<-"Relative_humidity"
-#variable<-"Mean_Precipitation"
-#variable<-"Mean_wind_speed"
-
-#variable_y<-"Relative_humidity"
-#variable_y<-"Mean_Precipitation"
-#variable_y<-"Cumul_Precipitation"
-#variable_y<-"Mean_wind_speed"
-#variable_y<-"daylength"
-#variable_y<-"Minimum_air_temperature"
-#variable<-"Difference_air_temperature"
-
-#variable_y<-"Mean_Precipitation"
-#variable<-"daylength"
-#variable<-"Mean_Precipitation"
-#"Maximum_air_temperature",
-#"Minimum_air_temperature",
-#"Mean_wind_speed",
-#"Mean_Precipitation",
-#"Relative_humidity",
-#"daylength"
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-colnames(Env_laboratory_weekly)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
-
-
-################### include latitude and longitude
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-
-Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-Env_laboratory_int3<-data.frame(Env_laboratory_int2)
-
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
-
-
-
-######################## include daylength ##################
-
-PC_df<-data.frame(All_PC,as.Date(dates))
-colnames(PC_df)<-c("PostCode","Date")
-
-Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
-
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Post_Codes_df$lat
-day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-daylength_df$Date<-as.factor(daylength_df$Date)
-daylength_df$lat<-as.factor(daylength_df$lat)
-Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
-
-#Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-#Env_laboratory<-data.frame(Env_laboratory_int4)
-Env_laboratory<-data.frame(Env_laboratory_int3)
-Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
-
-
-#Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-#Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
-
-
-
-Env_Campylobacter_data$Difference_air_temperature<-Env_Campylobacter_data$Maximum_air_temperature-Env_Campylobacter_data$Minimum_air_temperature
-Env_laboratory$Difference_air_temperature<-Env_laboratory$Maximum_air_temperature-Env_laboratory$Minimum_air_temperature
-
-
-
-################### Divide the domains of the variables in bins according to quantiles
-
-
-index_C<-which (names(Env_Campylobacter_data)==variable)
-index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
-index<-which (names(Env_laboratory)==variable)
-index_y<-which (names(Env_laboratory)==variable_y)
-
-
-#########################
-
-
-breaks_z_lab<-function(variable,by_z)
-{
-
- index<-which (names(Env_laboratory)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-breaks_z<-function(variable,by_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-breaks_y_lab<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- index<-which (names(Env_laboratory)==variable)
- index_y<-which (names(Env_laboratory)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval(Env_laboratory[,index],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_some<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-
-breaks_y<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
- index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-#################
-
-
-
-
-var_x_loc_df<-data.frame(character(), character(),numeric(),numeric(),numeric())
-colnames(var_x_loc_df)<-c(variable,variable_y,"counts","residents","residents_tot")
-
-residents_i_var<-0
-residents_universal<-0
-#i_var_max<-length(breaks_var)
-#i_var_min<-1
-#i_var_max_x<-length(breaks_var_x)
-#i_var_min_x<-1
-
-
-#####################
-by_z<-0.2
-by_y<-0.05
-
-
-#i_var_min<-breaks_z(variable,by_z)[1]
-#i_var_max<-breaks_z(variable,by_z)[length(breaks_z(variable,by_z))]
-j_z_min<-1
-j_z_max<-length(breaks_z(variable,by_z))-1
-
-
-
-for (j_z in c(j_z_min:j_z_max))
-{
-
- wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval((Env_laboratory[,index]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_z<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_z[,1])!=0){
- if (length(breaks_y(variable,variable_y,by_z,by_y,j_z))!=0){
-
- j_y_min<-1
- j_y_max<-length(breaks_y(variable,variable_y,by_z,by_y,j_z))-1
-
-
-
- for (j_y in c(j_y_min:j_y_max))
- {
-
- wt<-(findInterval((Env_Campylobacter_data_z[,index_y_C]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_Campylobacter_data_y<-Env_Campylobacter_data_z[ww,]
-
- wt<-(findInterval((Env_laboratory_z[,index_y]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_laboratory_y<-Env_laboratory_z[ww,]
-
-
-
-
- Total_cases<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$Cases))))
- residents<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$residents))))
- residents_tot<-sum((as.numeric(na.omit(Env_laboratory_y$residents))))
-
- data_df<-data.frame(
- breaks_z(variable,by_z)[j_z],
- breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y],
- Total_cases,
- residents,
- residents_tot)
-
-
-
-
-
- colnames(data_df)<-c(variable,variable_y,"counts","residents","residents_tot")
- var_x_loc_df<-rbind(var_x_loc_df,data_df)
- print(c(j_y,j_z, Total_cases))
-
- }
- }
-
-
- }}
-
-
-write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
diff --git a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI.Rout b/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI.Rout
deleted file mode 100644
index c55b63a8b9f21fdb455144426ff7d32e52258c9d..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI.Rout
+++ /dev/null
@@ -1,519 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> #The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> #library(Hmisc)
->
-> #list.of.packages <- c("xts")
-> #new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-> #if(length(new.packages)) install.packages(new.packages)
-> library(xts)
-Loading required package: zoo
-
-Attaching package: ‘zoo’
-
-The following object is masked from ‘package:timeSeries’:
-
- time<-
-
-The following objects are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-
-Attaching package: ‘xts’
-
-The following objects are masked from ‘package:pastecs’:
-
- first, last
-
->
->
->
->
-> ## Varaible file
->
-> variable_int<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> width<-14
-> width_char<-paste(width)
->
->
->
-> #variable_x<-"Maximum_air_temperature"
-> #variable<-"daylength"
-> #variable_y<-"Relative_humidity"
->
-> #variable<-"Mean_Precipitation"
-> variable_y<-"Relative_humidity"
-> variable<-"Maximum_air_temperature"
->
-> #variable_y<-"Mean_Precipitation"
-> #variable<-"daylength"
-> #variable<-"Mean_Precipitation"
-> #"Maximum_air_temperature",
-> #"Minimum_air_temperature",
-> #"Mean_wind_speed",
-> #"Mean_Precipitation",
-> #"Relative_humidity",
-> #"daylength"
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
->
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
-> Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-> colnames(Env_laboratory_weekly)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
->
-> Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-> Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
->
->
-> ################### include latitude and longitude
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
->
->
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
->
-> Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-> Env_laboratory_int3<-data.frame(Env_laboratory_int2)
->
-> Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-> Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
->
->
->
-> ######################## include daylength ##################
->
-> PC_df<-data.frame(All_PC,as.Date(dates))
-> colnames(PC_df)<-c("PostCode","Date")
->
-> Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
->
->
-> daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
->
-> latitude<-Post_Codes_df$lat
-> day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-> daylength_df$Date<-as.factor(daylength_df$Date)
-> daylength_df$lat<-as.factor(daylength_df$lat)
-> Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-> Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
->
-> #Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-> #Env_laboratory<-data.frame(Env_laboratory_int4)
-> Env_laboratory<-data.frame(Env_laboratory_int3)
-> Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-> Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
->
->
-> #Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-> #Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-> Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
->
->
->
->
->
->
-> ################### Divide the domains of the variables in bins according to quantiles
->
->
-> index_C<-which (names(Env_Campylobacter_data)==variable)
-> index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
->
->
-> index<-which (names(Env_laboratory)==variable)
-> index_y<-which (names(Env_laboratory)==variable_y)
->
->
-> #########################
->
->
-> breaks_z_lab<-function(variable,by_z)
-+ {
-+
-+ index<-which (names(Env_laboratory)==variable)
-+
-+ breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-+
-+ breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
-+ breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-+
-+
-+ return(breaks_z)
-+
-+ }
->
->
-> breaks_z<-function(variable,by_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+
-+ breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-+
-+ breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
-+ breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-+
-+
-+ return(breaks_z)
-+
-+ }
->
->
-> breaks_y_lab<-function(variable,variable_y,by_z,by_y,j_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+
-+ index<-which (names(Env_laboratory)==variable)
-+ index_y<-which (names(Env_laboratory)==variable_y)
-+
-+
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+ wt<-(findInterval(Env_laboratory[,index],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_laboratory_some<-Env_laboratory[ww,]
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ breaks_y<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE))
-+ breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
-+ breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-+
-+ }else{
-+
-+ breaks_y<-c()
-+ }
-+
-+ return(breaks_y)
-+ }
->
->
->
->
-> breaks_y<-function(variable,variable_y,by_z,by_y,j_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+ index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-+
-+
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ breaks_y<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE))
-+ breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
-+ breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-+
-+ }else{
-+
-+ breaks_y<-c()
-+ }
-+
-+ return(breaks_y)
-+ }
->
->
->
-> #################
->
->
->
->
-> var_x_loc_df<-data.frame(character(), character(),numeric(),numeric(),numeric())
-> colnames(var_x_loc_df)<-c(variable,variable_y,"counts","residents","residents_tot")
->
-> residents_i_var<-0
-> residents_universal<-0
-> #i_var_max<-length(breaks_var)
-> #i_var_min<-1
-> #i_var_max_x<-length(breaks_var_x)
-> #i_var_min_x<-1
->
->
-> #####################
-> by_z<-0.2
-> by_y<-0.05
->
->
-> #i_var_min<-breaks_z(variable,by_z)[1]
-> #i_var_max<-breaks_z(variable,by_z)[length(breaks_z(variable,by_z))]
-> j_z_min<-1
-> j_z_max<-length(breaks_z(variable,by_z))-1
->
->
->
-> for (j_z in c(j_z_min:j_z_max))
-+ {
-+
-+ wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-+
-+ wt<-(findInterval((Env_laboratory[,index]),breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_laboratory_z<-Env_laboratory[ww,]
-+
-+ if (length(Env_Campylobacter_data_z[,1])!=0){
-+ if (length(breaks_y(variable,variable_y,by_z,by_y,j_z))!=0){
-+
-+ j_y_min<-1
-+ j_y_max<-length(breaks_y(variable,variable_y,by_z,by_y,j_z))-1
-+
-+
-+
-+ for (j_y in c(j_y_min:j_y_max))
-+ {
-+
-+ wt<-(findInterval((Env_Campylobacter_data_z[,index_y_C]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_Campylobacter_data_y<-Env_Campylobacter_data_z[ww,]
-+
-+ wt<-(findInterval((Env_laboratory_z[,index_y]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_laboratory_y<-Env_laboratory_z[ww,]
-+
-+
-+
-+
-+ Total_cases<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$Cases))))
-+ residents<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$residents))))
-+ residents_tot<-sum((as.numeric(na.omit(Env_laboratory_y$residents))))
-+
-+ data_df<-data.frame(
-+ breaks_z(variable,by_z)[j_z],
-+ breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y],
-+ Total_cases,
-+ residents,
-+ residents_tot)
-+
-+
-+
-+
-+
-+ colnames(data_df)<-c(variable,variable_y,"counts","residents","residents_tot")
-+ var_x_loc_df<-rbind(var_x_loc_df,data_df)
-+ print(c(j_y,j_z, Total_cases))
-+
-+ }
-+ }
-+
-+
-+ }}
-[1] 1 1 16381
-[1] 2 1 16399
-[1] 3 1 16202
-[1] 4 1 16108
-[1] 5 1 15977
-[1] 6 1 16167
-[1] 7 1 16365
-[1] 8 1 15732
-[1] 9 1 15786
-[1] 10 1 16207
-[1] 11 1 16208
-[1] 12 1 16134
-[1] 13 1 16176
-[1] 14 1 16066
-[1] 15 1 15923
-[1] 16 1 16236
-[1] 17 1 16493
-[1] 18 1 16615
-[1] 19 1 16278
-[1] 20 1 16586
-[1] 1 2 18580
-[1] 2 2 17952
-[1] 3 2 17855
-[1] 4 2 18326
-[1] 5 2 18528
-[1] 6 2 18022
-[1] 7 2 17874
-[1] 8 2 17693
-[1] 9 2 17809
-[1] 10 2 17369
-[1] 11 2 17669
-[1] 12 2 17439
-[1] 13 2 17376
-[1] 14 2 17360
-[1] 15 2 17411
-[1] 16 2 17294
-[1] 17 2 17636
-[1] 18 2 17410
-[1] 19 2 16777
-[1] 20 2 17470
-[1] 1 3 21174
-[1] 2 3 20936
-[1] 3 3 20726
-[1] 4 3 20464
-[1] 5 3 20644
-[1] 6 3 21448
-[1] 7 3 21934
-[1] 8 3 21392
-[1] 9 3 21352
-[1] 10 3 21420
-[1] 11 3 21164
-[1] 12 3 21344
-[1] 13 3 20579
-[1] 14 3 19855
-[1] 15 3 19713
-[1] 16 3 20119
-[1] 17 3 19859
-[1] 18 3 19670
-[1] 19 3 18852
-[1] 20 3 19512
-[1] 1 4 21076
-[1] 2 4 21371
-[1] 3 4 21879
-[1] 4 4 21538
-[1] 5 4 20647
-[1] 6 4 21140
-[1] 7 4 21444
-[1] 8 4 20787
-[1] 9 4 20884
-[1] 10 4 20758
-[1] 11 4 21532
-[1] 12 4 21201
-[1] 13 4 21238
-[1] 14 4 21551
-[1] 15 4 21133
-[1] 16 4 21052
-[1] 17 4 21660
-[1] 18 4 21317
-[1] 19 4 21938
-[1] 20 4 20876
-[1] 1 5 19650
-[1] 2 5 20738
-[1] 3 5 20128
-[1] 4 5 20236
-[1] 5 5 20638
-[1] 6 5 20986
-[1] 7 5 20437
-[1] 8 5 20657
-[1] 9 5 20647
-[1] 10 5 20995
-[1] 11 5 20447
-[1] 12 5 21241
-[1] 13 5 20869
-[1] 14 5 20794
-[1] 15 5 20673
-[1] 16 5 20808
-[1] 17 5 20312
-[1] 18 5 20619
-[1] 19 5 21206
-[1] 20 5 21335
->
->
-> write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
->
-> proc.time()
- user system elapsed
-261.239 10.356 271.610
diff --git a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_high_resolution.R b/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_high_resolution.R
deleted file mode 100644
index 56e12fca96b98f488f58afd95ca3b52631aff364..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_high_resolution.R
+++ /dev/null
@@ -1,364 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-#The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-#library(Hmisc)
-
-#list.of.packages <- c("xts")
-#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-#if(length(new.packages)) install.packages(new.packages)
-library(xts)
-
-
-
-
-## Varaible file
-
-variable_int<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-width<-30
-width_char<-paste(width)
-
-
-#variable_y<-"Difference_air_temperature"
-variable_y<-"Maximum_air_temperature"
-#variable<-"daylength"
-variable<-"Relative_humidity"
-#variable<-"Mean_Precipitation"
-#variable<-"Mean_wind_speed"
-
-#variable_y<-"Relative_humidity"
-#variable_y<-"Mean_Precipitation"
-#variable_y<-"Cumul_Precipitation"
-#variable_y<-"Mean_wind_speed"
-#variable_y<-"daylength"
-#variable_y<-"Minimum_air_temperature"
-#variable<-"Difference_air_temperature"
-
-#variable_y<-"Mean_Precipitation"
-#variable<-"daylength"
-#variable<-"Mean_Precipitation"
-#"Maximum_air_temperature",
-#"Minimum_air_temperature",
-#"Mean_wind_speed",
-#"Mean_Precipitation",
-#"Relative_humidity",
-#"daylength"
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-colnames(Env_laboratory_weekly)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
-
-
-################### include latitude and longitude
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-
-Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-Env_laboratory_int3<-data.frame(Env_laboratory_int2)
-
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
-
-
-
-######################## include daylength ##################
-
-PC_df<-data.frame(All_PC,as.Date(dates))
-colnames(PC_df)<-c("PostCode","Date")
-
-Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
-
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Post_Codes_df$lat
-day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-daylength_df$Date<-as.factor(daylength_df$Date)
-daylength_df$lat<-as.factor(daylength_df$lat)
-Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
-
-#Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-#Env_laboratory<-data.frame(Env_laboratory_int4)
-Env_laboratory<-data.frame(Env_laboratory_int3)
-Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
-
-
-#Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-#Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
-
-
-
-Env_Campylobacter_data$Difference_air_temperature<-Env_Campylobacter_data$Maximum_air_temperature-Env_Campylobacter_data$Minimum_air_temperature
-Env_laboratory$Difference_air_temperature<-Env_laboratory$Maximum_air_temperature-Env_laboratory$Minimum_air_temperature
-
-
-
-################### Divide the domains of the variables in bins according to quantiles
-
-
-index_C<-which (names(Env_Campylobacter_data)==variable)
-index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
-index<-which (names(Env_laboratory)==variable)
-index_y<-which (names(Env_laboratory)==variable_y)
-
-
-#########################
-
-
-breaks_z_lab<-function(variable,by_z)
-{
-
- index<-which (names(Env_laboratory)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-breaks_z<-function(variable,by_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-breaks_y_lab<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- index<-which (names(Env_laboratory)==variable)
- index_y<-which (names(Env_laboratory)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval(Env_laboratory[,index],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_some<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-
-breaks_y<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
- index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-#################
-
-
-
-
-var_x_loc_df<-data.frame(character(), character(),numeric(),numeric(),numeric())
-colnames(var_x_loc_df)<-c(variable,variable_y,"counts","residents","residents_tot")
-
-residents_i_var<-0
-residents_universal<-0
-#i_var_max<-length(breaks_var)
-#i_var_min<-1
-#i_var_max_x<-length(breaks_var_x)
-#i_var_min_x<-1
-
-
-#####################
-by_z<-0.005
-by_y<-0.005
-
-
-#i_var_min<-breaks_z(variable,by_z)[1]
-#i_var_max<-breaks_z(variable,by_z)[length(breaks_z(variable,by_z))]
-j_z_min<-1
-j_z_max<-length(breaks_z(variable,by_z))-1
-
-
-
-for (j_z in c(j_z_min:j_z_max))
-{
-
- wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval((Env_laboratory[,index]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_z<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_z[,1])!=0){
- if (length(breaks_y(variable,variable_y,by_z,by_y,j_z))!=0){
-
- j_y_min<-1
- j_y_max<-length(breaks_y(variable,variable_y,by_z,by_y,j_z))-1
-
-
-
- for (j_y in c(j_y_min:j_y_max))
- {
-
- wt<-(findInterval((Env_Campylobacter_data_z[,index_y_C]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_Campylobacter_data_y<-Env_Campylobacter_data_z[ww,]
-
- wt<-(findInterval((Env_laboratory_z[,index_y]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_laboratory_y<-Env_laboratory_z[ww,]
-
-
-
-
- Total_cases<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$Cases))))
- residents<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$residents))))
- residents_tot<-sum((as.numeric(na.omit(Env_laboratory_y$residents))))
-
- data_df<-data.frame(
- breaks_z(variable,by_z)[j_z],
- breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y],
- Total_cases,
- residents,
- residents_tot)
-
-
-
-
-
- colnames(data_df)<-c(variable,variable_y,"counts","residents","residents_tot")
- var_x_loc_df<-rbind(var_x_loc_df,data_df)
- print(c(j_y,j_z, Total_cases))
-
- }
- }
-
-
- }}
-
-
-write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",width_char,"_Simulated_for_rec_original_MEDMI_high_resolution.csv",sep=""))
diff --git a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_high_resolution.Rout b/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_high_resolution.Rout
deleted file mode 100644
index 57e67f1b0467b21f08b440d402054a0cb1016e86..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_high_resolution.Rout
+++ /dev/null
@@ -1,40427 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> #The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> #library(Hmisc)
->
-> #list.of.packages <- c("xts")
-> #new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-> #if(length(new.packages)) install.packages(new.packages)
-> library(xts)
-Loading required package: zoo
-
-Attaching package: ‘zoo’
-
-The following object is masked from ‘package:timeSeries’:
-
- time<-
-
-The following objects are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-
-Attaching package: ‘xts’
-
-The following objects are masked from ‘package:pastecs’:
-
- first, last
-
->
->
->
->
-> ## Varaible file
->
-> variable_int<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> width<-30
-> width_char<-paste(width)
->
->
-> #variable_y<-"Difference_air_temperature"
-> variable_y<-"Maximum_air_temperature"
-> #variable<-"daylength"
-> variable<-"Relative_humidity"
-> #variable<-"Mean_Precipitation"
-> #variable<-"Mean_wind_speed"
->
-> #variable_y<-"Relative_humidity"
-> #variable_y<-"Mean_Precipitation"
-> #variable_y<-"Cumul_Precipitation"
-> #variable_y<-"Mean_wind_speed"
-> #variable_y<-"daylength"
-> #variable_y<-"Minimum_air_temperature"
-> #variable<-"Difference_air_temperature"
->
-> #variable_y<-"Mean_Precipitation"
-> #variable<-"daylength"
-> #variable<-"Mean_Precipitation"
-> #"Maximum_air_temperature",
-> #"Minimum_air_temperature",
-> #"Mean_wind_speed",
-> #"Mean_Precipitation",
-> #"Relative_humidity",
-> #"daylength"
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
->
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
-> Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-> colnames(Env_laboratory_weekly)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
->
-> Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-> Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
->
->
-> ################### include latitude and longitude
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
->
->
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
->
-> Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-> Env_laboratory_int3<-data.frame(Env_laboratory_int2)
->
-> Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-> Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
->
->
->
-> ######################## include daylength ##################
->
-> PC_df<-data.frame(All_PC,as.Date(dates))
-> colnames(PC_df)<-c("PostCode","Date")
->
-> Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
->
->
-> daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
->
-> latitude<-Post_Codes_df$lat
-> day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-> daylength_df$Date<-as.factor(daylength_df$Date)
-> daylength_df$lat<-as.factor(daylength_df$lat)
-> Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-> Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
->
-> #Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-> #Env_laboratory<-data.frame(Env_laboratory_int4)
-> Env_laboratory<-data.frame(Env_laboratory_int3)
-> Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-> Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
->
->
-> #Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-> #Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-> Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
->
->
->
-> Env_Campylobacter_data$Difference_air_temperature<-Env_Campylobacter_data$Maximum_air_temperature-Env_Campylobacter_data$Minimum_air_temperature
-> Env_laboratory$Difference_air_temperature<-Env_laboratory$Maximum_air_temperature-Env_laboratory$Minimum_air_temperature
->
->
->
-> ################### Divide the domains of the variables in bins according to quantiles
->
->
-> index_C<-which (names(Env_Campylobacter_data)==variable)
-> index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
->
->
-> index<-which (names(Env_laboratory)==variable)
-> index_y<-which (names(Env_laboratory)==variable_y)
->
->
-> #########################
->
->
-> breaks_z_lab<-function(variable,by_z)
-+ {
-+
-+ index<-which (names(Env_laboratory)==variable)
-+
-+ breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-+
-+ breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
-+ breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-+
-+
-+ return(breaks_z)
-+
-+ }
->
->
-> breaks_z<-function(variable,by_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+
-+ breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-+
-+ breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
-+ breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-+
-+
-+ return(breaks_z)
-+
-+ }
->
->
-> breaks_y_lab<-function(variable,variable_y,by_z,by_y,j_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+
-+ index<-which (names(Env_laboratory)==variable)
-+ index_y<-which (names(Env_laboratory)==variable_y)
-+
-+
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+ wt<-(findInterval(Env_laboratory[,index],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_laboratory_some<-Env_laboratory[ww,]
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ breaks_y<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE))
-+ breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
-+ breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-+
-+ }else{
-+
-+ breaks_y<-c()
-+ }
-+
-+ return(breaks_y)
-+ }
->
->
->
->
-> breaks_y<-function(variable,variable_y,by_z,by_y,j_z)
-+ {
-+
-+ index_C<-which (names(Env_Campylobacter_data)==variable)
-+ index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-+
-+
-+
-+ wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-+
-+
-+ if (length(Env_Campylobacter_data_some[,1])!=0) {
-+
-+ breaks_y<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE))
-+ breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
-+ breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-+
-+ }else{
-+
-+ breaks_y<-c()
-+ }
-+
-+ return(breaks_y)
-+ }
->
->
->
-> #################
->
->
->
->
-> var_x_loc_df<-data.frame(character(), character(),numeric(),numeric(),numeric())
-> colnames(var_x_loc_df)<-c(variable,variable_y,"counts","residents","residents_tot")
->
-> residents_i_var<-0
-> residents_universal<-0
-> #i_var_max<-length(breaks_var)
-> #i_var_min<-1
-> #i_var_max_x<-length(breaks_var_x)
-> #i_var_min_x<-1
->
->
-> #####################
-> by_z<-0.005
-> by_y<-0.005
->
->
-> #i_var_min<-breaks_z(variable,by_z)[1]
-> #i_var_max<-breaks_z(variable,by_z)[length(breaks_z(variable,by_z))]
-> j_z_min<-1
-> j_z_max<-length(breaks_z(variable,by_z))-1
->
->
->
-> for (j_z in c(j_z_min:j_z_max))
-+ {
-+
-+ wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-+
-+ wt<-(findInterval((Env_laboratory[,index]),breaks_z(variable,by_z)))
-+ ww<-which(wt==j_z)
-+ Env_laboratory_z<-Env_laboratory[ww,]
-+
-+ if (length(Env_Campylobacter_data_z[,1])!=0){
-+ if (length(breaks_y(variable,variable_y,by_z,by_y,j_z))!=0){
-+
-+ j_y_min<-1
-+ j_y_max<-length(breaks_y(variable,variable_y,by_z,by_y,j_z))-1
-+
-+
-+
-+ for (j_y in c(j_y_min:j_y_max))
-+ {
-+
-+ wt<-(findInterval((Env_Campylobacter_data_z[,index_y_C]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_Campylobacter_data_y<-Env_Campylobacter_data_z[ww,]
-+
-+ wt<-(findInterval((Env_laboratory_z[,index_y]),breaks_y(variable,variable_y,by_z,by_y,j_z)))
-+ ww<-which(wt==j_y)
-+ Env_laboratory_y<-Env_laboratory_z[ww,]
-+
-+
-+
-+
-+ Total_cases<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$Cases))))
-+ residents<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$residents))))
-+ residents_tot<-sum((as.numeric(na.omit(Env_laboratory_y$residents))))
-+
-+ data_df<-data.frame(
-+ breaks_z(variable,by_z)[j_z],
-+ breaks_y(variable,variable_y,by_z,by_y,j_z)[j_y],
-+ Total_cases,
-+ residents,
-+ residents_tot)
-+
-+
-+
-+
-+
-+ colnames(data_df)<-c(variable,variable_y,"counts","residents","residents_tot")
-+ var_x_loc_df<-rbind(var_x_loc_df,data_df)
-+ print(c(j_y,j_z, Total_cases))
-+
-+ }
-+ }
-+
-+
-+ }}
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->
->
-> write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",width_char,"_Simulated_for_rec_original_MEDMI_high_resolution.csv",sep=""))
->
-> proc.time()
- user system elapsed
-64449.803 11.724 64470.304
diff --git a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_lab.R b/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_lab.R
deleted file mode 100644
index 8eff1fef7eedcbb5c9ab0e28ae1c6654602a317f..0000000000000000000000000000000000000000
--- a/PAPER_Conditional_probability_quantile_original_two_variables_MEDMI_lab.R
+++ /dev/null
@@ -1,358 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-#The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
-
-# Qnatile calculated over the laboratory cathcments
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-#library(Hmisc)
-
-#list.of.packages <- c("xts")
-#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-#if(length(new.packages)) install.packages(new.packages)
-library(xts)
-
-
-
-
-## Varaible file
-
-variable_int<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable_int,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-width<-30
-width_char<-paste(width)
-
-
-
-#variable_x<-"Maximum_air_temperature"
-#variable<-"daylength"
-#variable_y<-"Relative_humidity"
-
-#variable<-"Mean_Precipitation"
-variable<-"Relative_humidity"
-variable_y<-"Maximum_air_temperature"
-
-#variable_y<-"Mean_Precipitation"
-#variable<-"daylength"
-#variable<-"Mean_Precipitation"
-#"Maximum_air_temperature",
-#"Minimum_air_temperature",
-#"Mean_wind_speed",
-#"Mean_Precipitation",
-#"Relative_humidity",
-#"daylength"
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-colnames(Env_laboratory_weekly)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(as.Date(Env_Campylobacter_data_all2$Date))>=1990 & year(as.Date(Env_Campylobacter_data_all2$Date))<=2015)
-Env_laboratory_int1<-subset(Env_laboratory_weekly,year(as.Date(Env_laboratory_weekly$Date))>=1990 & year(as.Date(Env_laboratory_weekly$Date))<=2015)
-
-
-################### include latitude and longitude
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))#
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-
-Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-Env_laboratory_int3<-data.frame(Env_laboratory_int2)
-
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int3<-data.frame(Env_Campylobacter_data_int2)
-
-
-
-######################## include daylength ##################
-
-PC_df<-data.frame(All_PC,as.Date(dates))
-colnames(PC_df)<-c("PostCode","Date")
-
-Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
-
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Post_Codes_df$lat
-day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-daylength_df$Date<-as.factor(daylength_df$Date)
-daylength_df$lat<-as.factor(daylength_df$lat)
-Env_laboratory_int3$Date<-as.factor(Env_laboratory_int3$Date)
-Env_laboratory_int3$lat<-as.factor(Env_laboratory_int3$lat)
-
-#Env_laboratory_int4<-merge(Env_laboratory_int3,daylength_df,by=c("lat","Date"))
-#Env_laboratory<-data.frame(Env_laboratory_int4)
-Env_laboratory<-data.frame(Env_laboratory_int3)
-Env_Campylobacter_data_int3$Date<-as.factor(Env_Campylobacter_data_int3$Date)
-Env_Campylobacter_data_int3$lat <-as.factor(Env_Campylobacter_data_int3$lat)
-
-
-#Env_Campylobacter_data_int4<-merge(Env_Campylobacter_data_int3,daylength_df,by=c("lat","Date"))
-#Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int4)
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int3)
-
-
-
-
-
-
-################### Divide the domains of the variables in bins according to quantiles
-
-
-index_C<-which (names(Env_Campylobacter_data)==variable)
-index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
-index<-which (names(Env_laboratory)==variable)
-index_y<-which (names(Env_laboratory)==variable_y)
-
-
-#########################
-
-
-breaks_z_lab<-function(variable,by_z)
-{
-
- index<-which (names(Env_laboratory)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory[,index]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-breaks_z<-function(variable,by_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- breaks_z<-as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE))
-
- breaks_z[length(breaks_z)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[length(breaks_z)]
- breaks_z[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data[,index_C]), probs=seq(0,1, by=by_z), na.rm=TRUE)))[1]
-
-
- return(breaks_z)
-
-}
-
-
-breaks_y_lab<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
-
- index<-which (names(Env_laboratory)==variable)
- index_y<-which (names(Env_laboratory)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval(Env_laboratory[,index],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_some<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_laboratory_some[,index_y]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-
-breaks_y<-function(variable,variable_y,by_z,by_y,j_z)
-{
-
- index_C<-which (names(Env_Campylobacter_data)==variable)
- index_y_C<-which (names(Env_Campylobacter_data)==variable_y)
-
-
-
- wt<-(findInterval(Env_Campylobacter_data[,index_C],breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_some<-Env_Campylobacter_data[ww,]
-
-
- if (length(Env_Campylobacter_data_some[,1])!=0) {
-
- breaks_y<-as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE))
- breaks_y[length(breaks_y)]<-ceiling(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[length(breaks_y)]
- breaks_y[1]<-floor(as.numeric(quantile(na.omit(Env_Campylobacter_data_some[,index_y_C]), probs=seq(0,1, by=by_y), na.rm=TRUE)))[1]
-
- }else{
-
- breaks_y<-c()
- }
-
- return(breaks_y)
- }
-
-
-
-#################
-
-
-
-
-var_x_loc_df<-data.frame(character(), character(),numeric(),numeric(),numeric())
-colnames(var_x_loc_df)<-c(variable,variable_y,"counts","residents","residents_tot")
-
-residents_i_var<-0
-residents_universal<-0
-#i_var_max<-length(breaks_var)
-#i_var_min<-1
-#i_var_max_x<-length(breaks_var_x)
-#i_var_min_x<-1
-
-
-#####################
-by_z<-0.2
-by_y<-0.05
-
-
-#i_var_min<-breaks_z(variable,by_z)[1]
-#i_var_max<-breaks_z(variable,by_z)[length(breaks_z(variable,by_z))]
-j_z_min<-1
-j_z_max<-length(breaks_z_lab(variable,by_z))-1
-
-
-
-for (j_z in c(j_z_min:j_z_max))
-{
-
- wt<-(findInterval((Env_Campylobacter_data[,index_C]),breaks_z(variable,by_z)))
- ww<-which(wt==j_z)
- Env_Campylobacter_data_z<-Env_Campylobacter_data[ww,]
-
- wt<-(findInterval((Env_laboratory[,index]),breaks_z_lab(variable,by_z)))
- ww<-which(wt==j_z)
- Env_laboratory_z<-Env_laboratory[ww,]
-
- if (length(Env_Campylobacter_data_z[,1])!=0){
- if (length(breaks_y_lab(variable,variable_y,by_z,by_y,j_z))!=0){
-
- j_y_min<-1
- j_y_max<-length(breaks_y_lab(variable,variable_y,by_z,by_y,j_z))-1
-
-
-
- for (j_y in c(j_y_min:j_y_max))
- {
-
- wt<-(findInterval((Env_Campylobacter_data_z[,index_y_C]),breaks_y_lab(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_Campylobacter_data_y<-Env_Campylobacter_data_z[ww,]
-
- wt<-(findInterval((Env_laboratory_z[,index_y]),breaks_y_lab(variable,variable_y,by_z,by_y,j_z)))
- ww<-which(wt==j_y)
- Env_laboratory_y<-Env_laboratory_z[ww,]
-
-
-
-
- Total_cases<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$Cases))))
- residents<-sum((as.numeric(na.omit(Env_Campylobacter_data_y$residents))))
- residents_tot<-sum((as.numeric(na.omit(Env_laboratory_y$residents))))
-
- data_df<-data.frame(
- breaks_z_lab(variable,by_z)[j_z],
- breaks_y_lab(variable,variable_y,by_z,by_y,j_z)[j_y],
- Total_cases,
- residents,
- residents_tot)
-
-
-
-
-
- colnames(data_df)<-c(variable,variable_y,"counts","residents","residents_tot")
- var_x_loc_df<-rbind(var_x_loc_df,data_df)
- print(c(j_y,j_z, Total_cases))
-
- }
- }
-
-
- }}
-
-
-write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",width_char,"_Simulated_for_rec_original_MEDMI_lab.csv",sep=""))
diff --git a/PAPER_Efficient_Simulated_Campylobacter_environment_analysis_subset_3_variables_for_reconstruction.R b/PAPER_Efficient_Simulated_Campylobacter_environment_analysis_subset_3_variables_for_reconstruction.R
deleted file mode 100644
index 35cf5be77fe7ff47fb9a2d9f8af5bae93617b0e9..0000000000000000000000000000000000000000
--- a/PAPER_Efficient_Simulated_Campylobacter_environment_analysis_subset_3_variables_for_reconstruction.R
+++ /dev/null
@@ -1,797 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
-
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-#library(Hmisc)
-
-list.of.packages <- c("xts")
-new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-if(length(new.packages)) install.packages(new.packages)
-library(xts)
-
-
-
-width<-30
-width_char<-paste(width)
-
-
-variable_x<-"min_air_temp"
-#variable_y<-"wind"
-variable_y<-"rain"
-#variable_y<-"humidity"
-variable<-"light"
-#variable<-"rain"
-
-#write.table(Env_Campylobacter_data_all2,paste("../DataBase/Cases_Environment/Campylobacter_environment_",width_char,".csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-#colnames(Env_Campylobacter_data_all2)<-c("PostCode","PHE_Centre_Name","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-#colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-#colnames(Env_laboratory_weekly)<-c("PostCode","Date","humidity","max_temp","min_temp","rain","cum_rain","wind","residents")
-colnames(Env_laboratory_weekly)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#For Dorset only
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-#Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_PHE,year(Env_Campylobacter_data_PHE$Date)>=2001 & year(Env_Campylobacter_data_PHE$Date)<2012)
-
-Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-
-Env_laboratory_int1<-subset(Env_laboratory_weekly,year(Env_laboratory_weekly$Date)>=1990 & year(Env_laboratory_weekly$Date)<=2015)
-################### weekly summary
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-#names(Coord_laboratory)
-#as.numeric(Coord_laboratory[1,])
-#as.numeric(Coord_laboratory[2,])
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
-
-daylength<-function(lat,day_year)
- {
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Env_laboratory_int2$lat
-day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-#var_list<-list(lat,day_year)
-#lapply(var_list,daylength)
-#lapply(Env_laboratory,daylength, Env_laboratory$lat)
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#daylength_df$lat<-as.factor(daylength_df$lat)
-#daylength_df$Date<-as.factor(daylength_df$Date)
-#Env_laboratory_int2$lat<-as.factor(Env_laboratory_int2$lat)
-#Env_laboratory_int2$Date<-as.factor(Env_laboratory_int2$Date)
-#Env_laboratory<-merge(Env_laboratory_int2,daylength_df,by=c("lat","Date"))
-
-Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
-
-### repeat for the data only ####
-
-latitude<-Env_Campylobacter_data_int2$lat
-day_of_the_year<-yday(as.Date(Env_Campylobacter_data_int2$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Campylobacter_data_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#daylength_df$lat<-as.factor(daylength_df$lat)
-#daylength_df$Date<-as.factor(daylength_df$Date)
-#Env_Campylobacter_data_int2$lat<-as.factor(Env_Campylobacter_data_int2$lat)
-#Env_Campylobacter_data_int2$Date<-as.factor(Env_Campylobacter_data_int2$Date)
-
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int2,daylength_df)
-
-
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-1
-delta_cum_rain<-20
-delta_wind<-2
-delta_light<-1
-breaks_hum<-seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp)
-breaks_max_temp<-seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation))-1,0), max(na.omit(Env_laboratory$Mean_Precipitation))+1,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory_weekly$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-breaks_light<-seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-# First find right domain where the values have no NA
-#i_hum_min<-max(min(which(breaks_hum>=min(na.omit(Env_Campylobacter_data$Relative_humidity)))-1,length(breaks_hum)),1)
-
-#i_min_temp_min<-max(min(min(which(breaks_min_temp>=min(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))-1,length(breaks_min_temp)),1)
-#i_min_temp_max<-max(max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))+1,1)
-
-#i_max_temp_min<-max(min(min(which(breaks_max_temp>=min(na.omit(Env_Campylobacter_data$max_temp))))-1,length(breaks_max_temp)),1)
-#i_max_temp_max<-max(max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$max_temp))))+1,1)
-
-#i_rain_min<-max(min(min(which(breaks_rain>=min(na.omit(Env_Campylobacter_data$Mean_Precipitation))))-1,length(breaks_rain)),1),1)
-#i_rain_max<-max(min(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$Mean_Precipitation))))+1,1)
-
-#i_cum_rain_min<-max(min(min(which(breaks_cum_rain>=min(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))-1,length(breaks_cum_rain)),1)
-#i_cum_rain_max<-max(min(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))+1,1)
-
-#i_wind_min<-max(min(min(which(breaks_wind>=min(na.omit(Env_Campylobacter_data$Mean_wind_speed))))-1,length(breaks_wind)),1)
-#i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$Mean_wind_speed))))+1
-
-
-#print(c(i_hum_min,i_min_temp_min ,i_max_temp_min, i_rain_min,i_wind_min))
-#print(c(i_hum_min,i_hum_max,i_min_temp_min ,i_min_temp_max,i_max_temp_min,i_max_temp_max, i_rain_min,i_rain_max,i_wind_min,i_wind_max))
-
-
-
-i_hum_min<-max(which(breaks_hum<=min(na.omit(Env_Campylobacter_data$Relative_humidity))))
-i_hum_max<-max(which(breaks_hum<=max(na.omit(Env_Campylobacter_data$Relative_humidity))))
-
-i_min_temp_min<-max(which(breaks_min_temp<=min(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-i_min_temp_max<-max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-
-i_max_temp_min<-max(which(breaks_max_temp<=min(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-i_max_temp_max<-max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-
-i_rain_min<-max(which(breaks_rain<=min(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-i_rain_max<-max(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-
-i_cum_rain_min<-max(which(breaks_cum_rain<=min(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-i_cum_rain_max<-max(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-
-i_wind_min<-max(which(breaks_wind<=min(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-
-
-i_light_min<-max(which(breaks_light<=min(na.omit(Env_Campylobacter_data$daylength))))
-i_light_max<-max(which(breaks_light<=max(na.omit(Env_Campylobacter_data$daylength))))
-
-############################# General variables ###########################
-
-
-
-
-
-var_x_loc_df<-c()
-
-if (variable=="light"){
-
- breaks_var<-breaks_light
- i_var_min<-i_light_min
- i_var_max<-i_light_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$daylength
- Env_laboratory_var<-Env_laboratory$daylength
-}
-
-if (variable_x=="light"){
-
- i_var_x_min<-i_light_min
- i_var_x_max<-i_light_max
- breaks_var_x<-breaks_light
-}
-
-if (variable_y=="light"){
-
- i_var_y_min<-i_light_min
- i_var_y_max<-i_light_max
- breaks_var_y<-breaks_light
-}
-
-
-if (variable=="max_air_temp"){
-
- breaks_var<-breaks_max_temp
- i_var_min<-i_max_temp_min
- i_var_max<-i_max_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Maximum_air_temperature
- Env_laboratory_var<-Env_laboratory$Maximum_air_temperature
-}
-if (variable_x=="max_air_temp"){
-
- i_var_x_min<-i_max_temp_min
- i_var_x_max<-i_max_temp_max
- breaks_var_x<-breaks_max_temp
-}
-
-if (variable_y=="max_air_temp"){
-
- i_var_y_min<-i_max_temp_min
- i_var_y_max<-i_max_temp_max
- breaks_var_y<-breaks_max_temp
-}
-
-if (variable=="min_air_temp"){
-
- breaks_var<-breaks_min_temp
- i_var_min<-i_min_temp_min
- i_var_max<-i_min_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Minimum_air_temperature
- Env_laboratory_var<-Env_laboratory$Minimum_air_temperature
-}
-if (variable_x=="min_air_temp"){
-
- i_var_x_min<-i_min_temp_min
- i_var_x_max<-i_min_temp_max
- breaks_var_x<-breaks_min_temp
-}
-
-if (variable_y=="min_air_temp"){
-
- i_var_y_min<-i_min_temp_min
- i_var_y_max<-i_min_temp_max
- breaks_var_y<-breaks_min_temp
-}
-
-if (variable=="humidity"){
-
- breaks_var<-breaks_hum
- i_var_min<-i_hum_min
- i_var_max<-i_hum_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Relative_humidity
- Env_laboratory_var<-Env_laboratory$Relative_humidity
-}
-if (variable_x=="humidity"){
-
- i_var_x_min<-i_hum_min
- i_var_x_max<-i_hum_max
- breaks_var_x<-breaks_hum
-}
-
-
-if (variable_y=="humidity"){
-
- i_var_y_min<-i_hum_min
- i_var_y_max<-i_hum_max
- breaks_var_y<-breaks_hum
-}
-
-
-if (variable=="mean_temp"){
-
- breaks_var<-breaks_mean_temp
- i_var_min<-i_mean_temp_min
- i_var_max<-i_mean_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$mean_temp
- Env_laboratory_var<-Env_laboratory$mean_temp
-}
-if (variable_x=="mean_temp"){
-
- i_var_x_min<-i_mean_temp_min
- i_var_x_max<-i_mean_temp_max
- breaks_var_x<-breaks_mean_temp
-}
-
-
-if (variable_y=="mean_temp"){
-
- i_var_y_min<-i_mean_temp_min
- i_var_y_max<-i_mean_temp_max
- breaks_var_y<-breaks_mean_temp
-}
-
-
-if (variable=="rain"){
-
- breaks_var<-breaks_rain
- i_var_min<-i_rain_min
- i_var_max<-i_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_Precipitation
- Env_laboratory_var<-Env_laboratory$Mean_Precipitation
-}
-if (variable_x=="rain"){
-
- i_var_x_min<-i_rain_min
- i_var_x_max<-i_rain_max
- breaks_var_x<-breaks_rain
-}
-
-
-if (variable_y=="rain"){
-
- i_var_y_min<-i_rain_min
- i_var_y_max<-i_rain_max
- breaks_var_y<-breaks_rain
-}
-
-if (variable=="cum_rain"){
-
- breaks_var<-breaks_cum_rain
- i_var_min<-i_cum_rain_min
- i_var_max<-i_cum_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Cumul_Precipitation
- Env_laboratory_var<-Env_laboratory$Cumul_Precipitation
-}
-if (variable_x=="cum_rain"){
-
- i_var_x_min<-i_cum_rain_min
- i_var_x_max<-i_cum_rain_max
- breaks_var_x<-breaks_cum_rain
-}
-
-if (variable_y=="cum_rain"){
-
- i_var_y_min<-i_cum_rain_min
- i_var_y_max<-i_cum_rain_max
- breaks_var_y<-breaks_cum_rain
-}
-
-if (variable=="wind"){
-
- breaks_var<-breaks_wind
- i_var_min<-i_wind_min
- i_var_max<-i_wind_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_wind_speed
- Env_laboratory_var<-Env_laboratory$Mean_wind_speed
-}
-if (variable_x=="wind"){
-
- i_var_x_min<-i_wind_min
- i_var_x_max<-i_wind_max
- breaks_var_x<-breaks_wind
-}
-
-
-if (variable_y=="wind"){
-
- i_var_y_min<-i_wind_min
- i_var_y_max<-i_wind_max
- breaks_var_y<-breaks_wind
-}
-
-
-Yt_var_x<-function(i_var,i_var_y)
-{
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt1<-subset(Env_Campylobacter_data,Env_Campylobacter_data_var>=breaks_var[i_var])
- } else {
- Yt1<-subset(Env_Campylobacter_data,Env_Campylobacter_data_var>=breaks_var[i_var] & Env_Campylobacter_data_var<breaks_var[i_var+1])
- }
-
- if(variable_y=="humidity"){
-
- if(is.na(breaks_hum[i_var_y+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y] & Yt1$Relative_humidity<=breaks_hum[i_var_y+1])
- }
- }
- if(variable_y=="wind"){
- if(is.na(breaks_wind[i_var_y+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y] & Yt1$Mean_wind_speed<=breaks_wind[i_var_y+1])
- }
- }
- if(variable_y=="rain"){
- if(is.na(breaks_rain[i_var_y+1])==TRUE)
-{
- Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y] & Yt1$Mean_Precipitation<=breaks_rain[i_var_y+1])
- }
-
- }
-
- if(variable_y=="cum_rain"){
- if(is.na(breaks_cum_rain[i_var_y+1])==TRUE)
-{
- Yt2<-subset(Yt1,Yt1$Cumul_Precipitation>=breaks_cum_rain[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Cumul_Precipitation>=breaks_cum_rain[i_var_y] & Yt1$Cumul_Precipitation<=breaks_cum_rain[i_var_y+1])
- }
-
- }
-
- return(as.list(Yt2))
-
-}
-
-
-Tot_var_x<-function(i_var,i_var_y)
-{
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt1<-subset(Env_laboratory,Env_laboratory_var>=breaks_var[i_var])
- } else {
- Yt1<-subset(Env_laboratory,Env_laboratory_var>=breaks_var[i_var] & Env_laboratory_var<breaks_var[i_var+1])
- }
-
- if(variable_y=="humidity"){
-
- if(is.na(breaks_hum[i_var_y+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y] & Yt1$Relative_humidity<=breaks_hum[i_var_y+1])
- }
- }
-
- if(variable_y=="wind"){
-
- if(is.na(breaks_wind[i_var_y+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y] & Yt1$Mean_wind_speed<=breaks_wind[i_var_y+1])
- }
-
- }
-
- if(variable_y=="rain"){
-
- if(is.na(breaks_rain[i_var_y+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y] & Yt1$Mean_Precipitation<=breaks_rain[i_var_y+1])
- }
-
- }
-
-
- return(as.list(Yt2))
-
-}
-
-
-
-var_x_loc_df<-c(0)
-n_seas<-1
-residents_i_var<-0
-residents_universal<-0
-#i_var_max<-length(breaks_var)
-#i_var_min<-1
-#i_var_max_x<-length(breaks_var_x)
-#i_var_min_x<-1
-
-for (i_var in c(i_var_min:i_var_max))
-{
-
- for (i_var_y in c(i_var_y_min:i_var_y_max))
- {
-
-
- for (i in c(1:n_seas))
- {
-
- n_months<-12/n_seas
-
-
- wt<-which(month(Yt_var_x(i_var,i_var_y )$Date)>(i-1)*n_months & month(Yt_var_x(i_var,i_var_y )$Date)<=i*n_months)
- wt_tot<-which(month(Tot_var_x(i_var,i_var_y )$Date)>(i-1)*n_months & month(Tot_var_x(i_var,i_var_y )$Date)<=i*n_months)
-
- if (variable_x=="min_air_temp"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Minimum_air_temperature[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$Minimum_air_temperature[wt_tot]
-
-
-
-
- }
- if (variable_x=="max_air_temp"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Maximum_air_temperature[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$Maximum_air_temperature[wt_tot]
-
- }
-
- if (variable_x=="mean_temp"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$mean_temp[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$mean_temp[wt_tot]
-
- }
-
-
-
- if (variable_x=="humidity"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Relative_humidity[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$Relative_humidity[wt_tot]
-
- }
- if (variable_x=="wind"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Mean_wind_speed[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$Mean_wind_speed[wt_tot]
-
- }
-
- if (variable_x=="light"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$daylength[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$daylength[wt_tot]
-
- }
-
- if (variable_x=="rain"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Mean_Precipitation[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$Mean_Precipitation[wt_tot]
-
- }
- if (variable_x=="cum_rain"){
- Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Cumul_Precipitation[wt]
- var_x_tot<-Tot_var_x(i_var,i_var_y )$Cumul_Precipitation[wt_tot]
-
- }
-
-
- n_var_x_tot<-hist(var_x_tot,breaks=breaks_var_x)
- n_var_x<-hist((as.numeric(as.character(Campylobacter_var_x))),breaks=breaks_var_x)
-
- residents<-rep(0,times=length(breaks_var_x)-1)
- residents_tot<-rep(0,times=length(breaks_var_x)-1)
- numb_PC<-rep(0,times=length(breaks_var_x)-1)
-
- wt<-which(n_var_x_tot$counts!=0)
- wt2<-which(n_var_x$counts!=0)
- delta_step_tot<-diff(n_var_x_tot$breaks)
- delta_step<-diff(n_var_x$breaks) #Actuallly these two are identical, but left as it is for generality
-
- if (variable_x=="min_air_temp"){
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
-
- ww<-which(Tot_var_x(i_var,i_var_y)$Minimum_air_temperature>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$Minimum_air_temperature<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$Minimum_air_temperature>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Minimum_air_temperature<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
-
-
- }
-
-
- if (variable_x=="max_air_temp"){
-
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
- ww<-which(Tot_var_x(i_var,i_var_y)$Maximum_air_temperature>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$Maximum_air_temperature<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$Maximum_air_temperature>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Maximum_air_temperature<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
-
- }
-
- if (variable_x=="light"){
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
-
- ww<-which(Tot_var_x(i_var,i_var_y)$daylength>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$daylength<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$daylength>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$daylength<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
-
-
- }
-
-
-
- if (variable_x=="mean_temp"){
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
- ww<-which(Tot_var_x(i_var,i_var_y)$mean_temp>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$mean_temp<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$mean_temp>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$mean_temp<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
- }
-
-
- if (variable_x=="humidity"){
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
- ww<-which(Tot_var_x(i_var,i_var_y)$hum>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$hum<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$hum>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$hum<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
- }
- if (variable_x=="wind"){
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
- ww<-which(Tot_var_x(i_var,i_var_y)$Mean_wind_speed>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$Mean_wind_speed<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]])
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$Mean_wind_speed>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Mean_wind_speed<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
-
- }
- if (variable_x=="rain"){
-
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
- ww<-which(Tot_var_x(i_var,i_var_y)$Mean_Precipitation>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$Mean_Precipitation<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]])
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$Mean_Precipitation>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Mean_Precipitation<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
- }
-
- if (variable_x=="cum_rain"){
-
- if(length(wt)>0){
- for (j in c(1:(length(wt)))){
-
- ww<-which(Tot_var_x(i_var,i_var_y)$Cumul_Precipitation>=n_var_x_tot$breaks[wt[j]] &
- Tot_var_x(i_var,i_var_y)$Cumul_Precipitation<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]])
- residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
- numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
- }
- if(length(wt2)>0){
- for (j in c(1:(length(wt2)))){
-
- ww<-which(Yt_var_x(i_var,i_var_y)$Cumul_Precipitation>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Cumul_Precipitation<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
- residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
- }
- }
-
- }
- }
-
-
-
- data_df<-data.frame(
- n_var_x$mids,
- n_var_x$breaks[-length(n_var_x$breaks)],
- n_var_x$counts/n_var_x_tot$counts,
- (n_var_x$counts)/(residents_tot),
- i,
- breaks_var[i_var],
- breaks_var_y[i_var_y],
- n_var_x$counts,
- n_var_x_tot$counts,
- residents,
- residents_tot,
- numb_PC)
-
-
- colnames(data_df)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
- var_x_loc_df<-rbind(var_x_loc_df,data_df)
- colnames(var_x_loc_df)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
- #}
- #}
-
-
- }
- }
-residents_i_var<-residents_i_var+sum(residents_tot)
-}
-
-residents_universal<residents_universal+sum(residents_i_var)
-residents_i_var
-residents_universal
-
-write.table(var_x_loc_df,paste("../../Data_Base/Cases_Environment/",variable,"_",variable_y,"_",variable_x,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
diff --git a/PAPER_Efficient_Simulated_Campylobacter_environment_analysis_subset_3_variables_for_reconstruction.Rout b/PAPER_Efficient_Simulated_Campylobacter_environment_analysis_subset_3_variables_for_reconstruction.Rout
deleted file mode 100644
index 6bbf6a47b12f1cfad3781bbe5b88098506ab2653..0000000000000000000000000000000000000000
--- a/PAPER_Efficient_Simulated_Campylobacter_environment_analysis_subset_3_variables_for_reconstruction.Rout
+++ /dev/null
@@ -1,863 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
->
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> #library(Hmisc)
->
-> list.of.packages <- c("xts")
-> new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
-> if(length(new.packages)) install.packages(new.packages)
-> library(xts)
-Loading required package: zoo
-
-Attaching package: ‘zoo’
-
-The following object is masked from ‘package:timeSeries’:
-
- time<-
-
-The following objects are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-
-Attaching package: ‘xts’
-
-The following objects are masked from ‘package:pastecs’:
-
- first, last
-
->
->
->
-> width<-30
-> width_char<-paste(width)
->
->
-> variable_x<-"min_air_temp"
-> #variable_y<-"wind"
-> variable_y<-"rain"
-> #variable_y<-"humidity"
-> variable<-"light"
-> #variable<-"rain"
->
-> #write.table(Env_Campylobacter_data_all2,paste("../DataBase/Cases_Environment/Campylobacter_environment_",width_char,".csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
->
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> #colnames(Env_Campylobacter_data_all2)<-c("PostCode","PHE_Centre_Name","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-> #colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> Env_laboratory_weekly<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory_weekly<-Env_laboratory_weekly[,-1]
-> #colnames(Env_laboratory_weekly)<-c("PostCode","Date","humidity","max_temp","min_temp","rain","cum_rain","wind","residents")
-> colnames(Env_laboratory_weekly)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #For Dorset only
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> #Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_PHE,year(Env_Campylobacter_data_PHE$Date)>=2001 & year(Env_Campylobacter_data_PHE$Date)<2012)
->
-> Env_Campylobacter_data_int1<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
->
-> Env_laboratory_int1<-subset(Env_laboratory_weekly,year(Env_laboratory_weekly$Date)>=1990 & year(Env_laboratory_weekly$Date)<=2015)
-> ################### weekly summary
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
->
-> #names(Coord_laboratory)
-> #as.numeric(Coord_laboratory[1,])
-> #as.numeric(Coord_laboratory[2,])
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
-> Env_laboratory_int2<-merge(Env_laboratory_int1,lat_long_lab,by="PostCode")
-> Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data_int1,lat_long_lab,by="PostCode")
->
-> daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
->
-> latitude<-Env_laboratory_int2$lat
-> day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-> #var_list<-list(lat,day_year)
-> #lapply(var_list,daylength)
-> #lapply(Env_laboratory,daylength, Env_laboratory$lat)
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
-> #daylength_df$lat<-as.factor(daylength_df$lat)
-> #daylength_df$Date<-as.factor(daylength_df$Date)
-> #Env_laboratory_int2$lat<-as.factor(Env_laboratory_int2$lat)
-> #Env_laboratory_int2$Date<-as.factor(Env_laboratory_int2$Date)
-> #Env_laboratory<-merge(Env_laboratory_int2,daylength_df,by=c("lat","Date"))
->
-> Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
->
-> ### repeat for the data only ####
->
-> latitude<-Env_Campylobacter_data_int2$lat
-> day_of_the_year<-yday(as.Date(Env_Campylobacter_data_int2$Date))
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Campylobacter_data_int2$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
-> #daylength_df$lat<-as.factor(daylength_df$lat)
-> #daylength_df$Date<-as.factor(daylength_df$Date)
-> #Env_Campylobacter_data_int2$lat<-as.factor(Env_Campylobacter_data_int2$lat)
-> #Env_Campylobacter_data_int2$Date<-as.factor(Env_Campylobacter_data_int2$Date)
->
-> Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int2,daylength_df)
->
->
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-1
-> delta_cum_rain<-20
-> delta_wind<-2
-> delta_light<-1
-> breaks_hum<-seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation))-0,0), max(na.omit(Env_laboratory$Mean_Precipitation))+0,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory_weekly$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-> breaks_light<-seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
-> # First find right domain where the values have no NA
-> #i_hum_min<-max(min(which(breaks_hum>=min(na.omit(Env_Campylobacter_data$Relative_humidity)))-1,length(breaks_hum)),1)
->
-> #i_min_temp_min<-max(min(min(which(breaks_min_temp>=min(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))-1,length(breaks_min_temp)),1)
-> #i_min_temp_max<-max(max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))+1,1)
->
-> #i_max_temp_min<-max(min(min(which(breaks_max_temp>=min(na.omit(Env_Campylobacter_data$max_temp))))-1,length(breaks_max_temp)),1)
-> #i_max_temp_max<-max(max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$max_temp))))+1,1)
->
-> #i_rain_min<-max(min(min(which(breaks_rain>=min(na.omit(Env_Campylobacter_data$Mean_Precipitation))))-1,length(breaks_rain)),1),1)
-> #i_rain_max<-max(min(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$Mean_Precipitation))))+1,1)
->
-> #i_cum_rain_min<-max(min(min(which(breaks_cum_rain>=min(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))-1,length(breaks_cum_rain)),1)
-> #i_cum_rain_max<-max(min(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))+1,1)
->
-> #i_wind_min<-max(min(min(which(breaks_wind>=min(na.omit(Env_Campylobacter_data$Mean_wind_speed))))-1,length(breaks_wind)),1)
-> #i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$Mean_wind_speed))))+1
->
->
-> #print(c(i_hum_min,i_min_temp_min ,i_max_temp_min, i_rain_min,i_wind_min))
-> #print(c(i_hum_min,i_hum_max,i_min_temp_min ,i_min_temp_max,i_max_temp_min,i_max_temp_max, i_rain_min,i_rain_max,i_wind_min,i_wind_max))
->
->
->
-> i_hum_min<-max(which(breaks_hum<=min(na.omit(Env_Campylobacter_data$Relative_humidity))))
-> i_hum_max<-max(which(breaks_hum<=max(na.omit(Env_Campylobacter_data$Relative_humidity))))
->
-> i_min_temp_min<-max(which(breaks_min_temp<=min(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-> i_min_temp_max<-max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
->
-> i_max_temp_min<-max(which(breaks_max_temp<=min(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-> i_max_temp_max<-max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
->
-> i_rain_min<-max(which(breaks_rain<=min(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-> i_rain_max<-max(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
->
-> i_cum_rain_min<-max(which(breaks_cum_rain<=min(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-> i_cum_rain_max<-max(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
->
-> i_wind_min<-max(which(breaks_wind<=min(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-> i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
->
->
-> i_light_min<-max(which(breaks_light<=min(na.omit(Env_Campylobacter_data$daylength))))
-> i_light_max<-max(which(breaks_light<=max(na.omit(Env_Campylobacter_data$daylength))))
->
-> ############################# General variables ###########################
->
->
->
->
->
-> var_x_loc_df<-c()
->
-> if (variable=="light"){
-+
-+ breaks_var<-breaks_light
-+ i_var_min<-i_light_min
-+ i_var_max<-i_light_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$daylength
-+ Env_laboratory_var<-Env_laboratory$daylength
-+ }
->
-> if (variable_x=="light"){
-+
-+ i_var_x_min<-i_light_min
-+ i_var_x_max<-i_light_max
-+ breaks_var_x<-breaks_light
-+ }
->
-> if (variable_y=="light"){
-+
-+ i_var_y_min<-i_light_min
-+ i_var_y_max<-i_light_max
-+ breaks_var_y<-breaks_light
-+ }
->
->
-> if (variable=="max_air_temp"){
-+
-+ breaks_var<-breaks_max_temp
-+ i_var_min<-i_max_temp_min
-+ i_var_max<-i_max_temp_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Maximum_air_temperature
-+ Env_laboratory_var<-Env_laboratory$Maximum_air_temperature
-+ }
-> if (variable_x=="max_air_temp"){
-+
-+ i_var_x_min<-i_max_temp_min
-+ i_var_x_max<-i_max_temp_max
-+ breaks_var_x<-breaks_max_temp
-+ }
->
-> if (variable_y=="max_air_temp"){
-+
-+ i_var_y_min<-i_max_temp_min
-+ i_var_y_max<-i_max_temp_max
-+ breaks_var_y<-breaks_max_temp
-+ }
->
-> if (variable=="min_air_temp"){
-+
-+ breaks_var<-breaks_min_temp
-+ i_var_min<-i_min_temp_min
-+ i_var_max<-i_min_temp_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Minimum_air_temperature
-+ Env_laboratory_var<-Env_laboratory$Minimum_air_temperature
-+ }
-> if (variable_x=="min_air_temp"){
-+
-+ i_var_x_min<-i_min_temp_min
-+ i_var_x_max<-i_min_temp_max
-+ breaks_var_x<-breaks_min_temp
-+ }
->
-> if (variable_y=="min_air_temp"){
-+
-+ i_var_y_min<-i_min_temp_min
-+ i_var_y_max<-i_min_temp_max
-+ breaks_var_y<-breaks_min_temp
-+ }
->
-> if (variable=="humidity"){
-+
-+ breaks_var<-breaks_hum
-+ i_var_min<-i_hum_min
-+ i_var_max<-i_hum_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Relative_humidity
-+ Env_laboratory_var<-Env_laboratory$Relative_humidity
-+ }
-> if (variable_x=="humidity"){
-+
-+ i_var_x_min<-i_hum_min
-+ i_var_x_max<-i_hum_max
-+ breaks_var_x<-breaks_hum
-+ }
->
->
-> if (variable_y=="humidity"){
-+
-+ i_var_y_min<-i_hum_min
-+ i_var_y_max<-i_hum_max
-+ breaks_var_y<-breaks_hum
-+ }
->
->
-> if (variable=="mean_temp"){
-+
-+ breaks_var<-breaks_mean_temp
-+ i_var_min<-i_mean_temp_min
-+ i_var_max<-i_mean_temp_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$mean_temp
-+ Env_laboratory_var<-Env_laboratory$mean_temp
-+ }
-> if (variable_x=="mean_temp"){
-+
-+ i_var_x_min<-i_mean_temp_min
-+ i_var_x_max<-i_mean_temp_max
-+ breaks_var_x<-breaks_mean_temp
-+ }
->
->
-> if (variable_y=="mean_temp"){
-+
-+ i_var_y_min<-i_mean_temp_min
-+ i_var_y_max<-i_mean_temp_max
-+ breaks_var_y<-breaks_mean_temp
-+ }
->
->
-> if (variable=="rain"){
-+
-+ breaks_var<-breaks_rain
-+ i_var_min<-i_rain_min
-+ i_var_max<-i_rain_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_Precipitation
-+ Env_laboratory_var<-Env_laboratory$Mean_Precipitation
-+ }
-> if (variable_x=="rain"){
-+
-+ i_var_x_min<-i_rain_min
-+ i_var_x_max<-i_rain_max
-+ breaks_var_x<-breaks_rain
-+ }
->
->
-> if (variable_y=="rain"){
-+
-+ i_var_y_min<-i_rain_min
-+ i_var_y_max<-i_rain_max
-+ breaks_var_y<-breaks_rain
-+ }
->
-> if (variable=="cum_rain"){
-+
-+ breaks_var<-breaks_cum_rain
-+ i_var_min<-i_cum_rain_min
-+ i_var_max<-i_cum_rain_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Cumul_Precipitation
-+ Env_laboratory_var<-Env_laboratory$Cumul_Precipitation
-+ }
-> if (variable_x=="cum_rain"){
-+
-+ i_var_x_min<-i_cum_rain_min
-+ i_var_x_max<-i_cum_rain_max
-+ breaks_var_x<-breaks_cum_rain
-+ }
->
-> if (variable_y=="cum_rain"){
-+
-+ i_var_y_min<-i_cum_rain_min
-+ i_var_y_max<-i_cum_rain_max
-+ breaks_var_y<-breaks_cum_rain
-+ }
->
-> if (variable=="wind"){
-+
-+ breaks_var<-breaks_wind
-+ i_var_min<-i_wind_min
-+ i_var_max<-i_wind_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_wind_speed
-+ Env_laboratory_var<-Env_laboratory$Mean_wind_speed
-+ }
-> if (variable_x=="wind"){
-+
-+ i_var_x_min<-i_wind_min
-+ i_var_x_max<-i_wind_max
-+ breaks_var_x<-breaks_wind
-+ }
->
->
-> if (variable_y=="wind"){
-+
-+ i_var_y_min<-i_wind_min
-+ i_var_y_max<-i_wind_max
-+ breaks_var_y<-breaks_wind
-+ }
->
->
-> Yt_var_x<-function(i_var,i_var_y)
-+ {
-+
-+ if(is.na(breaks_var[i_var+1])==TRUE)
-+ {
-+ Yt1<-subset(Env_Campylobacter_data,Env_Campylobacter_data_var>=breaks_var[i_var])
-+ } else {
-+ Yt1<-subset(Env_Campylobacter_data,Env_Campylobacter_data_var>=breaks_var[i_var] & Env_Campylobacter_data_var<breaks_var[i_var+1])
-+ }
-+
-+ if(variable_y=="humidity"){
-+
-+ if(is.na(breaks_hum[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y] & Yt1$Relative_humidity<=breaks_hum[i_var_y+1])
-+ }
-+ }
-+ if(variable_y=="wind"){
-+ if(is.na(breaks_wind[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y] & Yt1$Mean_wind_speed<=breaks_wind[i_var_y+1])
-+ }
-+ }
-+ if(variable_y=="rain"){
-+ if(is.na(breaks_rain[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y] & Yt1$Mean_Precipitation<=breaks_rain[i_var_y+1])
-+ }
-+
-+ }
-+
-+ if(variable_y=="cum_rain"){
-+ if(is.na(breaks_cum_rain[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Cumul_Precipitation>=breaks_cum_rain[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Cumul_Precipitation>=breaks_cum_rain[i_var_y] & Yt1$Cumul_Precipitation<=breaks_cum_rain[i_var_y+1])
-+ }
-+
-+ }
-+
-+ return(as.list(Yt2))
-+
-+ }
->
->
-> Tot_var_x<-function(i_var,i_var_y)
-+ {
-+
-+ if(is.na(breaks_var[i_var+1])==TRUE)
-+ {
-+ Yt1<-subset(Env_laboratory,Env_laboratory_var>=breaks_var[i_var])
-+ } else {
-+ Yt1<-subset(Env_laboratory,Env_laboratory_var>=breaks_var[i_var] & Env_laboratory_var<breaks_var[i_var+1])
-+ }
-+
-+ if(variable_y=="humidity"){
-+
-+ if(is.na(breaks_hum[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_hum[i_var_y] & Yt1$Relative_humidity<=breaks_hum[i_var_y+1])
-+ }
-+ }
-+
-+ if(variable_y=="wind"){
-+
-+ if(is.na(breaks_wind[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Mean_wind_speed>=breaks_wind[i_var_y] & Yt1$Mean_wind_speed<=breaks_wind[i_var_y+1])
-+ }
-+
-+ }
-+
-+ if(variable_y=="rain"){
-+
-+ if(is.na(breaks_rain[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Mean_Precipitation>=breaks_rain[i_var_y] & Yt1$Mean_Precipitation<=breaks_rain[i_var_y+1])
-+ }
-+
-+ }
-+
-+
-+ return(as.list(Yt2))
-+
-+ }
->
->
->
-> var_x_loc_df<-c(0)
-> n_seas<-1
-> residents_i_var<-0
-> residents_universal<-0
-> #i_var_max<-length(breaks_var)
-> #i_var_min<-1
-> #i_var_max_x<-length(breaks_var_x)
-> #i_var_min_x<-1
->
-> for (i_var in c(i_var_min:i_var_max))
-+ {
-+
-+ for (i_var_y in c(i_var_y_min:i_var_y_max))
-+ {
-+
-+
-+ for (i in c(1:n_seas))
-+ {
-+
-+ n_months<-12/n_seas
-+
-+
-+ wt<-which(month(Yt_var_x(i_var,i_var_y )$Date)>(i-1)*n_months & month(Yt_var_x(i_var,i_var_y )$Date)<=i*n_months)
-+ wt_tot<-which(month(Tot_var_x(i_var,i_var_y )$Date)>(i-1)*n_months & month(Tot_var_x(i_var,i_var_y )$Date)<=i*n_months)
-+
-+ if (variable_x=="min_air_temp"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Minimum_air_temperature[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$Minimum_air_temperature[wt_tot]
-+
-+
-+
-+
-+ }
-+ if (variable_x=="max_air_temp"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Maximum_air_temperature[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$Maximum_air_temperature[wt_tot]
-+
-+ }
-+
-+ if (variable_x=="mean_temp"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$mean_temp[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$mean_temp[wt_tot]
-+
-+ }
-+
-+
-+
-+ if (variable_x=="humidity"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Relative_humidity[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$Relative_humidity[wt_tot]
-+
-+ }
-+ if (variable_x=="wind"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Mean_wind_speed[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$Mean_wind_speed[wt_tot]
-+
-+ }
-+
-+ if (variable_x=="light"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$daylength[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$daylength[wt_tot]
-+
-+ }
-+
-+ if (variable_x=="rain"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Mean_Precipitation[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$Mean_Precipitation[wt_tot]
-+
-+ }
-+ if (variable_x=="cum_rain"){
-+ Campylobacter_var_x<-Yt_var_x(i_var,i_var_y )$Cumul_Precipitation[wt]
-+ var_x_tot<-Tot_var_x(i_var,i_var_y )$Cumul_Precipitation[wt_tot]
-+
-+ }
-+
-+
-+ n_var_x_tot<-hist(var_x_tot,breaks=breaks_var_x)
-+ n_var_x<-hist((as.numeric(as.character(Campylobacter_var_x))),breaks=breaks_var_x)
-+
-+ residents<-rep(0,times=length(breaks_var_x)-1)
-+ residents_tot<-rep(0,times=length(breaks_var_x)-1)
-+ numb_PC<-rep(0,times=length(breaks_var_x)-1)
-+
-+ wt<-which(n_var_x_tot$counts!=0)
-+ wt2<-which(n_var_x$counts!=0)
-+ delta_step_tot<-diff(n_var_x_tot$breaks)
-+ delta_step<-diff(n_var_x$breaks) #Actuallly these two are identical, but left as it is for generality
-+
-+ if (variable_x=="min_air_temp"){
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$Minimum_air_temperature>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$Minimum_air_temperature<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$Minimum_air_temperature>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Minimum_air_temperature<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+
-+
-+ }
-+
-+
-+ if (variable_x=="max_air_temp"){
-+
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$Maximum_air_temperature>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$Maximum_air_temperature<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$Maximum_air_temperature>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Maximum_air_temperature<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+
-+ }
-+
-+ if (variable_x=="light"){
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$daylength>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$daylength<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$daylength>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$daylength<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+
-+
-+ }
-+
-+
-+
-+ if (variable_x=="mean_temp"){
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$mean_temp>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$mean_temp<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$mean_temp>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$mean_temp<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+ }
-+
-+
-+ if (variable_x=="humidity"){
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$hum>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$hum<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]] )
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$hum>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$hum<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+ }
-+ if (variable_x=="wind"){
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$Mean_wind_speed>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$Mean_wind_speed<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]])
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$Mean_wind_speed>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Mean_wind_speed<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+
-+ }
-+ if (variable_x=="rain"){
-+
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$Mean_Precipitation>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$Mean_Precipitation<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]])
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$Mean_Precipitation>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Mean_Precipitation<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+ }
-+
-+ if (variable_x=="cum_rain"){
-+
-+ if(length(wt)>0){
-+ for (j in c(1:(length(wt)))){
-+
-+ ww<-which(Tot_var_x(i_var,i_var_y)$Cumul_Precipitation>=n_var_x_tot$breaks[wt[j]] &
-+ Tot_var_x(i_var,i_var_y)$Cumul_Precipitation<=n_var_x_tot$breaks[wt[j]]+delta_step_tot[wt[j]])
-+ residents_tot[wt[j]]<-sum(as.numeric(Tot_var_x(i_var,i_var_y)$residents[ww]))
-+ numb_PC[wt[j]]<-length(unique(Tot_var_x(i_var,i_var_y)$PostCode[ww]))
-+ }
-+ if(length(wt2)>0){
-+ for (j in c(1:(length(wt2)))){
-+
-+ ww<-which(Yt_var_x(i_var,i_var_y)$Cumul_Precipitation>=n_var_x$breaks[wt2[j]] & Yt_var_x(i_var,i_var_y)$Cumul_Precipitation<=n_var_x$breaks[wt2[j]]+delta_step[wt2[j]])
-+ residents[wt2[j]]<-sum(as.numeric(Yt_var_x(i_var,i_var_y)$residents[ww]))
-+ }
-+ }
-+
-+ }
-+ }
-+
-+
-+
-+ data_df<-data.frame(
-+ n_var_x$mids,
-+ n_var_x$breaks[-length(n_var_x$breaks)],
-+ n_var_x$counts/n_var_x_tot$counts,
-+ (n_var_x$counts)/(residents_tot),
-+ i,
-+ breaks_var[i_var],
-+ breaks_var_y[i_var_y],
-+ n_var_x$counts,
-+ n_var_x_tot$counts,
-+ residents,
-+ residents_tot,
-+ numb_PC)
-+
-+
-+ colnames(data_df)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ var_x_loc_df<-rbind(var_x_loc_df,data_df)
-+ colnames(var_x_loc_df)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ #}
-+ #}
-+
-+
-+ }
-+ }
-+ residents_i_var<-residents_i_var+sum(residents_tot)
-+ }
->
-> residents_universal<residents_universal+sum(residents_i_var)
-[1] TRUE
-> residents_i_var
-[1] 31294027
-> residents_universal
-[1] 0
->
-> write.table(var_x_loc_df,paste("../../Data_Base/Cases_Environment/",variable,"_",variable_y,"_",variable_x,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
-> proc.time()
- user system elapsed
-755.196 9.128 764.397
diff --git a/PAPER_Pathogen_Linkage_fixed_time_lag.R b/PAPER_Pathogen_Linkage_fixed_time_lag.R
deleted file mode 100644
index 29f4bee2beb3999fe39d57ef8c707a5281a4e664..0000000000000000000000000000000000000000
--- a/PAPER_Pathogen_Linkage_fixed_time_lag.R
+++ /dev/null
@@ -1,328 +0,0 @@
-# The code associates the value of the environmental variables to the Campylobacter cases at the location (diagnostic postcode) and date of occurrence with a chosen time-time_lag
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(xts)
-library(lubridate)
-
-
-## Environmental Variable file. Original MEDMI files
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-variable<-"min_air_temp"
-variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-min_temp<-variable_df_3[,-c(1,2)]
-min_temp<-min_temp[-c(1,2),]
-names(min_temp) <- NULL
-Minimum_air_temperature<-unlist(c(min_temp))
-
-
-variable<-"rain"
-variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-rain<-variable_df_4[,-c(1,2)]
-rain<-rain[-c(1,2),]
-names(rain) <- NULL
-Precipitation<-unlist(c(rain))
-
-variable<-"wind_speed"
-variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-wind<-variable_df_5[,-c(1,2)]
-wind<-wind[-c(1,2),]
-names(wind) <- NULL
-Mean_wind_speed<-unlist(c(wind))
-
-######################## include daylength ##################
-
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-PC_df<-data.frame(All_PC,as.Date(dates))
-colnames(PC_df)<-c("PostCode","Date")
-
-Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
-
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Post_Codes_df$lat
-day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-
-
-######################## Catchment areas #################
-
-catchment_population_df<-read.csv(paste("../../Data_Base/Catchment_areas/Sum_ByLab_1987_2016.csv",sep=""))
-colnames(catchment_population_df)<-c("PostCode","col2","col3",
- "residents_1987",
- "residents_1988",
- "residents_1989",
- "residents_1990",
- "residents_1991",
- "residents_1992",
- "residents_1993",
- "residents_1994",
- "residents_1995",
- "residents_1996",
- "residents_1997",
- "residents_1998",
- "residents_1999",
- "residents_2000",
- "residents_2001",
- "residents_2002",
- "residents_2003",
- "residents_2004",
- "residents_2005",
- "residents_2006",
- "residents_2007",
- "residents_2008",
- "residents_2009",
- "residents_2010",
- "residents_2011",
- "residents_2012",
- "residents_2013",
- "residents_2014",
- "residents_2015",
- "residents_2016",
- "residents_2011bis")
-
-diagnostic_laboratory_info_df<-read.csv(paste("../../Data_Base/Catchment_areas/Lab_information.csv",sep=""))
-diagnostic_laboratory_info_df<-diagnostic_laboratory_info_df[,-1]
-
-
-## This create a database for all laboratories postcode, even if there were zero cases
-diagnostic_laboratory_df2<-data.frame(All_PC,dates,
- Maximum_air_temperature,
- Minimum_air_temperature,
- Mean_wind_speed,
- Precipitation,
- Relative_humidity,
- daylength_df$daylength)
-
-colnames(diagnostic_laboratory_df2)<-c("PostCode","dates",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Precipitation",
- "Relative_humidity",
- "daylength")
-
-
-
-
-
-###################################
-
-starting_year_catchment_population_df<-1987
-end_year_catchment_population_df<-2016
-numb_years<-length(seq(starting_year_catchment_population_df:end_year_catchment_population_df))
-sequence_years<-seq(starting_year_catchment_population_df,end_year_catchment_population_df)
-
-diagnostic_laboratory_df2$year<-year(diagnostic_laboratory_df2$dates)
-
-catchment_population_df2<-catchment_population_df[,-c(1:3,34)]
-
-
-
-catchment_population_df3<-data.frame(
- rep(catchment_population_df$PostCode,times=numb_years),
- rep(sequence_years,each=length(unique(catchment_population_df$PostCode))),
- stack(catchment_population_df2)
-)
-colnames(catchment_population_df3)<-c("PostCode","year","residents","ind")
-
-
-#catchment_population_df3$ind<-unlist(strsplit(as.character(catchment_population_df3$ind), split='_', fixed=TRUE)[seq(1,length(catchment_population_df3$ind))])[2]
-
-catchment_population_df3$year<-as.factor(catchment_population_df3$year)
-diagnostic_laboratory_df2$year<-as.factor(diagnostic_laboratory_df2$year)
-
-diagnostic_laboratory_df<-merge(diagnostic_laboratory_df2, catchment_population_df3,by=c('PostCode','year'))
-#diagnostic_laboratory_df$Date<-diagnostic_laboratory_df$dates
-colnames(diagnostic_laboratory_df)[3]<-c("Date")
-
-
-write.table(diagnostic_laboratory_df,paste("../../Data_Base/OPIE_data_base/Environment_Laboratories_linkage_original_MEDMI.csv",sep=""),sep=',')
-
-
-
-
-
-################ Define the time lag here ###############################
-
-time_lag<-1
-time_lag_char<-paste(time_lag)
-
-## This create a database for all postocode where there was at least one case
-Campylobacter_cases<-read.csv("../../Data_Base/Cases/Campylobacter_Simulated_Data_final_UK.csv")
-Campylobacter_cases<-Campylobacter_cases[,-1]
-colnames(Campylobacter_cases)<-c("Cases","Adjusted","Spec_Date", "PostCode","Date","Simulation")
-
-Campylobacter_cases$PostCode<-stri_replace_all_fixed(Campylobacter_cases$PostCode, " ", "")
-Campylobacter_cases$Date<-as.Date((as.character(Campylobacter_cases$Date)))
-Campylobacter_cases$year<-as.factor((as.character(year(Campylobacter_cases$Date))))
-Campylobacter_cases$PostCode<-as.factor(Campylobacter_cases$PostCode)
-
-Campylobacter_cases_df<-merge(Campylobacter_cases, catchment_population_df3,by=c("PostCode","year"))
-
-
-########################### time_lag average ######################
-
-### This function calculate the average values of the environmental variables over a certain time-time_lag
-time_lag_PC<-function(lab_fac){
- lab<-as.character(lab_fac)
- merged_lab_sub<-subset(diagnostic_laboratory_df,diagnostic_laboratory_df$PostCode==lab)
- merged_lab_sub2<-merged_lab_sub[order(as.Date(merged_lab_sub$Date)),]
-
-
- mean_Maximum_air_temperature<-rollmean(merged_lab_sub2$Maximum_air_temperature,time_lag)
- mean_Minimum_air_temperature<-rollmean(merged_lab_sub2$Minimum_air_temperature,time_lag)
- mean_Mean_wind_speed<-rollmean(merged_lab_sub2$Mean_wind_speed,time_lag)
- cum_Precipitation<-rollsum(merged_lab_sub2$Precipitation,time_lag)
- mean_Precipitation<-rollmean(merged_lab_sub2$Precipitation,time_lag)
- mean_Relative_humidity<-rollmean(merged_lab_sub2$Relative_humidity,time_lag)
- mean_daylength<-rollmean(merged_lab_sub2$daylength,time_lag)
- mean_residents<-round(rollmean(merged_lab_sub2$residents, time_lag)) # 1 This because the number of residetns is
-
-
- PC<-rep(as.character(unique(merged_lab_sub$PostCode)),times=length(mean_residents))
- ep<-seq(time_lag,length(mean_residents)+time_lag-1)
-
- merged_lab_time_lag<-data.frame(PC,dates[ep],
- mean_Maximum_air_temperature,
- mean_Minimum_air_temperature,
- mean_Mean_wind_speed,
- cum_Precipitation,
- mean_Precipitation,
- mean_Relative_humidity,
- mean_daylength,
- mean_residents)
-
- return(merged_lab_time_lag)
-}
-
-### This function calculate the total number of cases on the particular day and location
-cases_all<-function(lab_fac){
- lab<-as.character(lab_fac)
- merged.data_sub<-subset(Campylobacter_cases_df,Campylobacter_cases_df$PostCode==lab)
- merged.data_sub2<-merged.data_sub[order(as.Date(merged.data_sub$Date)),]
-
- cases<-(as.data.frame(table(merged.data_sub2$Date)))
-
- PC<-rep(as.character(unique(merged.data_sub$PostCode)),times=length(cases[,1]))
- cases_all<-data.frame(PC,cases)
-
- return(cases_all)
-}
-
-merged_lab_time_lag<-c()
-cases_all_df<-c()
-
-index_PC<-unique(diagnostic_laboratory_df$PostCode)
-for (i in c(1:length(index_PC))){
- merged_lab_time_lag<-rbind(merged_lab_time_lag,lapply(index_PC[i], time_lag_PC)[[1]])
- cases_all_df<-rbind(cases_all_df,lapply(index_PC[i], cases_all)[[1]])
- print(100*i/length(index_PC))
-}
-
-
-
-names(merged_lab_time_lag)[c(1,2)]<-c("PostCode","Date")
-
-
-
-colnames(cases_all_df)<-c("PostCode","Date","Cases_all")
-
-Campylobacter_cases_df<-Campylobacter_cases_df[,-3]
-Campylobacter_cases_df_time_lag<-merge(Campylobacter_cases_df, merged_lab_time_lag,by=c("PostCode","Date"))
-Campylobacter_cases_df_time_lag2<-merge(Campylobacter_cases_df_time_lag, cases_all_df,by=c("PostCode","Date"))
-
-
-Campylobacter_cases_df2<-data.frame(Campylobacter_cases_df_time_lag2$PostCode,
- Campylobacter_cases_df_time_lag2$Date,
- Campylobacter_cases_df_time_lag2$Cases,
- Campylobacter_cases_df_time_lag2$mean_Maximum_air_temperature,
- Campylobacter_cases_df_time_lag2$mean_Minimum_air_temperature,
- Campylobacter_cases_df_time_lag2$mean_Mean_wind_speed,
- Campylobacter_cases_df_time_lag2$cum_Precipitation,
- Campylobacter_cases_df_time_lag2$mean_Precipitation,
- Campylobacter_cases_df_time_lag2$mean_Relative_humidity,
- Campylobacter_cases_df_time_lag2$mean_daylength,
- Campylobacter_cases_df_time_lag2$mean_residents)
-
-
-colnames(Campylobacter_cases_df2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-Campylobacter_cases_df2<-Campylobacter_cases_df2[order(as.Date(Campylobacter_cases_df2$Date)),]
-
-write.table(merged_lab_time_lag,paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",time_lag_char,"_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-write.table(Campylobacter_cases_df2,paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",time_lag_char,"_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
-########################### END time_lag average ######################
-
-
-#PHE_Centre<-Campylobacter_cases_df$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-6
-#For Dorset only
-#merged.data_PHE<-subset(Campylobacter_cases_df,Campylobacter_cases_df$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-#merged.data<-subset(merged.data_PHE,year(merged.data_PHE$Date)>=2000 & year(merged.data_PHE$Date)<2016)
-#merged.data<-subset(Campylobacter_cases_df2,year(Campylobacter_cases_df2$Date)>=1990 & year(Campylobacter_cases_df2$Date)<2015)
-#merged_lab<-subset(diagnostic_laboratory_df,year(diagnostic_laboratory_df$Date)>=1990 & year(diagnostic_laboratory_df$Date)<2015)
-################### time_lag summary
-
diff --git a/PAPER_Pathogen_Linkage_fixed_time_lag.Rout b/PAPER_Pathogen_Linkage_fixed_time_lag.Rout
deleted file mode 100644
index 2b357d540ad0e9d8050fe775c476750b862c73a4..0000000000000000000000000000000000000000
--- a/PAPER_Pathogen_Linkage_fixed_time_lag.Rout
+++ /dev/null
@@ -1,604 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code associates the value of the environmental variables to the Campylobacter cases at the location (diagnostic postcode) and date of occurrence with a chosen time-time_lag
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(xts)
-Loading required package: zoo
-
-Attaching package: ‘zoo’
-
-The following object is masked from ‘package:timeSeries’:
-
- time<-
-
-The following objects are masked from ‘package:base’:
-
- as.Date, as.Date.numeric
-
-
-Attaching package: ‘xts’
-
-The following objects are masked from ‘package:pastecs’:
-
- first, last
-
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
->
->
-> ## Environmental Variable file. Original MEDMI files
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
-> variable<-"min_air_temp"
-> variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> min_temp<-variable_df_3[,-c(1,2)]
-> min_temp<-min_temp[-c(1,2),]
-> names(min_temp) <- NULL
-> Minimum_air_temperature<-unlist(c(min_temp))
->
->
-> variable<-"rain"
-> variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> rain<-variable_df_4[,-c(1,2)]
-> rain<-rain[-c(1,2),]
-> names(rain) <- NULL
-> Precipitation<-unlist(c(rain))
->
-> variable<-"wind_speed"
-> variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> wind<-variable_df_5[,-c(1,2)]
-> wind<-wind[-c(1,2),]
-> names(wind) <- NULL
-> Mean_wind_speed<-unlist(c(wind))
->
-> ######################## include daylength ##################
->
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
->
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
-> PC_df<-data.frame(All_PC,as.Date(dates))
-> colnames(PC_df)<-c("PostCode","Date")
->
-> Post_Codes_df<-merge(PC_df,lat_long_lab,by="PostCode")
->
->
-> daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
->
-> latitude<-Post_Codes_df$lat
-> day_of_the_year<-yday(as.Date(Post_Codes_df$Date))
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Post_Codes_df$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
->
->
-> ######################## Catchment areas #################
->
-> catchment_population_df<-read.csv(paste("../../Data_Base/Catchment_areas/Sum_ByLab_1987_2016.csv",sep=""))
-> colnames(catchment_population_df)<-c("PostCode","col2","col3",
-+ "residents_1987",
-+ "residents_1988",
-+ "residents_1989",
-+ "residents_1990",
-+ "residents_1991",
-+ "residents_1992",
-+ "residents_1993",
-+ "residents_1994",
-+ "residents_1995",
-+ "residents_1996",
-+ "residents_1997",
-+ "residents_1998",
-+ "residents_1999",
-+ "residents_2000",
-+ "residents_2001",
-+ "residents_2002",
-+ "residents_2003",
-+ "residents_2004",
-+ "residents_2005",
-+ "residents_2006",
-+ "residents_2007",
-+ "residents_2008",
-+ "residents_2009",
-+ "residents_2010",
-+ "residents_2011",
-+ "residents_2012",
-+ "residents_2013",
-+ "residents_2014",
-+ "residents_2015",
-+ "residents_2016",
-+ "residents_2011bis")
->
-> diagnostic_laboratory_info_df<-read.csv(paste("../../Data_Base/Catchment_areas/Lab_information.csv",sep=""))
-> diagnostic_laboratory_info_df<-diagnostic_laboratory_info_df[,-1]
->
->
-> ## This create a database for all laboratories postcode, even if there were zero cases
-> diagnostic_laboratory_df2<-data.frame(All_PC,dates,
-+ Maximum_air_temperature,
-+ Minimum_air_temperature,
-+ Mean_wind_speed,
-+ Precipitation,
-+ Relative_humidity,
-+ daylength_df$daylength)
->
-> colnames(diagnostic_laboratory_df2)<-c("PostCode","dates",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Precipitation",
-+ "Relative_humidity",
-+ "daylength")
->
->
->
->
->
-> ###################################
->
-> starting_year_catchment_population_df<-1987
-> end_year_catchment_population_df<-2016
-> numb_years<-length(seq(starting_year_catchment_population_df:end_year_catchment_population_df))
-> sequence_years<-seq(starting_year_catchment_population_df,end_year_catchment_population_df)
->
-> diagnostic_laboratory_df2$year<-year(diagnostic_laboratory_df2$dates)
->
-> catchment_population_df2<-catchment_population_df[,-c(1:3,34)]
->
->
->
-> catchment_population_df3<-data.frame(
-+ rep(catchment_population_df$PostCode,times=numb_years),
-+ rep(sequence_years,each=length(unique(catchment_population_df$PostCode))),
-+ stack(catchment_population_df2)
-+ )
-> colnames(catchment_population_df3)<-c("PostCode","year","residents","ind")
->
->
-> #catchment_population_df3$ind<-unlist(strsplit(as.character(catchment_population_df3$ind), split='_', fixed=TRUE)[seq(1,length(catchment_population_df3$ind))])[2]
->
-> catchment_population_df3$year<-as.factor(catchment_population_df3$year)
-> diagnostic_laboratory_df2$year<-as.factor(diagnostic_laboratory_df2$year)
->
-> diagnostic_laboratory_df<-merge(diagnostic_laboratory_df2, catchment_population_df3,by=c('PostCode','year'))
-> #diagnostic_laboratory_df$Date<-diagnostic_laboratory_df$dates
-> colnames(diagnostic_laboratory_df)[3]<-c("Date")
->
->
-> write.table(diagnostic_laboratory_df,paste("../../Data_Base/OPIE_data_base/Environment_Laboratories_linkage_original_MEDMI.csv",sep=""),sep=',')
->
->
->
->
->
-> ################ Define the time lag here ###############################
->
-> time_lag<-1
-> time_lag_char<-paste(time_lag)
->
-> ## This create a database for all postocode where there was at least one case
-> Campylobacter_cases<-read.csv("../../Data_Base/Cases/Campylobacter_Simulated_Data_final_UK.csv")
-> Campylobacter_cases<-Campylobacter_cases[,-1]
-> colnames(Campylobacter_cases)<-c("Cases","Adjusted","Spec_Date", "PostCode","Date","Simulation")
->
-> Campylobacter_cases$PostCode<-stri_replace_all_fixed(Campylobacter_cases$PostCode, " ", "")
-> Campylobacter_cases$Date<-as.Date((as.character(Campylobacter_cases$Date)))
-> Campylobacter_cases$year<-as.factor((as.character(year(Campylobacter_cases$Date))))
-> Campylobacter_cases$PostCode<-as.factor(Campylobacter_cases$PostCode)
->
-> Campylobacter_cases_df<-merge(Campylobacter_cases, catchment_population_df3,by=c("PostCode","year"))
->
->
-> ########################### time_lag average ######################
->
-> ### This function calculate the average values of the environmental variables over a certain time-time_lag
-> time_lag_PC<-function(lab_fac){
-+ lab<-as.character(lab_fac)
-+ merged_lab_sub<-subset(diagnostic_laboratory_df,diagnostic_laboratory_df$PostCode==lab)
-+ merged_lab_sub2<-merged_lab_sub[order(as.Date(merged_lab_sub$Date)),]
-+
-+
-+ mean_Maximum_air_temperature<-rollmean(merged_lab_sub2$Maximum_air_temperature,time_lag)
-+ mean_Minimum_air_temperature<-rollmean(merged_lab_sub2$Minimum_air_temperature,time_lag)
-+ mean_Mean_wind_speed<-rollmean(merged_lab_sub2$Mean_wind_speed,time_lag)
-+ cum_Precipitation<-rollsum(merged_lab_sub2$Precipitation,time_lag)
-+ mean_Precipitation<-rollmean(merged_lab_sub2$Precipitation,time_lag)
-+ mean_Relative_humidity<-rollmean(merged_lab_sub2$Relative_humidity,time_lag)
-+ mean_daylength<-rollmean(merged_lab_sub2$daylength,time_lag)
-+ mean_residents<-round(rollmean(merged_lab_sub2$residents, time_lag)) # 1 This because the number of residetns is
-+
-+
-+ PC<-rep(as.character(unique(merged_lab_sub$PostCode)),times=length(mean_residents))
-+ ep<-seq(time_lag,length(mean_residents)+time_lag-1)
-+
-+ merged_lab_time_lag<-data.frame(PC,dates[ep],
-+ mean_Maximum_air_temperature,
-+ mean_Minimum_air_temperature,
-+ mean_Mean_wind_speed,
-+ cum_Precipitation,
-+ mean_Precipitation,
-+ mean_Relative_humidity,
-+ mean_daylength,
-+ mean_residents)
-+
-+ return(merged_lab_time_lag)
-+ }
->
-> ### This function calculate the total number of cases on the particular day and location
-> cases_all<-function(lab_fac){
-+ lab<-as.character(lab_fac)
-+ merged.data_sub<-subset(Campylobacter_cases_df,Campylobacter_cases_df$PostCode==lab)
-+ merged.data_sub2<-merged.data_sub[order(as.Date(merged.data_sub$Date)),]
-+
-+ cases<-(as.data.frame(table(merged.data_sub2$Date)))
-+
-+ PC<-rep(as.character(unique(merged.data_sub$PostCode)),times=length(cases[,1]))
-+ cases_all<-data.frame(PC,cases)
-+
-+ return(cases_all)
-+ }
->
-> merged_lab_time_lag<-c()
-> cases_all_df<-c()
->
-> index_PC<-unique(diagnostic_laboratory_df$PostCode)
-> for (i in c(1:length(index_PC))){
-+ merged_lab_time_lag<-rbind(merged_lab_time_lag,lapply(index_PC[i], time_lag_PC)[[1]])
-+ cases_all_df<-rbind(cases_all_df,lapply(index_PC[i], cases_all)[[1]])
-+ print(100*i/length(index_PC))
-+ }
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->
->
->
-> names(merged_lab_time_lag)[c(1,2)]<-c("PostCode","Date")
->
->
->
-> colnames(cases_all_df)<-c("PostCode","Date","Cases_all")
->
-> Campylobacter_cases_df<-Campylobacter_cases_df[,-3]
-> Campylobacter_cases_df_time_lag<-merge(Campylobacter_cases_df, merged_lab_time_lag,by=c("PostCode","Date"))
-> Campylobacter_cases_df_time_lag2<-merge(Campylobacter_cases_df_time_lag, cases_all_df,by=c("PostCode","Date"))
->
->
-> Campylobacter_cases_df2<-data.frame(Campylobacter_cases_df_time_lag2$PostCode,
-+ Campylobacter_cases_df_time_lag2$Date,
-+ Campylobacter_cases_df_time_lag2$Cases,
-+ Campylobacter_cases_df_time_lag2$mean_Maximum_air_temperature,
-+ Campylobacter_cases_df_time_lag2$mean_Minimum_air_temperature,
-+ Campylobacter_cases_df_time_lag2$mean_Mean_wind_speed,
-+ Campylobacter_cases_df_time_lag2$cum_Precipitation,
-+ Campylobacter_cases_df_time_lag2$mean_Precipitation,
-+ Campylobacter_cases_df_time_lag2$mean_Relative_humidity,
-+ Campylobacter_cases_df_time_lag2$mean_daylength,
-+ Campylobacter_cases_df_time_lag2$mean_residents)
->
->
-> colnames(Campylobacter_cases_df2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> Campylobacter_cases_df2<-Campylobacter_cases_df2[order(as.Date(Campylobacter_cases_df2$Date)),]
->
-> write.table(merged_lab_time_lag,paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",time_lag_char,"_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
-> write.table(Campylobacter_cases_df2,paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",time_lag_char,"_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
->
-> ########################### END time_lag average ######################
->
->
-> #PHE_Centre<-Campylobacter_cases_df$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-6
-> #For Dorset only
-> #merged.data_PHE<-subset(Campylobacter_cases_df,Campylobacter_cases_df$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> #merged.data<-subset(merged.data_PHE,year(merged.data_PHE$Date)>=2000 & year(merged.data_PHE$Date)<2016)
-> #merged.data<-subset(Campylobacter_cases_df2,year(Campylobacter_cases_df2$Date)>=1990 & year(Campylobacter_cases_df2$Date)<2015)
-> #merged_lab<-subset(diagnostic_laboratory_df,year(diagnostic_laboratory_df$Date)>=1990 & year(diagnostic_laboratory_df$Date)<2015)
-> ################### time_lag summary
->
->
-> proc.time()
- user system elapsed
-308.829 6.083 316.116
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI.Rout b/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI.Rout
deleted file mode 100644
index ed983fc33d30c79cbebb397b25700b9641d3d467..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI.Rout
+++ /dev/null
@@ -1,546 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-14
-> width_char<-paste(width)
->
->
-> ## Environmental Variable file. Original MEDMI files
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
-> variable<-"min_air_temp"
-> variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> min_temp<-variable_df_3[,-c(1,2)]
-> min_temp<-min_temp[-c(1,2),]
-> names(min_temp) <- NULL
-> Minimum_air_temperature<-unlist(c(min_temp))
->
->
-> variable<-"rain"
-> variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> rain<-variable_df_4[,-c(1,2)]
-> rain<-rain[-c(1,2),]
-> names(rain) <- NULL
-> Precipitation<-unlist(c(rain))
->
-> variable<-"wind_speed"
-> variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> wind<-variable_df_5[,-c(1,2)]
-> wind<-wind[-c(1,2),]
-> names(wind) <- NULL
-> Mean_wind_speed<-unlist(c(wind))
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2010)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Minimum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"min_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
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-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
-> #var_x_loc_df<-data.frame( matrix(ncol = 6, nrow = 500))
-> #colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
->
-> #norm<-1#-sum(na.omit(var_x_loc_df$incidence))/100
-> #var_x_loc_df$incidence<-var_x_loc_df$incidence/norm
-> #var_x_loc_df<-na.omit(var_x_loc_df)
->
->
->
->
-> ###################
->
->
->
-> ################### Divide the domains of the variables in bins
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<2010)
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+ #variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
-+ #variable_df_3_dis<-variable_df_3_dis[order(variable_df_3_dis$dates),]
-+ #variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
-+ #variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
-+ #variable_df_5_dis$dates<-as.Date(as.character((variable_df_5_dis$dates)))
-+ #variable_df_5_dis<-variable_df_5_dis[order(variable_df_5_dis$dates),]
-+
-+ #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
-+ #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Maximum_air_temperature")
-+ #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-+
-+ #variable_df_3_dis$dates<-as.factor(variable_df_3_dis$dates)
-+ #variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
-+ #variable_df_5_dis$dates<-as.factor(variable_df_5_dis$dates)
-+
-+ #variable_df_dis_int<-merge(variable_df_3_dis,variable_df_4_dis, by=c("dates") )
-+
-+
-+ #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
-+ #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Maximum_air_temperature")
-+ #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-Error in `$<-.data.frame`(`*tmp*`, "Maximum_air_temperature", value = numeric(0)) :
- replacement has 0 rows, data has 781
-Calls: $<- -> $<-.data.frame
-Execution halted
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI2.R b/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI2.R
deleted file mode 100644
index 1496656bdad3967025f1fc3e420685198f0b59d3..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI2.R
+++ /dev/null
@@ -1,306 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# it works for
-#variable_x<-"Minimum_air_temperature"
-#variable_y<-"Relative_humidity"
-#variable<-"daylength"
-## Change breaks_max etc. for different varialbes
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-
-
-## Environmental Variable file. Original MEDMI files
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-variable<-"min_air_temp"
-variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-min_temp<-variable_df_3[,-c(1,2)]
-min_temp<-min_temp[-c(1,2),]
-names(min_temp) <- NULL
-Minimum_air_temperature<-unlist(c(min_temp))
-
-
-variable<-"rain"
-variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-rain<-variable_df_4[,-c(1,2)]
-rain<-rain[-c(1,2),]
-names(rain) <- NULL
-Precipitation<-unlist(c(rain))
-
-variable<-"wind_speed"
-variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-wind<-variable_df_5[,-c(1,2)]
-wind<-wind[-c(1,2),]
-names(wind) <- NULL
-Mean_wind_speed<-unlist(c(wind))
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2010)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Minimum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"min_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-#var_x_loc_df<-data.frame( matrix(ncol = 6, nrow = 500))
-#colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
-
-#norm<-1#-sum(na.omit(var_x_loc_df$incidence))/100
-#var_x_loc_df$incidence<-var_x_loc_df$incidence/norm
-#var_x_loc_df<-na.omit(var_x_loc_df)
-
-
-
-
-###################
-
-
-
-################### Divide the domains of the variables in bins
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Minimum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<2010)
-
- x<-variable_df$Relative_humidity
- y<-variable_df$Minimum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Minimum_air_temperature<-(breaks_min_temp[findInterval(y, breaks_min_temp)])###Change here for different varialbes
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Minimum_air_temperature<-(breaks_min_temp[findInterval(var_x_loc_df$Minimum_air_temperature, breaks_min_temp)])##floor(var_x_loc_df$breaks)
-
-
- #variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
- #variable_df_3_dis<-variable_df_3_dis[order(variable_df_3_dis$dates),]
- #variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
- #variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
- #variable_df_5_dis$dates<-as.Date(as.character((variable_df_5_dis$dates)))
- #variable_df_5_dis<-variable_df_5_dis[order(variable_df_5_dis$dates),]
-
- #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
- #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Minimum_air_temperature")
- #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-
- #variable_df_3_dis$dates<-as.factor(variable_df_3_dis$dates)
- #variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
- #variable_df_5_dis$dates<-as.factor(variable_df_5_dis$dates)
-
- #variable_df_dis_int<-merge(variable_df_3_dis,variable_df_4_dis, by=c("dates") )
-
-
- #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
- #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Minimum_air_temperature")
- #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-
- ###############
- # variable_df_dis_int$Minimum_air_temperature<-floor(variable_df_dis_int$Minimum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Minimum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Minimum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
-#time_series<-read.csv(paste("../../Data_Base/OPIE_data_base/Time_series_",variable,"_",variable_x,".csv",sep=""))
-#time_series<-time_series[,-1]
-#colnames(time_series)<-c("Date","Cases","Lambda","Lab")
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI2.Rout b/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI2.Rout
deleted file mode 100644
index 822c05e6593d6cbf359a50096901ce2cd8669c7e..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI2.Rout
+++ /dev/null
@@ -1,551 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # it works for
-> #variable_x<-"Minimum_air_temperature"
-> #variable_y<-"Relative_humidity"
-> #variable<-"daylength"
-> ## Change breaks_max etc. for different varialbes
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
->
->
-> ## Environmental Variable file. Original MEDMI files
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
-> variable<-"min_air_temp"
-> variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> min_temp<-variable_df_3[,-c(1,2)]
-> min_temp<-min_temp[-c(1,2),]
-> names(min_temp) <- NULL
-> Minimum_air_temperature<-unlist(c(min_temp))
->
->
-> variable<-"rain"
-> variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> rain<-variable_df_4[,-c(1,2)]
-> rain<-rain[-c(1,2),]
-> names(rain) <- NULL
-> Precipitation<-unlist(c(rain))
->
-> variable<-"wind_speed"
-> variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> wind<-variable_df_5[,-c(1,2)]
-> wind<-wind[-c(1,2),]
-> names(wind) <- NULL
-> Mean_wind_speed<-unlist(c(wind))
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2010)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Minimum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"min_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
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-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
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-[1] "98.0582524271845" "WR13AS"
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-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
-> #var_x_loc_df<-data.frame( matrix(ncol = 6, nrow = 500))
-> #colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
->
-> #norm<-1#-sum(na.omit(var_x_loc_df$incidence))/100
-> #var_x_loc_df$incidence<-var_x_loc_df$incidence/norm
-> #var_x_loc_df<-na.omit(var_x_loc_df)
->
->
->
->
-> ###################
->
->
->
-> ################### Divide the domains of the variables in bins
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(ceiling(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Minimum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<2010)
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Minimum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Minimum_air_temperature<-(breaks_min_temp[findInterval(y, breaks_min_temp)])###Change here for different varialbes
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Minimum_air_temperature<-(breaks_min_temp[findInterval(var_x_loc_df$Minimum_air_temperature, breaks_min_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+ #variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
-+ #variable_df_3_dis<-variable_df_3_dis[order(variable_df_3_dis$dates),]
-+ #variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
-+ #variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
-+ #variable_df_5_dis$dates<-as.Date(as.character((variable_df_5_dis$dates)))
-+ #variable_df_5_dis<-variable_df_5_dis[order(variable_df_5_dis$dates),]
-+
-+ #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
-+ #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Minimum_air_temperature")
-+ #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-+
-+ #variable_df_3_dis$dates<-as.factor(variable_df_3_dis$dates)
-+ #variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
-+ #variable_df_5_dis$dates<-as.factor(variable_df_5_dis$dates)
-+
-+ #variable_df_dis_int<-merge(variable_df_3_dis,variable_df_4_dis, by=c("dates") )
-+
-+
-+ #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
-+ #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Minimum_air_temperature")
-+ #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-+
-+ ###############
-+ # variable_df_dis_int$Minimum_air_temperature<-floor(variable_df_dis_int$Minimum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Minimum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Minimum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-Error in `$<-.data.frame`(`*tmp*`, "Minimum_air_temperature", value = c(1, :
- replacement has 565 rows, data has 568
-Calls: $<- -> $<-.data.frame
-Execution halted
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_hum_light.R b/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_hum_light.R
deleted file mode 100644
index 88562a0eaeddd71b8d2e0601ef6989cace61a5af..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_hum_light.R
+++ /dev/null
@@ -1,280 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-
-
-## Environmental Variable file. Original MEDMI files
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-variable<-"min_air_temp"
-variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-min_temp<-variable_df_3[,-c(1,2)]
-min_temp<-min_temp[-c(1,2),]
-names(min_temp) <- NULL
-Minimum_air_temperature<-unlist(c(min_temp))
-
-
-variable<-"rain"
-variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-rain<-variable_df_4[,-c(1,2)]
-rain<-rain[-c(1,2),]
-names(rain) <- NULL
-Precipitation<-unlist(c(rain))
-
-variable<-"wind_speed"
-variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-wind<-variable_df_5[,-c(1,2)]
-wind<-wind[-c(1,2),]
-names(wind) <- NULL
-Mean_wind_speed<-unlist(c(wind))
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Minimum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"min_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-#var_x_loc_df<-data.frame( matrix(ncol = 6, nrow = 500))
-#colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
-
-#norm<-1#-sum(na.omit(var_x_loc_df$incidence))/100
-#var_x_loc_df$incidence<-var_x_loc_df$incidence/norm
-#var_x_loc_df<-na.omit(var_x_loc_df)
-
-
-
-
-###################
-
-
-
-################### Divide the domains of the variables in bins
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-
- x<-variable_df$Relative_humidity
- y<-variable_df$Maximum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
-#time_series<-read.csv(paste("../../Data_Base/OPIE_data_base/Time_series_",variable,"_",variable_x,".csv",sep=""))
-#time_series<-time_series[,-1]
-#colnames(time_series)<-c("Date","Cases","Lambda","Lab")
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_rain_light.R b/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_rain_light.R
deleted file mode 100644
index 2dfa7eacebf6f87bc501a1a8c080341c43639665..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_rain_light.R
+++ /dev/null
@@ -1,283 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-
-
-## Environmental Variable file. Original MEDMI files
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-variable<-"min_air_temp"
-variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-min_temp<-variable_df_3[,-c(1,2)]
-min_temp<-min_temp[-c(1,2),]
-names(min_temp) <- NULL
-Minimum_air_temperature<-unlist(c(min_temp))
-
-
-variable<-"rain"
-variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-rain<-variable_df_4[,-c(1,2)]
-rain<-rain[-c(1,2),]
-names(rain) <- NULL
-Precipitation<-unlist(c(rain))
-
-variable<-"wind_speed"
-variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-wind<-variable_df_5[,-c(1,2)]
-wind<-wind[-c(1,2),]
-names(wind) <- NULL
-Mean_wind_speed<-unlist(c(wind))
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Mean_Precipitation"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"rain"
-variable_2<-"light"
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-#var_x_loc_df<-data.frame( matrix(ncol = 6, nrow = 500))
-#colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
-
-#norm<-1#-sum(na.omit(var_x_loc_df$incidence))/100
-#var_x_loc_df$incidence<-var_x_loc_df$incidence/norm
-#var_x_loc_df<-na.omit(var_x_loc_df)
-
-
-
-
-###################
-
-
-
-################### Divide the domains of the variables in bins
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-1
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),-1), max(na.omit(Env_laboratory$Mean_Precipitation))+1,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Mean_Precipitation,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-
- x<-variable_df$Mean_Precipitation
- y<-variable_df$Maximum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Mean_Precipitation<-(breaks_rain[findInterval(x, breaks_rain)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- #breaks_rain2<-floor(breaks_rain)
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Mean_Precipitation<-(breaks_rain[findInterval(var_x_loc_df$Mean_Precipitation, breaks_rain)])##floor(var_x_loc_df$Mean_Precipitation)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Mean_Precipitation","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Mean_Precipitation","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
-#time_series<-read.csv(paste("../../Data_Base/OPIE_data_base/Time_series_",variable,"_",variable_x,".csv",sep=""))
-#time_series<-time_series[,-1]
-#colnames(time_series)<-c("Date","Cases","Lambda","Lab")
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_rain_light.Rout b/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_rain_light.Rout
deleted file mode 100644
index 048636d5c5ab7df2e72b24e9b0a7259d7e583ca0..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_max_rain_light.Rout
+++ /dev/null
@@ -1,525 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
->
->
-> ## Environmental Variable file. Original MEDMI files
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
-> variable<-"min_air_temp"
-> variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> min_temp<-variable_df_3[,-c(1,2)]
-> min_temp<-min_temp[-c(1,2),]
-> names(min_temp) <- NULL
-> Minimum_air_temperature<-unlist(c(min_temp))
->
->
-> variable<-"rain"
-> variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> rain<-variable_df_4[,-c(1,2)]
-> rain<-rain[-c(1,2),]
-> names(rain) <- NULL
-> Precipitation<-unlist(c(rain))
->
-> variable<-"wind_speed"
-> variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> wind<-variable_df_5[,-c(1,2)]
-> wind<-wind[-c(1,2),]
-> names(wind) <- NULL
-> Mean_wind_speed<-unlist(c(wind))
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Minimum_air_temperature"
-> variable_y<-"Mean_Precipitation"
-> variable<-"daylength"
->
-> variable_x2<-"min_air_temp"
-> variable_y2<-"rain"
-> variable_2<-"light"
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
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-[1] "44.1747572815534" "LE15WW"
-[1] "44.6601941747573" "LL137TP"
-[1] "45.1456310679612" "LL185UJ"
-[1] "45.6310679611651" "LL572TP"
-[1] "46.1165048543689" "LN25QY"
-[1] "46.6019417475728" "LS157TR"
-[1] "47.0873786407767" "LS29JT"
-[1] "47.5728155339806" "LS97TF"
-[1] "48.0582524271845" "LU40EP"
-[1] "48.5436893203884" "M208LR"
-[1] "49.0291262135922" "M415SL"
-[1] "49.5145631067961" "M68WH"
-[1] "50" "M85RB"
-[1] "50.4854368932039" "M97AA"
-[1] "50.9708737864078" "ME169QQ"
-[1] "51.4563106796116" "ME207NJ"
-[1] "51.9417475728155" "ME75NY"
-[1] "52.4271844660194" "MK429DJ"
-[1] "52.9126213592233" "MK65LD"
-[1] "53.3980582524272" "N181QX"
-[1] "53.8834951456311" "N195NF"
-[1] "54.3689320388349" "NE298NH"
-[1] "54.8543689320388" "NE340PL"
-[1] "55.3398058252427" "NE46BE"
-[1] "55.8252427184466" "NE77DN"
-[1] "56.3106796116505" "NE96SX"
-[1] "56.7961165048544" "NG174JL"
-[1] "57.2815533980583" "NG318DG"
-[1] "57.7669902912621" "NG72UH"
-[1] "58.252427184466" "NN15BD"
-[1] "58.7378640776699" "NN168UZ"
-[1] "59.2233009708738" "NP77EG"
-[1] "59.7087378640777" "NP92UB"
-[1] "60.1941747572816" "NR23TX"
-[1] "60.6796116504854" "NR316LA"
-[1] "61.1650485436893" "NW107NS"
-[1] "61.6504854368932" "NW32QG"
-[1] "62.1359223300971" "NW95HT"
-[1] "62.621359223301" "OL129QB"
-[1] "63.1067961165049" "OL12JH"
-[1] "63.5922330097087" "OL69RW"
-[1] "64.0776699029126" "OX39DU"
-[1] "64.5631067961165" "PE188NT"
-[1] "65.0485436893204" "PE219QS"
-[1] "65.5339805825243" "PE304ET"
-[1] "66.0194174757282" "PE36DA"
-[1] "66.504854368932" "PL68DH"
-[1] "66.9902912621359" "PO194SE"
-[1] "67.4757281553398" "PO305TG"
-[1] "67.9611650485437" "PO36AQ"
-[1] "68.4466019417476" "PR29HT"
-[1] "68.9320388349515" "PR86PN"
-[1] "69.4174757281553" "RG15AN"
-[1] "69.9029126213592" "RG249NA"
-[1] "70.3883495145631" "RH117DH"
-[1] "70.873786407767" "RM30BE"
-[1] "71.3592233009709" "RM70AG"
-[1] "71.8446601941748" "S445BL"
-[1] "72.3300970873786" "S57BQ"
-[1] "72.8155339805825" "S602UD"
-[1] "73.3009708737864" "S752EP"
-[1] "73.7864077669903" "S810BD"
-[1] "74.2718446601942" "SA28QA"
-[1] "74.7572815533981" "SA312AF"
-[1] "75.2427184466019" "SA612PZ"
-[1] "75.7281553398058" "SE136LH"
-[1] "76.2135922330097" "SE17EH"
-[1] "76.6990291262136" "SE184QH"
-[1] "77.1844660194175" "SE59RS"
-[1] "77.6699029126214" "SG14AB"
-[1] "78.1553398058252" "SK103BL"
-[1] "78.6407766990291" "SK27JE"
-[1] "79.126213592233" "SL24HL"
-[1] "79.6116504854369" "SM51AA"
-[1] "80.0970873786408" "SN36BB"
-[1] "80.5825242718447" "SO166YD"
-[1] "81.0679611650485" "SO226ZB"
-[1] "81.5533980582524" "SP28BJ"
-[1] "82.0388349514563" "SR47TP"
-[1] "82.5242718446602" "SS00RY"
-[1] "83.0097087378641" "SS165NL"
-[1] "83.495145631068" "ST163SA"
-[1] "83.9805825242718" "ST47PX"
-[1] "84.4660194174757" "SW109NH"
-[1] "84.9514563106796" "SW170QT"
-[1] "85.4368932038835" "SW36JJ"
-[1] "85.9223300970874" "SW36NP"
-[1] "86.4077669902913" "SY231ER"
-[1] "86.8932038834951" "SY38XQ"
-[1] "87.378640776699" "TA15DB"
-[1] "87.8640776699029" "TN240LZ"
-[1] "88.3495145631068" "TQ27AA"
-[1] "88.8349514563107" "TR13LQ"
-[1] "89.3203883495146" "TS198PE"
-[1] "89.8058252427184" "TS249AH"
-[1] "90.2912621359223" "TS43BW"
-[1] "90.7766990291262" "TW76AF"
-[1] "91.2621359223301" "UB13HW"
-[1] "91.747572815534" "UB83NN"
-[1] "92.2330097087379" "UB96JH"
-[1] "92.7184466019417" "W120NN"
-[1] "93.2038834951456" "W21NY"
-[1] "93.6893203883495" "W68RF"
-[1] "94.1747572815534" "WA51QG"
-[1] "94.6601941747573" "WC1E6DB"
-[1] "95.1456310679612" "WC1N3JH"
-[1] "95.631067961165" "WD18HB"
-[1] "96.1165048543689" "WF134HS"
-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
-> #var_x_loc_df<-data.frame( matrix(ncol = 6, nrow = 500))
-> #colnames(var_x_loc_df)<-c(variable,variable_y,variable_x,"counts","residents","residents_tot")
->
-> #norm<-1#-sum(na.omit(var_x_loc_df$incidence))/100
-> #var_x_loc_df$incidence<-var_x_loc_df$incidence/norm
-> #var_x_loc_df<-na.omit(var_x_loc_df)
->
->
->
->
-> ###################
->
->
->
-> ################### Divide the domains of the variables in bins
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Mean_Precipitation,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+
-+ x<-variable_df$Mean_Precipitation
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Mean_Precipitation<-(breaks_rain[findInterval(x, breaks_rain)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Mean_Precipitation<-(breaks_rain[findInterval(var_x_loc_df$Mean_Precipitation, breaks_rain)])##floor(var_x_loc_df$Mean_Precipitation)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Mean_Precipitation","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Mean_Precipitation","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-Error in `$<-.data.frame`(`*tmp*`, "Mean_Precipitation", value = c(0.1665106777, :
- replacement has 670 rows, data has 779
-Calls: $<- -> $<-.data.frame
-Execution halted
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_min_hum_light.R b/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_min_hum_light.R
deleted file mode 100644
index 01e0bc1c86977d07fd4f2e441d67f855698f53e0..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_fixed_lag_original_MEDMI_min_hum_light.R
+++ /dev/null
@@ -1,297 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-
-
-## Environmental Variable file. Original MEDMI files
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-variable<-"min_air_temp"
-variable_df_3<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-min_temp<-variable_df_3[,-c(1,2)]
-min_temp<-min_temp[-c(1,2),]
-names(min_temp) <- NULL
-Minimum_air_temperature<-unlist(c(min_temp))
-
-
-variable<-"rain"
-variable_df_4<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-rain<-variable_df_4[,-c(1,2)]
-rain<-rain[-c(1,2),]
-names(rain) <- NULL
-Precipitation<-unlist(c(rain))
-
-variable<-"wind_speed"
-variable_df_5<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-wind<-variable_df_5[,-c(1,2)]
-wind<-wind[-c(1,2),]
-names(wind) <- NULL
-Mean_wind_speed<-unlist(c(wind))
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Minimum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"min_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-### THis to avoid spurios cases with only one exceptional Campylobacter case
-w_spurious<-which(var_x_loc_df_all$counts==1)
-var_x_loc_df_all[w_spurious,]<-NA
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-
-###################
-
-
-
-################### Divide the domains of the variables in bins
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Minimum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-
- x<-variable_df$Relative_humidity
- y<-variable_df$Minimum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Minimum_air_temperature<-(breaks_min_temp[findInterval(y, breaks_min_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Minimum_air_temperature<-(breaks_min_temp[findInterval(var_x_loc_df$Minimum_air_temperature, breaks_min_temp)])##floor(var_x_loc_df$breaks)
-
-
- #variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
- #variable_df_3_dis<-variable_df_3_dis[order(variable_df_3_dis$dates),]
- #variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
- #variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
- #variable_df_5_dis$dates<-as.Date(as.character((variable_df_5_dis$dates)))
- #variable_df_5_dis<-variable_df_5_dis[order(variable_df_5_dis$dates),]
-
- #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
- #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Minimum_air_temperature")
- #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-
- #variable_df_3_dis$dates<-as.factor(variable_df_3_dis$dates)
- #variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
- #variable_df_5_dis$dates<-as.factor(variable_df_5_dis$dates)
-
- #variable_df_dis_int<-merge(variable_df_3_dis,variable_df_4_dis, by=c("dates") )
-
-
- #variable_df_3_dis<-merge(variable_df_x_dis,var_x_loc_df, by="Relative_humidity")
- #variable_df_4_dis<-merge(variable_df_y_dis,var_x_loc_df, by="Minimum_air_temperature")
- #variable_df_5_dis<-merge(variable_df_z_dis,var_x_loc_df, by="daylength")
-
- ###############
- # variable_df_dis_int$Minimum_air_temperature<-floor(variable_df_dis_int$Minimum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Minimum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Minimum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
-#time_series<-read.csv(paste("../../Data_Base/OPIE_data_base/Time_series_",variable,"_",variable_x,".csv",sep=""))
-#time_series<-time_series[,-1]
-#colnames(time_series)<-c("Date","Cases","Lambda","Lab")
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays.R b/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays.R
deleted file mode 100644
index 76bb1e1bdc41c31435b91b5130d55187c1171ca7..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays.R
+++ /dev/null
@@ -1,335 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# this to calculate delay
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-7
-width_char<-paste(width)
-n_seas<-1
-
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable_z<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_z2<-"light"
-
-
-
-## Varaible file
-
-## Varaible file
-
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.csv",sep = "")
-coefficients_temp<-read.csv(file_name)
-
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_hum_week.csv",sep = "")
-coefficients_hum<-read.csv(file_name)
-
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_week.csv",sep = "")
-coefficients_light<-read.csv(file_name)
-
-
-#colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
-
-dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2010-01-31"), by = "day")
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
-
-
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-#wt<-c(0)
-#for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-#wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-#print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-#}
-
-
-#Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-Env_laboratory_PHE<-Env_laboratory
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-################### Include daylength
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-#Env_Lyme_data_int2<-merge(Env_Lyme_data_all2,lat_long_lab,by="PostCode")
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Env_laboratory_int2$lat
-day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-#var_list<-list(lat,day_year)
-#lapply(var_list,daylength)
-#lapply(Env_laboratory,daylength, Env_laboratory$lat)
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#daylength_df$lat<-as.factor(daylength_df$lat)
-#daylength_df$Date<-as.factor(daylength_df$Date)
-#Env_laboratory_int2$lat<-as.factor(Env_laboratory_int2$lat)
-#Env_laboratory_int2$Date<-as.factor(Env_laboratory_int2$Date)
-#Env_laboratory<-merge(Env_laboratory_int2,daylength_df,by=c("lat","Date"))
-
-Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
-
-### repeat for the data only ####
-
-#latitude<-Env_Lyme_data_int2$lat
-#day_of_the_year<-yday(as.Date(Env_Lyme_data_int2$Date))
-
-#daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-#daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Lyme_data_int2$Date),daylength_int1)
-#colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#Env_Lyme_data<-data.frame(Env_Lyme_data_int2,daylength_df)
-
-
-########################################
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_z2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable_z,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-###################
-
-delta_light<-1
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-breaks_hum<-floor(seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum)) #i
-breaks_min_temp<-floor(seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp))
-breaks_max_temp<-floor(seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp))
-breaks_light<-floor(seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light))
-
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-
-
-
-
-time_series<-c()
-
-for (i in c(1: length(All_PC_s))){
- #for (i in c(1: 15)){
-
-
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- if (length(variable_df[,1])!=0){
-
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<2010)
-
-
-
- ##########include time here and then average according to coefficients up to the time weighted.mean
- x<-c()
- y<-c()
- z<-c()
-
- for (k in c(min(year(variable_df$Date)):max(year(variable_df$Date))) ){
-
- var_year<-subset(variable_df,year(variable_df$Date)==k )
- xx<-c()
- yy<-c()
- zz<-c()
-
- wt<-0
- for (n_coeff in (1:16)){
- wt<-wt+unlist(coefficients_temp)[[n_coeff]]*week(var_year$Date)^(n_coeff-1)
- }
- wt_temp<-wt/sum(wt)
-
- wt<-0
- for (n_coeff in (1:16)){
- wt<-wt+unlist(coefficients_hum)[[n_coeff]]*week(var_year$Date)^(n_coeff-1)
- }
- wt_hum<-wt/sum(wt)
-
- wt<-0
- for (n_coeff in (1:16)){
- wt<-wt+unlist(coefficients_light)[[n_coeff]]*week(var_year$Date)^(n_coeff-1)
-
- }
- wt_light<-wt/sum(wt)
-
-
-
- end_time<-yday(var_year$Date)
- for (k2 in c(1:max(end_time)) ){
- yy[k2] <- weighted.mean( var_year$Maximum_air_temperature[1:k2], wt[1:k2])
- xx[k2] <- weighted.mean( var_year$Relative_humidity[1:k2], wt[1:k2])
- zz[k2] <- weighted.mean( var_year$daylength[1:k2], wt[1:k2])
- }
-
- x<-c(x,xx)
- y<-c(y,yy)
- z<-c(z,zz)
- }
-
- # seq(min(as.Date(variable_df$Date)), max(as.Date(variable_df$Date)), by = "day")
-
- # breaks_hum[findInterval(x, breaks_hum)]
- # breaks_Maximum_air_temperature[findInterval(y, breaks_Maximum_air_temperature)]
-
-
-
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])
-
- #variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$max_temp)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,17:19,21:25)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable_z,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
-
-
-
-
- #variable_df0<-merge(variable_df_1_dis, variable_df_2_dis, by="dates")
- #variable_df1<-merge(variable_df0, variable_df_3_dis, by="dates")
- #variable_df_dis<-merge(variable_df1,var_x_loc_df, by=c("Maximum_air_temperature","Relative_humidity","daylength"))
-
-
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-
-
-}
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_Simulated_for_rec_multiple_delays.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays.Rout b/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays.Rout
deleted file mode 100644
index 4439d70a08e5e157b743e14f14c8047f3595b11f..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays.Rout
+++ /dev/null
@@ -1,592 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # this to calculate delay
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-7
-> width_char<-paste(width)
-> n_seas<-1
->
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable_z<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_z2<-"light"
->
->
->
-> ## Varaible file
->
-> ## Varaible file
->
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.csv",sep = "")
-> coefficients_temp<-read.csv(file_name)
->
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_hum_week.csv",sep = "")
-> coefficients_hum<-read.csv(file_name)
->
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_week.csv",sep = "")
-> coefficients_light<-read.csv(file_name)
->
->
-> #colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
->
-> dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2010-01-31"), by = "day")
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
->
->
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-> #wt<-c(0)
-> #for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-> #wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-> #print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-> #}
->
->
-> #Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> Env_laboratory_PHE<-Env_laboratory
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
-> ################### Include daylength
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
-> Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-> #Env_Lyme_data_int2<-merge(Env_Lyme_data_all2,lat_long_lab,by="PostCode")
->
-> daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
->
-> latitude<-Env_laboratory_int2$lat
-> day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-> #var_list<-list(lat,day_year)
-> #lapply(var_list,daylength)
-> #lapply(Env_laboratory,daylength, Env_laboratory$lat)
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
-> #daylength_df$lat<-as.factor(daylength_df$lat)
-> #daylength_df$Date<-as.factor(daylength_df$Date)
-> #Env_laboratory_int2$lat<-as.factor(Env_laboratory_int2$lat)
-> #Env_laboratory_int2$Date<-as.factor(Env_laboratory_int2$Date)
-> #Env_laboratory<-merge(Env_laboratory_int2,daylength_df,by=c("lat","Date"))
->
-> Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
->
-> ### repeat for the data only ####
->
-> #latitude<-Env_Lyme_data_int2$lat
-> #day_of_the_year<-yday(as.Date(Env_Lyme_data_int2$Date))
->
-> #daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> #daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Lyme_data_int2$Date),daylength_int1)
-> #colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
-> #Env_Lyme_data<-data.frame(Env_Lyme_data_int2,daylength_df)
->
->
-> ########################################
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_z2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable_z,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
-> ###################
->
-> delta_light<-1
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> breaks_hum<-floor(seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum)) #i
-> breaks_min_temp<-floor(seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp))
-> breaks_max_temp<-floor(seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp))
-> breaks_light<-floor(seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light))
->
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
->
->
->
->
-> time_series<-c()
->
-> for (i in c(1: length(All_PC_s))){
-+ #for (i in c(1: 15)){
-+
-+
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ if (length(variable_df[,1])!=0){
-+
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<2010)
-+
-+
-+
-+ ##########include time here and then average according to coefficients up to the time weighted.mean
-+ x<-c()
-+ y<-c()
-+ z<-c()
-+
-+ for (k in c(min(year(variable_df$Date)):max(year(variable_df$Date))) ){
-+
-+ var_year<-subset(variable_df,year(variable_df$Date)==k )
-+ xx<-c()
-+ yy<-c()
-+ zz<-c()
-+
-+ wt<-0
-+ for (n_coeff in (1:16)){
-+ wt<-wt+unlist(coefficients_temp)[[n_coeff]]*week(var_year$Date)^(n_coeff-1)
-+ }
-+ wt_temp<-wt/sum(wt)
-+
-+ wt<-0
-+ for (n_coeff in (1:16)){
-+ wt<-wt+unlist(coefficients_hum)[[n_coeff]]*week(var_year$Date)^(n_coeff-1)
-+ }
-+ wt_hum<-wt/sum(wt)
-+
-+ wt<-0
-+ for (n_coeff in (1:16)){
-+ wt<-wt+unlist(coefficients_light)[[n_coeff]]*week(var_year$Date)^(n_coeff-1)
-+
-+ }
-+ wt_light<-wt/sum(wt)
-+
-+
-+
-+ end_time<-yday(var_year$Date)
-+ for (k2 in c(1:max(end_time)) ){
-+ yy[k2] <- weighted.mean( var_year$Maximum_air_temperature[1:k2], wt[1:k2])
-+ xx[k2] <- weighted.mean( var_year$Relative_humidity[1:k2], wt[1:k2])
-+ zz[k2] <- weighted.mean( var_year$daylength[1:k2], wt[1:k2])
-+ }
-+
-+ x<-c(x,xx)
-+ y<-c(y,yy)
-+ z<-c(z,zz)
-+ }
-+
-+ # seq(min(as.Date(variable_df$Date)), max(as.Date(variable_df$Date)), by = "day")
-+
-+ # breaks_hum[findInterval(x, breaks_hum)]
-+ # breaks_Maximum_air_temperature[findInterval(y, breaks_Maximum_air_temperature)]
-+
-+
-+
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])
-+
-+ #variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$max_temp)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,17:19,21:25)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable_z,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+
-+
-+
-+
-+ #variable_df0<-merge(variable_df_1_dis, variable_df_2_dis, by="dates")
-+ #variable_df1<-merge(variable_df0, variable_df_3_dis, by="dates")
-+ #variable_df_dis<-merge(variable_df1,var_x_loc_df, by=c("Maximum_air_temperature","Relative_humidity","daylength"))
-+
-+
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+
-+
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_Simulated_for_rec_multiple_delays.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
-> proc.time()
- user system elapsed
-622.747 9.640 632.453
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays_half_year.R b/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays_half_year.R
deleted file mode 100644
index 82ed8a967d8db2c8edae0277cca4185e74c162a6..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays_half_year.R
+++ /dev/null
@@ -1,383 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# this to calculate delay
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-1
-width_char<-paste(width)
-n_seas<-1
-
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable_z<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_z2<-"light"
-
-
-
-## Varaible file
-
-## Varaible file
-
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.csv",sep = "")
-coefficients_temp<-read.csv(file_name)
-
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_hum_week.csv",sep = "")
-coefficients_hum<-read.csv(file_name)
-
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_week.csv",sep = "")
-coefficients_light<-read.csv(file_name)
-
-
-#colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
-
-dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2010-01-31"), by = "day")
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
-
-
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-#wt<-c(0)
-#for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-#wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-#print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-#}
-
-
-#Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-Env_laboratory_PHE<-Env_laboratory
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-################### Include daylength
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-#Env_Lyme_data_int2<-merge(Env_Lyme_data_all2,lat_long_lab,by="PostCode")
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Env_laboratory_int2$lat
-day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-#var_list<-list(lat,day_year)
-#lapply(var_list,daylength)
-#lapply(Env_laboratory,daylength, Env_laboratory$lat)
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#daylength_df$lat<-as.factor(daylength_df$lat)
-#daylength_df$Date<-as.factor(daylength_df$Date)
-#Env_laboratory_int2$lat<-as.factor(Env_laboratory_int2$lat)
-#Env_laboratory_int2$Date<-as.factor(Env_laboratory_int2$Date)
-#Env_laboratory<-merge(Env_laboratory_int2,daylength_df,by=c("lat","Date"))
-
-Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
-
-### repeat for the data only ####
-
-#latitude<-Env_Lyme_data_int2$lat
-#day_of_the_year<-yday(as.Date(Env_Lyme_data_int2$Date))
-
-#daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-#daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Lyme_data_int2$Date),daylength_int1)
-#colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#Env_Lyme_data<-data.frame(Env_Lyme_data_int2,daylength_df)
-
-
-########################################
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_z2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable_z,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-###################
-
-
-delta_light<-1
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-breaks_hum<-floor(seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum)) #i
-breaks_min_temp<-floor(seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp))
-breaks_max_temp<-floor(seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp))
-breaks_light<-floor(seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light))
-
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-
-
-
-
-time_series<-c()
-
-for (i in c(1: length(All_PC_s))){
- #for (i in c(1: 15)){
-
-
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- if (length(variable_df[,1])!=0){
-
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<2010)
-
-
-
- ##########include time here and then average according to coefficients up to the time weighted.mean
- x<-c()
- y<-c()
- z<-c()
-
- for (k in c(min(year(variable_df$Date)):max(year(variable_df$Date))) ){
-
- var_year<-subset(variable_df,year(variable_df$Date)==k )
- xx<-c()
- yy<-c()
- zz<-c()
-
- #difference<-c(rep(0,times=166),rep(24,times=length(week(var_year$Date))-166))
- #This 152 correspond to firs of June when dip in the data for coefficients in light, hum, and temperature
-
- #week_year<-week(var_year$Date)-difference+1
-
- week_year<-week(var_year$Date)
-
- wt<-0
- for (n_coeff in (1:16)){
- wt<-wt+unlist(coefficients_temp)[[n_coeff]]*week_year^(n_coeff-1)
- }
- wt_temp<-wt/sum(wt)
-
- wt<-0
- for (n_coeff in (1:16)){
- wt<-wt+unlist(coefficients_hum)[[n_coeff]]*week_year^(n_coeff-1)
- }
- wt_hum<-wt/sum(wt)
-
- wt<-0
- for (n_coeff in (1:16)){
- wt<-wt+unlist(coefficients_light)[[n_coeff]]*week_year^(n_coeff-1)
- }
-
-
- end_time<-yday(var_year$Date)
- mid_time<-166
- for (k2 in c(1:mid_time) ){
- yy[k2] <- weighted.mean( var_year$Maximum_air_temperature[1:k2], wt[1:k2]/(sum( wt[1:k2] )))
- xx[k2] <- weighted.mean( var_year$Relative_humidity[1:k2], wt[1:k2]/(sum( wt[1:k2] )))
- zz[k2] <- weighted.mean( var_year$daylength[1:k2],wt[1:k2]/(sum( wt[1:k2] )))
- }
-
- for (k2 in c((mid_time+1):max(end_time)) ){
- yy[k2] <- weighted.mean( var_year$Maximum_air_temperature[mid_time:k2],wt[mid_time:k2]/(sum( wt[mid_time:k2] )))
- xx[k2] <- weighted.mean( var_year$Relative_humidity[mid_time:k2], wt[mid_time:k2]/(sum( wt[mid_time:k2] )))
- zz[k2] <- weighted.mean( var_year$daylength[mid_time:k2], wt[mid_time:k2]/(sum( wt[mid_time:k2] )))
- }
-
-
-
- x<-c(x,xx)
- y<-c(y,yy)
- z<-c(z,zz)
- }
-
- # seq(min(as.Date(variable_df$Date)), max(as.Date(variable_df$Date)), by = "day")
-
- # breaks_hum[findInterval(x, breaks_hum)]
- # breaks_Maximum_air_temperature[findInterval(y, breaks_Maximum_air_temperature)]
-
-
- w_error<-which(findInterval(x, breaks_hum)==0)
- if(length(w_error)!=0){
- min_x_error<- min(x[-w_error])
- x[w_error]<-min_x_error}
-
-
- w_error2<-which(findInterval(y, breaks_max_temp)==0)
- if(length(w_error2)!=0){
- min_y_error<- min(y[-w_error2])
- y[w_error2]<-min_y_error}
-
- w_error3<-which(findInterval(z, breaks_light)==0)
- if(length(w_error3)!=0){
- min_z_error<- min(z[-w_error3])
- z[w_error3]<-min_z_error}
-
- w_error4<-which(findInterval(x, breaks_hum)>max(breaks_hum))
- if(length(w_error4)!=0){
- max_x_error<- max(x[-w_error4])
- x[w_error4]<-max_x_error}
-
-
- w_error5<-which(findInterval(y, breaks_max_temp)>max(breaks_max_temp))
- if(length(w_error5)!=0){
- max_y_error<- max(y[-w_error5])
- y[w_error5]<-max_y_error}
-
-
- w_error6<-which(findInterval(z, breaks_light)>max(breaks_light))
- if(length(w_error6)!=0){
- max_z_error<- max(z[-w_error6])
- z[w_error6]<-max_z_error}
-
-
-
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])
-
- #variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$max_temp)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,17:19,21:25)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable_z,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
-
-
-
-
- #variable_df0<-merge(variable_df_1_dis, variable_df_2_dis, by="dates")
- #variable_df1<-merge(variable_df0, variable_df_3_dis, by="dates")
- #variable_df_dis<-merge(variable_df1,var_x_loc_df, by=c("Maximum_air_temperature","Relative_humidity","daylength"))
-
-
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-
-
-}
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_Simulated_for_rec_multiple_delays_half_year.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
diff --git a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays_half_year.Rout b/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays_half_year.Rout
deleted file mode 100644
index 50ce75236f2ff8d0b10bd3fb61718e09a1b25a23..0000000000000000000000000000000000000000
--- a/PAPER_Reconstruct_Simulated_Campylobacter_environment_light_hum_max_for_rec_multiple_delays_half_year.Rout
+++ /dev/null
@@ -1,640 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # this to calculate delay
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-1
-> width_char<-paste(width)
-> n_seas<-1
->
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable_z<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_z2<-"light"
->
->
->
-> ## Varaible file
->
-> ## Varaible file
->
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.csv",sep = "")
-> coefficients_temp<-read.csv(file_name)
->
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_hum_week.csv",sep = "")
-> coefficients_hum<-read.csv(file_name)
->
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_week.csv",sep = "")
-> coefficients_light<-read.csv(file_name)
->
->
-> #colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
->
-> dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2010-01-31"), by = "day")
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
->
->
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-> #wt<-c(0)
-> #for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-> #wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-> #print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-> #}
->
->
-> #Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> Env_laboratory_PHE<-Env_laboratory
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
-> ################### Include daylength
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
-> Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-> #Env_Lyme_data_int2<-merge(Env_Lyme_data_all2,lat_long_lab,by="PostCode")
->
-> daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
->
-> latitude<-Env_laboratory_int2$lat
-> day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-> #var_list<-list(lat,day_year)
-> #lapply(var_list,daylength)
-> #lapply(Env_laboratory,daylength, Env_laboratory$lat)
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
-> #daylength_df$lat<-as.factor(daylength_df$lat)
-> #daylength_df$Date<-as.factor(daylength_df$Date)
-> #Env_laboratory_int2$lat<-as.factor(Env_laboratory_int2$lat)
-> #Env_laboratory_int2$Date<-as.factor(Env_laboratory_int2$Date)
-> #Env_laboratory<-merge(Env_laboratory_int2,daylength_df,by=c("lat","Date"))
->
-> Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
->
-> ### repeat for the data only ####
->
-> #latitude<-Env_Lyme_data_int2$lat
-> #day_of_the_year<-yday(as.Date(Env_Lyme_data_int2$Date))
->
-> #daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> #daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Lyme_data_int2$Date),daylength_int1)
-> #colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
-> #Env_Lyme_data<-data.frame(Env_Lyme_data_int2,daylength_df)
->
->
-> ########################################
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_z2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable_z,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
-> ###################
->
->
-> delta_light<-1
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> breaks_hum<-floor(seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum)) #i
-> breaks_min_temp<-floor(seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp))
-> breaks_max_temp<-floor(seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp))
-> breaks_light<-floor(seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light))
->
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
->
->
->
->
-> time_series<-c()
->
-> for (i in c(1: length(All_PC_s))){
-+ #for (i in c(1: 15)){
-+
-+
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ if (length(variable_df[,1])!=0){
-+
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<2010)
-+
-+
-+
-+ ##########include time here and then average according to coefficients up to the time weighted.mean
-+ x<-c()
-+ y<-c()
-+ z<-c()
-+
-+ for (k in c(min(year(variable_df$Date)):max(year(variable_df$Date))) ){
-+
-+ var_year<-subset(variable_df,year(variable_df$Date)==k )
-+ xx<-c()
-+ yy<-c()
-+ zz<-c()
-+
-+ #difference<-c(rep(0,times=166),rep(24,times=length(week(var_year$Date))-166))
-+ #This 152 correspond to firs of June when dip in the data for coefficients in light, hum, and temperature
-+
-+ #week_year<-week(var_year$Date)-difference+1
-+
-+ week_year<-week(var_year$Date)
-+
-+ wt<-0
-+ for (n_coeff in (1:16)){
-+ wt<-wt+unlist(coefficients_temp)[[n_coeff]]*week_year^(n_coeff-1)
-+ }
-+ wt_temp<-wt/sum(wt)
-+
-+ wt<-0
-+ for (n_coeff in (1:16)){
-+ wt<-wt+unlist(coefficients_hum)[[n_coeff]]*week_year^(n_coeff-1)
-+ }
-+ wt_hum<-wt/sum(wt)
-+
-+ wt<-0
-+ for (n_coeff in (1:16)){
-+ wt<-wt+unlist(coefficients_light)[[n_coeff]]*week_year^(n_coeff-1)
-+ }
-+
-+
-+ end_time<-yday(var_year$Date)
-+ mid_time<-166
-+ for (k2 in c(1:mid_time) ){
-+ yy[k2] <- weighted.mean( var_year$Maximum_air_temperature[1:k2], wt[1:k2]/(sum( wt[1:k2] )))
-+ xx[k2] <- weighted.mean( var_year$Relative_humidity[1:k2], wt[1:k2]/(sum( wt[1:k2] )))
-+ zz[k2] <- weighted.mean( var_year$daylength[1:k2],wt[1:k2]/(sum( wt[1:k2] )))
-+ }
-+
-+ for (k2 in c((mid_time+1):max(end_time)) ){
-+ yy[k2] <- weighted.mean( var_year$Maximum_air_temperature[mid_time:k2],wt[mid_time:k2]/(sum( wt[mid_time:k2] )))
-+ xx[k2] <- weighted.mean( var_year$Relative_humidity[mid_time:k2], wt[mid_time:k2]/(sum( wt[mid_time:k2] )))
-+ zz[k2] <- weighted.mean( var_year$daylength[mid_time:k2], wt[mid_time:k2]/(sum( wt[mid_time:k2] )))
-+ }
-+
-+
-+
-+ x<-c(x,xx)
-+ y<-c(y,yy)
-+ z<-c(z,zz)
-+ }
-+
-+ # seq(min(as.Date(variable_df$Date)), max(as.Date(variable_df$Date)), by = "day")
-+
-+ # breaks_hum[findInterval(x, breaks_hum)]
-+ # breaks_Maximum_air_temperature[findInterval(y, breaks_Maximum_air_temperature)]
-+
-+
-+ w_error<-which(findInterval(x, breaks_hum)==0)
-+ if(length(w_error)!=0){
-+ min_x_error<- min(x[-w_error])
-+ x[w_error]<-min_x_error}
-+
-+
-+ w_error2<-which(findInterval(y, breaks_max_temp)==0)
-+ if(length(w_error2)!=0){
-+ min_y_error<- min(y[-w_error2])
-+ y[w_error2]<-min_y_error}
-+
-+ w_error3<-which(findInterval(z, breaks_light)==0)
-+ if(length(w_error3)!=0){
-+ min_z_error<- min(z[-w_error3])
-+ z[w_error3]<-min_z_error}
-+
-+ w_error4<-which(findInterval(x, breaks_hum)>max(breaks_hum))
-+ if(length(w_error4)!=0){
-+ max_x_error<- max(x[-w_error4])
-+ x[w_error4]<-max_x_error}
-+
-+
-+ w_error5<-which(findInterval(y, breaks_max_temp)>max(breaks_max_temp))
-+ if(length(w_error5)!=0){
-+ max_y_error<- max(y[-w_error5])
-+ y[w_error5]<-max_y_error}
-+
-+
-+ w_error6<-which(findInterval(z, breaks_light)>max(breaks_light))
-+ if(length(w_error6)!=0){
-+ max_z_error<- max(z[-w_error6])
-+ z[w_error6]<-max_z_error}
-+
-+
-+
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])
-+
-+ #variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$max_temp)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,17:19,21:25)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable_z,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+
-+
-+
-+
-+ #variable_df0<-merge(variable_df_1_dis, variable_df_2_dis, by="dates")
-+ #variable_df1<-merge(variable_df0, variable_df_3_dis, by="dates")
-+ #variable_df_dis<-merge(variable_df1,var_x_loc_df, by=c("Maximum_air_temperature","Relative_humidity","daylength"))
-+
-+
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+
-+
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_Simulated_for_rec_multiple_delays_half_year.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
-> proc.time()
- user system elapsed
-402.808 6.412 409.246
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_PHE_centre.R b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_PHE_centre.R
deleted file mode 100644
index ea7a5bca7ac311f87423fbce385be003f6faea9c..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_PHE_centre.R
+++ /dev/null
@@ -1,235 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# Here we assume constant and uniform humidity
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-const_PHE<-"_Av_Glous_Wilt" # Post_Code= BA13NG
-
-## Environmental Variable file. Original MEDMI files
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-#All_PC_s<-names(variable_df_1[1,])
-#All_PC_s<-All_PC_s[-c(1,2)]
-#All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PostCode=="BA13NG")
-
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_PHE,year(Env_Campylobacter_data_PHE$Date)>=1990 & year(Env_Campylobacter_data_PHE$Date)<=2015)
-
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-
-
-
-
-Env_Campylobacter_data$PostCode<-as.factor(Env_Campylobacter_data$PostCode)
-Env_laboratory$PostCode<-as.factor(Env_laboratory$PostCode)
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-20
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-i_w<-which(All_PC_s=="BA13NG")
-
-time_series<-c()
-for (i in c(i_w: i_w)){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-
- x<-variable_df$Relative_humidity
- y<-variable_df$Maximum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_PHE,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_PHE_centre.Rout b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_PHE_centre.Rout
deleted file mode 100644
index c275a354e6ae2205c032882a61073a21c2567744..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_PHE_centre.Rout
+++ /dev/null
@@ -1,498 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # Here we assume constant and uniform humidity
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
-> const_PHE<-"_Av_Glous_Wilt" # Post_Code= BA13NG
->
-> ## Environmental Variable file. Original MEDMI files
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
->
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
->
-> Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PostCode=="BA13NG")
->
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_PHE,year(Env_Campylobacter_data_PHE$Date)>=1990 & year(Env_Campylobacter_data_PHE$Date)<=2015)
->
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> Env_laboratory<-subset(Env_laboratory,Env_laboratory$PostCode=="BA13NG")
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
->
->
->
->
->
-> #wt<-c(0)
-> #for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-> #wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-> #print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-> #}
->
->
-> Env_laboratory_PHE<-Env_laboratory #[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
-> time_series<-c()
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
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-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,"_",const_PHE,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
->
-> #time_series<-read.csv(paste("../../Data_Base/OPIE_data_base/Time_series_",variable,"_",variable_x,".csv",sep=""))
-> #time_series<-time_series[,-1]
-> #colnames(time_series)<-c("Date","Cases","Lambda","Lab")
->
->
->
-> proc.time()
- user system elapsed
- 92.078 1.671 93.745
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_daylength.R b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_daylength.R
deleted file mode 100644
index de96d6f9d65a09fa61de1c8f70cd27ecd2defbc5..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_daylength.R
+++ /dev/null
@@ -1,250 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# Here we assume constant and uniform humidity
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-const_hum<-"_const_daylength_9"
-## Varaible file
-
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-
-
-
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
- variable_df$daylength<-9 #Here I impose constant and uniform relative humidity
-
- x<-variable_df$Relative_humidity
- y<-variable_df$Maximum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_daylength.Rout b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_daylength.Rout
deleted file mode 100644
index 158b702b695340267804a364fd52d38fe2d7c6a0..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_daylength.Rout
+++ /dev/null
@@ -1,713 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # Here we assume constant and uniform humidity
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
-> const_hum<-"_const_daylength_9"
-> ## Varaible file
->
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
->
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
-[1] "0.485436893203884" "AL74HQ"
-[1] "0.970873786407767" "B152TG"
-[1] "1.45631067961165" "B187QH"
-[1] "1.94174757281553" "B46NH"
-[1] "2.42718446601942" "B714HJ"
-[1] "2.9126213592233" "B757RR"
-[1] "3.39805825242718" "B95SS"
-[1] "3.88349514563107" "B987UB"
-[1] "4.36893203883495" "BA13NG"
-[1] "4.85436893203883" "BA214AT"
-[1] "5.33980582524272" "BB102PQ"
-[1] "5.8252427184466" "BB23HH"
-[1] "6.31067961165049" "BB23LR"
-[1] "6.79611650485437" "BD206TD"
-[1] "7.28155339805825" "BD96RJ"
-[1] "7.76699029126214" "BH152JB"
-[1] "8.25242718446602" "BH77DW"
-[1] "8.7378640776699" "BL40JR"
-[1] "9.22330097087379" "BL96PG"
-[1] "9.70873786407767" "BN112DH"
-[1] "10.1941747572816" "BN212UD"
-[1] "10.6796116504854" "BN25BE"
-[1] "11.1650485436893" "BR68ND"
-[1] "11.6504854368932" "BS105NB"
-[1] "12.1359223300971" "BS161LE"
-[1] "12.621359223301" "BS234TQ"
-[1] "13.1067961165049" "BS28EL"
-[1] "13.5922330097087" "CA27HY"
-[1] "14.0776699029126" "CA288JG"
-[1] "14.5631067961165" "CB22QQ"
-[1] "15.0485436893204" "CB38RE"
-[1] "15.5339805825243" "CF311RQ"
-[1] "16.0194174757282" "CF479DT"
-[1] "16.504854368932" "CF728XR"
-[1] "16.9902912621359" "CF82WW"
-[1] "17.4757281553398" "CH21UL"
-[1] "17.9611650485437" "CM201XQ"
-[1] "18.4466019417476" "CM20YX"
-[1] "18.9320388349515" "CO45JR"
-[1] "19.4174757281553" "CR77YE"
-[1] "19.9029126213592" "CT94AN"
-[1] "20.3883495145631" "CV107DJ"
-[1] "20.873786407767" "CV14FH"
-[1] "21.3592233009709" "CV345BW"
-[1] "21.8446601941748" "CW14QJ"
-[1] "22.3300970873786" "DA146LT"
-[1] "22.8155339805825" "DA28DA"
-[1] "23.3009708737864" "DE12QY"
-[1] "23.7864077669903" "DE130RB"
-[1] "24.2718446601942" "DE223NE"
-[1] "24.7572815533981" "DH15TW"
-[1] "25.2427184466019" "DL146AD"
-[1] "25.7281553398058" "DL36HX"
-[1] "26.2135922330097" "DN171RS"
-[1] "26.6990291262136" "DN25LT"
-[1] "27.1844660194175" "DN332BA"
-[1] "27.6699029126214" "DT12JY"
-[1] "28.1553398058252" "DY12HQ"
-[1] "28.6407766990291" "E111NR"
-[1] "29.126213592233" "E11BB"
-[1] "29.6116504854369" "E96SR"
-[1] "30.0970873786408" "EN53DJ"
-[1] "30.5825242718447" "EX25AD"
-[1] "31.0679611650485" "EX314JB"
-[1] "31.5533980582524" "FY38NR"
-[1] "32.0388349514563" "GL13NN"
-[1] "32.5242718446602" "GL537AN"
-[1] "33.0097087378641" "GU167UJ"
-[1] "33.495145631068" "HA13UJ"
-[1] "33.9805825242718" "HD33EA"
-[1] "34.4660194174757" "HG27SX"
-[1] "34.9514563106796" "HP112TT"
-[1] "35.4368932038835" "HP218AL"
-[1] "35.9223300970874" "HP24AD"
-[1] "36.4077669902913" "HR12ER"
-[1] "36.8932038834951" "HU32JZ"
-[1] "37.378640776699" "IG119LX"
-[1] "37.8640776699029" "IP332QZ"
-[1] "38.3495145631068" "IP45PD"
-[1] "38.8349514563107" "KT160PZ"
-[1] "39.3203883495146" "KT198PB"
-[1] "39.8058252427184" "KT27QB"
-[1] "40.2912621359223" "L122AP"
-[1] "40.7766990291262" "L355DR"
-[1] "41.2621359223301" "L634JY"
-[1] "41.747572815534" "L78XP"
-[1] "42.2330097087379" "L97AL"
-[1] "42.7184466019417" "LA144LF"
-[1] "43.2038834951456" "LA14RP"
-[1] "43.6893203883495" "LA97RG"
-[1] "44.1747572815534" "LE15WW"
-[1] "44.6601941747573" "LL137TP"
-[1] "45.1456310679612" "LL185UJ"
-[1] "45.6310679611651" "LL572TP"
-[1] "46.1165048543689" "LN25QY"
-[1] "46.6019417475728" "LS157TR"
-[1] "47.0873786407767" "LS29JT"
-[1] "47.5728155339806" "LS97TF"
-[1] "48.0582524271845" "LU40EP"
-[1] "48.5436893203884" "M208LR"
-[1] "49.0291262135922" "M415SL"
-[1] "49.5145631067961" "M68WH"
-[1] "50" "M85RB"
-[1] "50.4854368932039" "M97AA"
-[1] "50.9708737864078" "ME169QQ"
-[1] "51.4563106796116" "ME207NJ"
-[1] "51.9417475728155" "ME75NY"
-[1] "52.4271844660194" "MK429DJ"
-[1] "52.9126213592233" "MK65LD"
-[1] "53.3980582524272" "N181QX"
-[1] "53.8834951456311" "N195NF"
-[1] "54.3689320388349" "NE298NH"
-[1] "54.8543689320388" "NE340PL"
-[1] "55.3398058252427" "NE46BE"
-[1] "55.8252427184466" "NE77DN"
-[1] "56.3106796116505" "NE96SX"
-[1] "56.7961165048544" "NG174JL"
-[1] "57.2815533980583" "NG318DG"
-[1] "57.7669902912621" "NG72UH"
-[1] "58.252427184466" "NN15BD"
-[1] "58.7378640776699" "NN168UZ"
-[1] "59.2233009708738" "NP77EG"
-[1] "59.7087378640777" "NP92UB"
-[1] "60.1941747572816" "NR23TX"
-[1] "60.6796116504854" "NR316LA"
-[1] "61.1650485436893" "NW107NS"
-[1] "61.6504854368932" "NW32QG"
-[1] "62.1359223300971" "NW95HT"
-[1] "62.621359223301" "OL129QB"
-[1] "63.1067961165049" "OL12JH"
-[1] "63.5922330097087" "OL69RW"
-[1] "64.0776699029126" "OX39DU"
-[1] "64.5631067961165" "PE188NT"
-[1] "65.0485436893204" "PE219QS"
-[1] "65.5339805825243" "PE304ET"
-[1] "66.0194174757282" "PE36DA"
-[1] "66.504854368932" "PL68DH"
-[1] "66.9902912621359" "PO194SE"
-[1] "67.4757281553398" "PO305TG"
-[1] "67.9611650485437" "PO36AQ"
-[1] "68.4466019417476" "PR29HT"
-[1] "68.9320388349515" "PR86PN"
-[1] "69.4174757281553" "RG15AN"
-[1] "69.9029126213592" "RG249NA"
-[1] "70.3883495145631" "RH117DH"
-[1] "70.873786407767" "RM30BE"
-[1] "71.3592233009709" "RM70AG"
-[1] "71.8446601941748" "S445BL"
-[1] "72.3300970873786" "S57BQ"
-[1] "72.8155339805825" "S602UD"
-[1] "73.3009708737864" "S752EP"
-[1] "73.7864077669903" "S810BD"
-[1] "74.2718446601942" "SA28QA"
-[1] "74.7572815533981" "SA312AF"
-[1] "75.2427184466019" "SA612PZ"
-[1] "75.7281553398058" "SE136LH"
-[1] "76.2135922330097" "SE17EH"
-[1] "76.6990291262136" "SE184QH"
-[1] "77.1844660194175" "SE59RS"
-[1] "77.6699029126214" "SG14AB"
-[1] "78.1553398058252" "SK103BL"
-[1] "78.6407766990291" "SK27JE"
-[1] "79.126213592233" "SL24HL"
-[1] "79.6116504854369" "SM51AA"
-[1] "80.0970873786408" "SN36BB"
-[1] "80.5825242718447" "SO166YD"
-[1] "81.0679611650485" "SO226ZB"
-[1] "81.5533980582524" "SP28BJ"
-[1] "82.0388349514563" "SR47TP"
-[1] "82.5242718446602" "SS00RY"
-[1] "83.0097087378641" "SS165NL"
-[1] "83.495145631068" "ST163SA"
-[1] "83.9805825242718" "ST47PX"
-[1] "84.4660194174757" "SW109NH"
-[1] "84.9514563106796" "SW170QT"
-[1] "85.4368932038835" "SW36JJ"
-[1] "85.9223300970874" "SW36NP"
-[1] "86.4077669902913" "SY231ER"
-[1] "86.8932038834951" "SY38XQ"
-[1] "87.378640776699" "TA15DB"
-[1] "87.8640776699029" "TN240LZ"
-[1] "88.3495145631068" "TQ27AA"
-[1] "88.8349514563107" "TR13LQ"
-[1] "89.3203883495146" "TS198PE"
-[1] "89.8058252427184" "TS249AH"
-[1] "90.2912621359223" "TS43BW"
-[1] "90.7766990291262" "TW76AF"
-[1] "91.2621359223301" "UB13HW"
-[1] "91.747572815534" "UB83NN"
-[1] "92.2330097087379" "UB96JH"
-[1] "92.7184466019417" "W120NN"
-[1] "93.2038834951456" "W21NY"
-[1] "93.6893203883495" "W68RF"
-[1] "94.1747572815534" "WA51QG"
-[1] "94.6601941747573" "WC1E6DB"
-[1] "95.1456310679612" "WC1N3JH"
-[1] "95.631067961165" "WD18HB"
-[1] "96.1165048543689" "WF134HS"
-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
->
->
->
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+ variable_df$daylength<-9 #Here I impose constant and uniform relative humidity
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
->
->
-> proc.time()
- user system elapsed
-185.642 1.687 187.328
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_daylength.R b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_daylength.R
deleted file mode 100644
index 36ad68bec228e13c560d68efbb5bd9ab28f75914..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_daylength.R
+++ /dev/null
@@ -1,251 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# Here we assume constant and uniform humidity and temperature
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-const_hum<-"_const_temp_14_hum_75"
-## Varaible file
-
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-
-
-
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
- variable_df_dis_int$Relative_humidity<-75 #Here I impose constant and uniform relative humidity
- variable_df_dis_int$Maximum_air_temperature<-14 #Here I impose constant and uniform relative humidity
-
- x<-variable_df_dis_int$Relative_humidity
- y<-variable_df_dis_int$Maximum_air_temperature
- z<-variable_df_dis_int$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_daylength.Rout b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_daylength.Rout
deleted file mode 100644
index 8cb48a11641e80ec868bbffd03250af181051f67..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_daylength.Rout
+++ /dev/null
@@ -1,714 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # Here we assume constant and uniform humidity and temperature
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
-> const_hum<-"_const_temp_14_hum_70"
-> ## Varaible file
->
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
->
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
-[1] "0.485436893203884" "AL74HQ"
-[1] "0.970873786407767" "B152TG"
-[1] "1.45631067961165" "B187QH"
-[1] "1.94174757281553" "B46NH"
-[1] "2.42718446601942" "B714HJ"
-[1] "2.9126213592233" "B757RR"
-[1] "3.39805825242718" "B95SS"
-[1] "3.88349514563107" "B987UB"
-[1] "4.36893203883495" "BA13NG"
-[1] "4.85436893203883" "BA214AT"
-[1] "5.33980582524272" "BB102PQ"
-[1] "5.8252427184466" "BB23HH"
-[1] "6.31067961165049" "BB23LR"
-[1] "6.79611650485437" "BD206TD"
-[1] "7.28155339805825" "BD96RJ"
-[1] "7.76699029126214" "BH152JB"
-[1] "8.25242718446602" "BH77DW"
-[1] "8.7378640776699" "BL40JR"
-[1] "9.22330097087379" "BL96PG"
-[1] "9.70873786407767" "BN112DH"
-[1] "10.1941747572816" "BN212UD"
-[1] "10.6796116504854" "BN25BE"
-[1] "11.1650485436893" "BR68ND"
-[1] "11.6504854368932" "BS105NB"
-[1] "12.1359223300971" "BS161LE"
-[1] "12.621359223301" "BS234TQ"
-[1] "13.1067961165049" "BS28EL"
-[1] "13.5922330097087" "CA27HY"
-[1] "14.0776699029126" "CA288JG"
-[1] "14.5631067961165" "CB22QQ"
-[1] "15.0485436893204" "CB38RE"
-[1] "15.5339805825243" "CF311RQ"
-[1] "16.0194174757282" "CF479DT"
-[1] "16.504854368932" "CF728XR"
-[1] "16.9902912621359" "CF82WW"
-[1] "17.4757281553398" "CH21UL"
-[1] "17.9611650485437" "CM201XQ"
-[1] "18.4466019417476" "CM20YX"
-[1] "18.9320388349515" "CO45JR"
-[1] "19.4174757281553" "CR77YE"
-[1] "19.9029126213592" "CT94AN"
-[1] "20.3883495145631" "CV107DJ"
-[1] "20.873786407767" "CV14FH"
-[1] "21.3592233009709" "CV345BW"
-[1] "21.8446601941748" "CW14QJ"
-[1] "22.3300970873786" "DA146LT"
-[1] "22.8155339805825" "DA28DA"
-[1] "23.3009708737864" "DE12QY"
-[1] "23.7864077669903" "DE130RB"
-[1] "24.2718446601942" "DE223NE"
-[1] "24.7572815533981" "DH15TW"
-[1] "25.2427184466019" "DL146AD"
-[1] "25.7281553398058" "DL36HX"
-[1] "26.2135922330097" "DN171RS"
-[1] "26.6990291262136" "DN25LT"
-[1] "27.1844660194175" "DN332BA"
-[1] "27.6699029126214" "DT12JY"
-[1] "28.1553398058252" "DY12HQ"
-[1] "28.6407766990291" "E111NR"
-[1] "29.126213592233" "E11BB"
-[1] "29.6116504854369" "E96SR"
-[1] "30.0970873786408" "EN53DJ"
-[1] "30.5825242718447" "EX25AD"
-[1] "31.0679611650485" "EX314JB"
-[1] "31.5533980582524" "FY38NR"
-[1] "32.0388349514563" "GL13NN"
-[1] "32.5242718446602" "GL537AN"
-[1] "33.0097087378641" "GU167UJ"
-[1] "33.495145631068" "HA13UJ"
-[1] "33.9805825242718" "HD33EA"
-[1] "34.4660194174757" "HG27SX"
-[1] "34.9514563106796" "HP112TT"
-[1] "35.4368932038835" "HP218AL"
-[1] "35.9223300970874" "HP24AD"
-[1] "36.4077669902913" "HR12ER"
-[1] "36.8932038834951" "HU32JZ"
-[1] "37.378640776699" "IG119LX"
-[1] "37.8640776699029" "IP332QZ"
-[1] "38.3495145631068" "IP45PD"
-[1] "38.8349514563107" "KT160PZ"
-[1] "39.3203883495146" "KT198PB"
-[1] "39.8058252427184" "KT27QB"
-[1] "40.2912621359223" "L122AP"
-[1] "40.7766990291262" "L355DR"
-[1] "41.2621359223301" "L634JY"
-[1] "41.747572815534" "L78XP"
-[1] "42.2330097087379" "L97AL"
-[1] "42.7184466019417" "LA144LF"
-[1] "43.2038834951456" "LA14RP"
-[1] "43.6893203883495" "LA97RG"
-[1] "44.1747572815534" "LE15WW"
-[1] "44.6601941747573" "LL137TP"
-[1] "45.1456310679612" "LL185UJ"
-[1] "45.6310679611651" "LL572TP"
-[1] "46.1165048543689" "LN25QY"
-[1] "46.6019417475728" "LS157TR"
-[1] "47.0873786407767" "LS29JT"
-[1] "47.5728155339806" "LS97TF"
-[1] "48.0582524271845" "LU40EP"
-[1] "48.5436893203884" "M208LR"
-[1] "49.0291262135922" "M415SL"
-[1] "49.5145631067961" "M68WH"
-[1] "50" "M85RB"
-[1] "50.4854368932039" "M97AA"
-[1] "50.9708737864078" "ME169QQ"
-[1] "51.4563106796116" "ME207NJ"
-[1] "51.9417475728155" "ME75NY"
-[1] "52.4271844660194" "MK429DJ"
-[1] "52.9126213592233" "MK65LD"
-[1] "53.3980582524272" "N181QX"
-[1] "53.8834951456311" "N195NF"
-[1] "54.3689320388349" "NE298NH"
-[1] "54.8543689320388" "NE340PL"
-[1] "55.3398058252427" "NE46BE"
-[1] "55.8252427184466" "NE77DN"
-[1] "56.3106796116505" "NE96SX"
-[1] "56.7961165048544" "NG174JL"
-[1] "57.2815533980583" "NG318DG"
-[1] "57.7669902912621" "NG72UH"
-[1] "58.252427184466" "NN15BD"
-[1] "58.7378640776699" "NN168UZ"
-[1] "59.2233009708738" "NP77EG"
-[1] "59.7087378640777" "NP92UB"
-[1] "60.1941747572816" "NR23TX"
-[1] "60.6796116504854" "NR316LA"
-[1] "61.1650485436893" "NW107NS"
-[1] "61.6504854368932" "NW32QG"
-[1] "62.1359223300971" "NW95HT"
-[1] "62.621359223301" "OL129QB"
-[1] "63.1067961165049" "OL12JH"
-[1] "63.5922330097087" "OL69RW"
-[1] "64.0776699029126" "OX39DU"
-[1] "64.5631067961165" "PE188NT"
-[1] "65.0485436893204" "PE219QS"
-[1] "65.5339805825243" "PE304ET"
-[1] "66.0194174757282" "PE36DA"
-[1] "66.504854368932" "PL68DH"
-[1] "66.9902912621359" "PO194SE"
-[1] "67.4757281553398" "PO305TG"
-[1] "67.9611650485437" "PO36AQ"
-[1] "68.4466019417476" "PR29HT"
-[1] "68.9320388349515" "PR86PN"
-[1] "69.4174757281553" "RG15AN"
-[1] "69.9029126213592" "RG249NA"
-[1] "70.3883495145631" "RH117DH"
-[1] "70.873786407767" "RM30BE"
-[1] "71.3592233009709" "RM70AG"
-[1] "71.8446601941748" "S445BL"
-[1] "72.3300970873786" "S57BQ"
-[1] "72.8155339805825" "S602UD"
-[1] "73.3009708737864" "S752EP"
-[1] "73.7864077669903" "S810BD"
-[1] "74.2718446601942" "SA28QA"
-[1] "74.7572815533981" "SA312AF"
-[1] "75.2427184466019" "SA612PZ"
-[1] "75.7281553398058" "SE136LH"
-[1] "76.2135922330097" "SE17EH"
-[1] "76.6990291262136" "SE184QH"
-[1] "77.1844660194175" "SE59RS"
-[1] "77.6699029126214" "SG14AB"
-[1] "78.1553398058252" "SK103BL"
-[1] "78.6407766990291" "SK27JE"
-[1] "79.126213592233" "SL24HL"
-[1] "79.6116504854369" "SM51AA"
-[1] "80.0970873786408" "SN36BB"
-[1] "80.5825242718447" "SO166YD"
-[1] "81.0679611650485" "SO226ZB"
-[1] "81.5533980582524" "SP28BJ"
-[1] "82.0388349514563" "SR47TP"
-[1] "82.5242718446602" "SS00RY"
-[1] "83.0097087378641" "SS165NL"
-[1] "83.495145631068" "ST163SA"
-[1] "83.9805825242718" "ST47PX"
-[1] "84.4660194174757" "SW109NH"
-[1] "84.9514563106796" "SW170QT"
-[1] "85.4368932038835" "SW36JJ"
-[1] "85.9223300970874" "SW36NP"
-[1] "86.4077669902913" "SY231ER"
-[1] "86.8932038834951" "SY38XQ"
-[1] "87.378640776699" "TA15DB"
-[1] "87.8640776699029" "TN240LZ"
-[1] "88.3495145631068" "TQ27AA"
-[1] "88.8349514563107" "TR13LQ"
-[1] "89.3203883495146" "TS198PE"
-[1] "89.8058252427184" "TS249AH"
-[1] "90.2912621359223" "TS43BW"
-[1] "90.7766990291262" "TW76AF"
-[1] "91.2621359223301" "UB13HW"
-[1] "91.747572815534" "UB83NN"
-[1] "92.2330097087379" "UB96JH"
-[1] "92.7184466019417" "W120NN"
-[1] "93.2038834951456" "W21NY"
-[1] "93.6893203883495" "W68RF"
-[1] "94.1747572815534" "WA51QG"
-[1] "94.6601941747573" "WC1E6DB"
-[1] "95.1456310679612" "WC1N3JH"
-[1] "95.631067961165" "WD18HB"
-[1] "96.1165048543689" "WF134HS"
-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
->
->
->
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+ variable_df_dis_int$Relative_humidity<-75 #Here I impose constant and uniform relative humidity
-+ variable_df_dis_int$Maximum_air_temperature<-14 #Here I impose constant and uniform relative humidity
-+
-+ x<-variable_df_dis_int$Relative_humidity
-+ y<-variable_df_dis_int$Maximum_air_temperature
-+ z<-variable_df_dis_int$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
->
->
-> proc.time()
- user system elapsed
-198.167 1.800 199.976
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_humidity.R b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_humidity.R
deleted file mode 100644
index cd636efae22e6660956dfbd6b4038120cae848f1..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_humidity.R
+++ /dev/null
@@ -1,252 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# Here we assume constant and uniform humidity
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-const_hum<-"_const_temp_14_daylenght_9"
-## Varaible file
-
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-
-
-
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
- variable_df_dis_int$daylength<-9 #Here I impose constant and uniform relative humidity
- variable_df_dis_int$Maximum_air_temperature<-14
-
-
- x<-variable_df_dis_int$Relative_humidity
- y<-variable_df_dis_int$Maximum_air_temperature
- z<-variable_df_dis_int$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_humidity.Rout b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_humidity.Rout
deleted file mode 100644
index 76fdc89217f4e2dee5ff194fcac28688378db7dc..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_humidity.Rout
+++ /dev/null
@@ -1,715 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # Here we assume constant and uniform humidity
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
-> const_hum<-"_const_temp_14_daylenght_9"
-> ## Varaible file
->
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
->
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
-[1] "0.485436893203884" "AL74HQ"
-[1] "0.970873786407767" "B152TG"
-[1] "1.45631067961165" "B187QH"
-[1] "1.94174757281553" "B46NH"
-[1] "2.42718446601942" "B714HJ"
-[1] "2.9126213592233" "B757RR"
-[1] "3.39805825242718" "B95SS"
-[1] "3.88349514563107" "B987UB"
-[1] "4.36893203883495" "BA13NG"
-[1] "4.85436893203883" "BA214AT"
-[1] "5.33980582524272" "BB102PQ"
-[1] "5.8252427184466" "BB23HH"
-[1] "6.31067961165049" "BB23LR"
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-[1] "48.0582524271845" "LU40EP"
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-[1] "49.0291262135922" "M415SL"
-[1] "49.5145631067961" "M68WH"
-[1] "50" "M85RB"
-[1] "50.4854368932039" "M97AA"
-[1] "50.9708737864078" "ME169QQ"
-[1] "51.4563106796116" "ME207NJ"
-[1] "51.9417475728155" "ME75NY"
-[1] "52.4271844660194" "MK429DJ"
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-[1] "53.3980582524272" "N181QX"
-[1] "53.8834951456311" "N195NF"
-[1] "54.3689320388349" "NE298NH"
-[1] "54.8543689320388" "NE340PL"
-[1] "55.3398058252427" "NE46BE"
-[1] "55.8252427184466" "NE77DN"
-[1] "56.3106796116505" "NE96SX"
-[1] "56.7961165048544" "NG174JL"
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-[1] "61.1650485436893" "NW107NS"
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-[1] "62.621359223301" "OL129QB"
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-[1] "65.0485436893204" "PE219QS"
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-[1] "67.9611650485437" "PO36AQ"
-[1] "68.4466019417476" "PR29HT"
-[1] "68.9320388349515" "PR86PN"
-[1] "69.4174757281553" "RG15AN"
-[1] "69.9029126213592" "RG249NA"
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-[1] "70.873786407767" "RM30BE"
-[1] "71.3592233009709" "RM70AG"
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-[1] "81.0679611650485" "SO226ZB"
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-[1] "82.0388349514563" "SR47TP"
-[1] "82.5242718446602" "SS00RY"
-[1] "83.0097087378641" "SS165NL"
-[1] "83.495145631068" "ST163SA"
-[1] "83.9805825242718" "ST47PX"
-[1] "84.4660194174757" "SW109NH"
-[1] "84.9514563106796" "SW170QT"
-[1] "85.4368932038835" "SW36JJ"
-[1] "85.9223300970874" "SW36NP"
-[1] "86.4077669902913" "SY231ER"
-[1] "86.8932038834951" "SY38XQ"
-[1] "87.378640776699" "TA15DB"
-[1] "87.8640776699029" "TN240LZ"
-[1] "88.3495145631068" "TQ27AA"
-[1] "88.8349514563107" "TR13LQ"
-[1] "89.3203883495146" "TS198PE"
-[1] "89.8058252427184" "TS249AH"
-[1] "90.2912621359223" "TS43BW"
-[1] "90.7766990291262" "TW76AF"
-[1] "91.2621359223301" "UB13HW"
-[1] "91.747572815534" "UB83NN"
-[1] "92.2330097087379" "UB96JH"
-[1] "92.7184466019417" "W120NN"
-[1] "93.2038834951456" "W21NY"
-[1] "93.6893203883495" "W68RF"
-[1] "94.1747572815534" "WA51QG"
-[1] "94.6601941747573" "WC1E6DB"
-[1] "95.1456310679612" "WC1N3JH"
-[1] "95.631067961165" "WD18HB"
-[1] "96.1165048543689" "WF134HS"
-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
->
->
->
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+ variable_df_dis_int$daylength<-9 #Here I impose constant and uniform relative humidity
-+ variable_df_dis_int$Maximum_air_temperature<-14
-+
-+
-+ x<-variable_df_dis_int$Relative_humidity
-+ y<-variable_df_dis_int$Maximum_air_temperature
-+ z<-variable_df_dis_int$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
->
->
-> proc.time()
- user system elapsed
-214.802 1.858 216.796
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_temperature.R b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_temperature.R
deleted file mode 100644
index d0abb206c0bc6072125e89b7c0bfc0c36b4b8c0b..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_temperature.R
+++ /dev/null
@@ -1,251 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# Here we assume constant and uniform humidity
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-const_hum<-"_const_hum_75_daylenght_9"
-## Varaible file
-
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-
-
-
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
- variable_df_dis_int$Relative_humidity<-75 #Here I impose constant and uniform relative humidity
- variable_df_dis_int$daylength<-9
-
- x<-variable_df_dis_int$Relative_humidity
- y<-variable_df_dis_int$Maximum_air_temperature
- z<-variable_df_dis_int$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_temperature.Rout b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_temperature.Rout
deleted file mode 100644
index 2c1380962d5eaf8c1368ce8625bb50bdcd1de1ef..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_except_temperature.Rout
+++ /dev/null
@@ -1,498 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # Here we assume constant and uniform humidity
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
-> const_hum<-"_const_hum_70_daylenght_9"
-> ## Varaible file
->
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
->
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
-[1] "0.485436893203884" "AL74HQ"
-[1] "0.970873786407767" "B152TG"
-[1] "1.45631067961165" "B187QH"
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-[1] "2.42718446601942" "B714HJ"
-[1] "2.9126213592233" "B757RR"
-[1] "3.39805825242718" "B95SS"
-[1] "3.88349514563107" "B987UB"
-[1] "4.36893203883495" "BA13NG"
-[1] "4.85436893203883" "BA214AT"
-[1] "5.33980582524272" "BB102PQ"
-[1] "5.8252427184466" "BB23HH"
-[1] "6.31067961165049" "BB23LR"
-[1] "6.79611650485437" "BD206TD"
-[1] "7.28155339805825" "BD96RJ"
-[1] "7.76699029126214" "BH152JB"
-[1] "8.25242718446602" "BH77DW"
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-[1] "9.22330097087379" "BL96PG"
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-[1] "13.1067961165049" "BS28EL"
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-[1] "21.8446601941748" "CW14QJ"
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-[1] "22.8155339805825" "DA28DA"
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-[1] "24.7572815533981" "DH15TW"
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-[1] "25.7281553398058" "DL36HX"
-[1] "26.2135922330097" "DN171RS"
-[1] "26.6990291262136" "DN25LT"
-[1] "27.1844660194175" "DN332BA"
-[1] "27.6699029126214" "DT12JY"
-[1] "28.1553398058252" "DY12HQ"
-[1] "28.6407766990291" "E111NR"
-[1] "29.126213592233" "E11BB"
-[1] "29.6116504854369" "E96SR"
-[1] "30.0970873786408" "EN53DJ"
-[1] "30.5825242718447" "EX25AD"
-[1] "31.0679611650485" "EX314JB"
-[1] "31.5533980582524" "FY38NR"
-[1] "32.0388349514563" "GL13NN"
-[1] "32.5242718446602" "GL537AN"
-[1] "33.0097087378641" "GU167UJ"
-[1] "33.495145631068" "HA13UJ"
-[1] "33.9805825242718" "HD33EA"
-[1] "34.4660194174757" "HG27SX"
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-[1] "43.2038834951456" "LA14RP"
-[1] "43.6893203883495" "LA97RG"
-[1] "44.1747572815534" "LE15WW"
-[1] "44.6601941747573" "LL137TP"
-[1] "45.1456310679612" "LL185UJ"
-[1] "45.6310679611651" "LL572TP"
-[1] "46.1165048543689" "LN25QY"
-[1] "46.6019417475728" "LS157TR"
-[1] "47.0873786407767" "LS29JT"
-[1] "47.5728155339806" "LS97TF"
-[1] "48.0582524271845" "LU40EP"
-[1] "48.5436893203884" "M208LR"
-[1] "49.0291262135922" "M415SL"
-[1] "49.5145631067961" "M68WH"
-[1] "50" "M85RB"
-[1] "50.4854368932039" "M97AA"
-[1] "50.9708737864078" "ME169QQ"
-[1] "51.4563106796116" "ME207NJ"
-[1] "51.9417475728155" "ME75NY"
-[1] "52.4271844660194" "MK429DJ"
-[1] "52.9126213592233" "MK65LD"
-[1] "53.3980582524272" "N181QX"
-[1] "53.8834951456311" "N195NF"
-[1] "54.3689320388349" "NE298NH"
-[1] "54.8543689320388" "NE340PL"
-[1] "55.3398058252427" "NE46BE"
-[1] "55.8252427184466" "NE77DN"
-[1] "56.3106796116505" "NE96SX"
-[1] "56.7961165048544" "NG174JL"
-[1] "57.2815533980583" "NG318DG"
-[1] "57.7669902912621" "NG72UH"
-[1] "58.252427184466" "NN15BD"
-[1] "58.7378640776699" "NN168UZ"
-[1] "59.2233009708738" "NP77EG"
-[1] "59.7087378640777" "NP92UB"
-[1] "60.1941747572816" "NR23TX"
-[1] "60.6796116504854" "NR316LA"
-[1] "61.1650485436893" "NW107NS"
-[1] "61.6504854368932" "NW32QG"
-[1] "62.1359223300971" "NW95HT"
-[1] "62.621359223301" "OL129QB"
-[1] "63.1067961165049" "OL12JH"
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-[1] "64.5631067961165" "PE188NT"
-[1] "65.0485436893204" "PE219QS"
-[1] "65.5339805825243" "PE304ET"
-[1] "66.0194174757282" "PE36DA"
-[1] "66.504854368932" "PL68DH"
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-[1] "67.4757281553398" "PO305TG"
-[1] "67.9611650485437" "PO36AQ"
-[1] "68.4466019417476" "PR29HT"
-[1] "68.9320388349515" "PR86PN"
-[1] "69.4174757281553" "RG15AN"
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-[1] "70.873786407767" "RM30BE"
-[1] "71.3592233009709" "RM70AG"
-[1] "71.8446601941748" "S445BL"
-[1] "72.3300970873786" "S57BQ"
-[1] "72.8155339805825" "S602UD"
-[1] "73.3009708737864" "S752EP"
-[1] "73.7864077669903" "S810BD"
-[1] "74.2718446601942" "SA28QA"
-[1] "74.7572815533981" "SA312AF"
-[1] "75.2427184466019" "SA612PZ"
-[1] "75.7281553398058" "SE136LH"
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-[1] "77.1844660194175" "SE59RS"
-[1] "77.6699029126214" "SG14AB"
-[1] "78.1553398058252" "SK103BL"
-[1] "78.6407766990291" "SK27JE"
-[1] "79.126213592233" "SL24HL"
-[1] "79.6116504854369" "SM51AA"
-[1] "80.0970873786408" "SN36BB"
-[1] "80.5825242718447" "SO166YD"
-[1] "81.0679611650485" "SO226ZB"
-[1] "81.5533980582524" "SP28BJ"
-[1] "82.0388349514563" "SR47TP"
-[1] "82.5242718446602" "SS00RY"
-[1] "83.0097087378641" "SS165NL"
-[1] "83.495145631068" "ST163SA"
-[1] "83.9805825242718" "ST47PX"
-[1] "84.4660194174757" "SW109NH"
-[1] "84.9514563106796" "SW170QT"
-[1] "85.4368932038835" "SW36JJ"
-[1] "85.9223300970874" "SW36NP"
-[1] "86.4077669902913" "SY231ER"
-[1] "86.8932038834951" "SY38XQ"
-[1] "87.378640776699" "TA15DB"
-[1] "87.8640776699029" "TN240LZ"
-[1] "88.3495145631068" "TQ27AA"
-[1] "88.8349514563107" "TR13LQ"
-[1] "89.3203883495146" "TS198PE"
-[1] "89.8058252427184" "TS249AH"
-[1] "90.2912621359223" "TS43BW"
-[1] "90.7766990291262" "TW76AF"
-[1] "91.2621359223301" "UB13HW"
-[1] "91.747572815534" "UB83NN"
-[1] "92.2330097087379" "UB96JH"
-[1] "92.7184466019417" "W120NN"
-[1] "93.2038834951456" "W21NY"
-[1] "93.6893203883495" "W68RF"
-[1] "94.1747572815534" "WA51QG"
-[1] "94.6601941747573" "WC1E6DB"
-[1] "95.1456310679612" "WC1N3JH"
-[1] "95.631067961165" "WD18HB"
-[1] "96.1165048543689" "WF134HS"
-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
->
->
->
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+ variable_df$Relative_humidity<-70 #Here I impose constant and uniform relative humidity
-+ variable_df$daylength<-9
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-Error in data.frame(variable_df_dis$Date, comp_cases, comp_cases2, comp_cases3, :
- arguments imply differing number of rows: 0, 1
-Execution halted
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_humidity.R b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_humidity.R
deleted file mode 100644
index a9c5b85c25bd2225339ee76abc9712a9c2472265..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_humidity.R
+++ /dev/null
@@ -1,250 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# Here we assume constant and uniform humidity
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-const_hum<-"_const_hum_70"
-## Varaible file
-
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-
-
-
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
- variable_df$Relative_humidity<-70 #Here I impose constant and uniform relative humidity
-
- x<-variable_df$Relative_humidity
- y<-variable_df$Maximum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_humidity.Rout b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_humidity.Rout
deleted file mode 100644
index 3c3dca2700ca2c4da278a04c4aee23610a9c492c..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_humidity.Rout
+++ /dev/null
@@ -1,713 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # Here we assume constant and uniform humidity
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
-> const_hum<-"_const_hum_70"
-> ## Varaible file
->
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
->
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
-[1] "0.485436893203884" "AL74HQ"
-[1] "0.970873786407767" "B152TG"
-[1] "1.45631067961165" "B187QH"
-[1] "1.94174757281553" "B46NH"
-[1] "2.42718446601942" "B714HJ"
-[1] "2.9126213592233" "B757RR"
-[1] "3.39805825242718" "B95SS"
-[1] "3.88349514563107" "B987UB"
-[1] "4.36893203883495" "BA13NG"
-[1] "4.85436893203883" "BA214AT"
-[1] "5.33980582524272" "BB102PQ"
-[1] "5.8252427184466" "BB23HH"
-[1] "6.31067961165049" "BB23LR"
-[1] "6.79611650485437" "BD206TD"
-[1] "7.28155339805825" "BD96RJ"
-[1] "7.76699029126214" "BH152JB"
-[1] "8.25242718446602" "BH77DW"
-[1] "8.7378640776699" "BL40JR"
-[1] "9.22330097087379" "BL96PG"
-[1] "9.70873786407767" "BN112DH"
-[1] "10.1941747572816" "BN212UD"
-[1] "10.6796116504854" "BN25BE"
-[1] "11.1650485436893" "BR68ND"
-[1] "11.6504854368932" "BS105NB"
-[1] "12.1359223300971" "BS161LE"
-[1] "12.621359223301" "BS234TQ"
-[1] "13.1067961165049" "BS28EL"
-[1] "13.5922330097087" "CA27HY"
-[1] "14.0776699029126" "CA288JG"
-[1] "14.5631067961165" "CB22QQ"
-[1] "15.0485436893204" "CB38RE"
-[1] "15.5339805825243" "CF311RQ"
-[1] "16.0194174757282" "CF479DT"
-[1] "16.504854368932" "CF728XR"
-[1] "16.9902912621359" "CF82WW"
-[1] "17.4757281553398" "CH21UL"
-[1] "17.9611650485437" "CM201XQ"
-[1] "18.4466019417476" "CM20YX"
-[1] "18.9320388349515" "CO45JR"
-[1] "19.4174757281553" "CR77YE"
-[1] "19.9029126213592" "CT94AN"
-[1] "20.3883495145631" "CV107DJ"
-[1] "20.873786407767" "CV14FH"
-[1] "21.3592233009709" "CV345BW"
-[1] "21.8446601941748" "CW14QJ"
-[1] "22.3300970873786" "DA146LT"
-[1] "22.8155339805825" "DA28DA"
-[1] "23.3009708737864" "DE12QY"
-[1] "23.7864077669903" "DE130RB"
-[1] "24.2718446601942" "DE223NE"
-[1] "24.7572815533981" "DH15TW"
-[1] "25.2427184466019" "DL146AD"
-[1] "25.7281553398058" "DL36HX"
-[1] "26.2135922330097" "DN171RS"
-[1] "26.6990291262136" "DN25LT"
-[1] "27.1844660194175" "DN332BA"
-[1] "27.6699029126214" "DT12JY"
-[1] "28.1553398058252" "DY12HQ"
-[1] "28.6407766990291" "E111NR"
-[1] "29.126213592233" "E11BB"
-[1] "29.6116504854369" "E96SR"
-[1] "30.0970873786408" "EN53DJ"
-[1] "30.5825242718447" "EX25AD"
-[1] "31.0679611650485" "EX314JB"
-[1] "31.5533980582524" "FY38NR"
-[1] "32.0388349514563" "GL13NN"
-[1] "32.5242718446602" "GL537AN"
-[1] "33.0097087378641" "GU167UJ"
-[1] "33.495145631068" "HA13UJ"
-[1] "33.9805825242718" "HD33EA"
-[1] "34.4660194174757" "HG27SX"
-[1] "34.9514563106796" "HP112TT"
-[1] "35.4368932038835" "HP218AL"
-[1] "35.9223300970874" "HP24AD"
-[1] "36.4077669902913" "HR12ER"
-[1] "36.8932038834951" "HU32JZ"
-[1] "37.378640776699" "IG119LX"
-[1] "37.8640776699029" "IP332QZ"
-[1] "38.3495145631068" "IP45PD"
-[1] "38.8349514563107" "KT160PZ"
-[1] "39.3203883495146" "KT198PB"
-[1] "39.8058252427184" "KT27QB"
-[1] "40.2912621359223" "L122AP"
-[1] "40.7766990291262" "L355DR"
-[1] "41.2621359223301" "L634JY"
-[1] "41.747572815534" "L78XP"
-[1] "42.2330097087379" "L97AL"
-[1] "42.7184466019417" "LA144LF"
-[1] "43.2038834951456" "LA14RP"
-[1] "43.6893203883495" "LA97RG"
-[1] "44.1747572815534" "LE15WW"
-[1] "44.6601941747573" "LL137TP"
-[1] "45.1456310679612" "LL185UJ"
-[1] "45.6310679611651" "LL572TP"
-[1] "46.1165048543689" "LN25QY"
-[1] "46.6019417475728" "LS157TR"
-[1] "47.0873786407767" "LS29JT"
-[1] "47.5728155339806" "LS97TF"
-[1] "48.0582524271845" "LU40EP"
-[1] "48.5436893203884" "M208LR"
-[1] "49.0291262135922" "M415SL"
-[1] "49.5145631067961" "M68WH"
-[1] "50" "M85RB"
-[1] "50.4854368932039" "M97AA"
-[1] "50.9708737864078" "ME169QQ"
-[1] "51.4563106796116" "ME207NJ"
-[1] "51.9417475728155" "ME75NY"
-[1] "52.4271844660194" "MK429DJ"
-[1] "52.9126213592233" "MK65LD"
-[1] "53.3980582524272" "N181QX"
-[1] "53.8834951456311" "N195NF"
-[1] "54.3689320388349" "NE298NH"
-[1] "54.8543689320388" "NE340PL"
-[1] "55.3398058252427" "NE46BE"
-[1] "55.8252427184466" "NE77DN"
-[1] "56.3106796116505" "NE96SX"
-[1] "56.7961165048544" "NG174JL"
-[1] "57.2815533980583" "NG318DG"
-[1] "57.7669902912621" "NG72UH"
-[1] "58.252427184466" "NN15BD"
-[1] "58.7378640776699" "NN168UZ"
-[1] "59.2233009708738" "NP77EG"
-[1] "59.7087378640777" "NP92UB"
-[1] "60.1941747572816" "NR23TX"
-[1] "60.6796116504854" "NR316LA"
-[1] "61.1650485436893" "NW107NS"
-[1] "61.6504854368932" "NW32QG"
-[1] "62.1359223300971" "NW95HT"
-[1] "62.621359223301" "OL129QB"
-[1] "63.1067961165049" "OL12JH"
-[1] "63.5922330097087" "OL69RW"
-[1] "64.0776699029126" "OX39DU"
-[1] "64.5631067961165" "PE188NT"
-[1] "65.0485436893204" "PE219QS"
-[1] "65.5339805825243" "PE304ET"
-[1] "66.0194174757282" "PE36DA"
-[1] "66.504854368932" "PL68DH"
-[1] "66.9902912621359" "PO194SE"
-[1] "67.4757281553398" "PO305TG"
-[1] "67.9611650485437" "PO36AQ"
-[1] "68.4466019417476" "PR29HT"
-[1] "68.9320388349515" "PR86PN"
-[1] "69.4174757281553" "RG15AN"
-[1] "69.9029126213592" "RG249NA"
-[1] "70.3883495145631" "RH117DH"
-[1] "70.873786407767" "RM30BE"
-[1] "71.3592233009709" "RM70AG"
-[1] "71.8446601941748" "S445BL"
-[1] "72.3300970873786" "S57BQ"
-[1] "72.8155339805825" "S602UD"
-[1] "73.3009708737864" "S752EP"
-[1] "73.7864077669903" "S810BD"
-[1] "74.2718446601942" "SA28QA"
-[1] "74.7572815533981" "SA312AF"
-[1] "75.2427184466019" "SA612PZ"
-[1] "75.7281553398058" "SE136LH"
-[1] "76.2135922330097" "SE17EH"
-[1] "76.6990291262136" "SE184QH"
-[1] "77.1844660194175" "SE59RS"
-[1] "77.6699029126214" "SG14AB"
-[1] "78.1553398058252" "SK103BL"
-[1] "78.6407766990291" "SK27JE"
-[1] "79.126213592233" "SL24HL"
-[1] "79.6116504854369" "SM51AA"
-[1] "80.0970873786408" "SN36BB"
-[1] "80.5825242718447" "SO166YD"
-[1] "81.0679611650485" "SO226ZB"
-[1] "81.5533980582524" "SP28BJ"
-[1] "82.0388349514563" "SR47TP"
-[1] "82.5242718446602" "SS00RY"
-[1] "83.0097087378641" "SS165NL"
-[1] "83.495145631068" "ST163SA"
-[1] "83.9805825242718" "ST47PX"
-[1] "84.4660194174757" "SW109NH"
-[1] "84.9514563106796" "SW170QT"
-[1] "85.4368932038835" "SW36JJ"
-[1] "85.9223300970874" "SW36NP"
-[1] "86.4077669902913" "SY231ER"
-[1] "86.8932038834951" "SY38XQ"
-[1] "87.378640776699" "TA15DB"
-[1] "87.8640776699029" "TN240LZ"
-[1] "88.3495145631068" "TQ27AA"
-[1] "88.8349514563107" "TR13LQ"
-[1] "89.3203883495146" "TS198PE"
-[1] "89.8058252427184" "TS249AH"
-[1] "90.2912621359223" "TS43BW"
-[1] "90.7766990291262" "TW76AF"
-[1] "91.2621359223301" "UB13HW"
-[1] "91.747572815534" "UB83NN"
-[1] "92.2330097087379" "UB96JH"
-[1] "92.7184466019417" "W120NN"
-[1] "93.2038834951456" "W21NY"
-[1] "93.6893203883495" "W68RF"
-[1] "94.1747572815534" "WA51QG"
-[1] "94.6601941747573" "WC1E6DB"
-[1] "95.1456310679612" "WC1N3JH"
-[1] "95.631067961165" "WD18HB"
-[1] "96.1165048543689" "WF134HS"
-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
->
->
->
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+ variable_df$Relative_humidity<-70 #Here I impose constant and uniform relative humidity
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
->
->
-> proc.time()
- user system elapsed
-192.048 1.958 194.004
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_temperature.R b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_temperature.R
deleted file mode 100644
index 716ea9c789fa3c1d81506a5efb6df741614e33d5..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_temperature.R
+++ /dev/null
@@ -1,250 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# Here we assume constant and uniform humidity
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-30
-width_char<-paste(width)
-const_hum<-"_const_temp_14"
-## Varaible file
-
-
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-#dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-Relative_humidity<-unlist(c(humidity))
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-Maximum_air_temperature<-unlist(c(max_temp))
-
-
-
-######################## Read Linked Data from file ##################
-
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-#PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-#n_Centre<-length(levels(PHE_Centre))
-#i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-#Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-#####This is required to identy all PostCodes in England and Wales ########
-dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_2<-"light"
-
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-
-#Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-#Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
-
-wt<-c(0)
-for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-}
-
-### I this was I can select those postcodes labs wher Campylobacter cases occur
-Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-
-
-########################
-
-
-
-
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-delta_light<-1
-breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
-
-# First find right domain where the values have no NA
-
-
-
-
-time_series<-c()
-
-
-
-for (i in c(1: length(All_PC_s))){
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
- if (length(na.omit(variable_df_check)[,1])!=0){
-
- variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
- variable_df$Maximum_air_temperature<-14 #Here I impose constant and uniform relative humidity
-
- x<-variable_df$Relative_humidity
- y<-variable_df$Maximum_air_temperature
- z<-variable_df$daylength
-
- variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
- var_x_loc_df<-var_x_loc_df_all2
- var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
- var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
- var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-
-
-
- ###############
- # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
- variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-
- variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
- colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
- "PostCode","Date",
- "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
- variable_df_dis<-na.omit(variable_df_dis)
- #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-
- #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
- #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-
- lambda<-variable_df_dis$incidence
- lambda2<-variable_df_dis$prop
- lambda3<-variable_df_dis$counts
-
- #library(Hmisc)
- #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-
- #day<-seq(1:length(variable_df_dis$dates))
- #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-
- #comp_cases<-unlist(lapply(day,cases))
-
-
- comp_cases<-lambda*All_residents_lab$tot[i]
- comp_cases2<-lambda2
- comp_cases3<-lambda3*All_residents_lab$tot[i]
- comp_cases4<-variable_df_dis$Numb_Lab[i]
-
- time_series_1<-
- data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
- colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
- time_series<-rbind(time_series,time_series_1)}
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
-
-}
-
-
-
-
-
-
-write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-
-
diff --git a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_temperature.Rout b/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_temperature.Rout
deleted file mode 100644
index b93cc5b09d0ab73ddb0a47dc9ddfe66fdfd87b76..0000000000000000000000000000000000000000
--- a/Paper_Reconstruct_Campylobacter_environment_light_hum_max_for_rec_constant_temperature.Rout
+++ /dev/null
@@ -1,713 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # Here we assume constant and uniform humidity
->
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-30
-> width_char<-paste(width)
-> const_hum<-"_const_temp_14"
-> ## Varaible file
->
->
->
-> variable<-"humidity"
-> variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> humidity<-variable_df_1[,-c(1,2)]
-> #dates<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%d/%m/%Y")
->
-> dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-> dates<-rep(dates_s,times=length(variable_df_1)-2)
-> All_PC_s<-names(variable_df_1[1,])
-> All_PC_s<-All_PC_s[-c(1,2)]
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
-> humidity<-humidity[-c(1,2),]
-> names(humidity) <- NULL
-> Relative_humidity<-unlist(c(humidity))
->
-> variable<-"max_air_temp"
-> variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-> max_temp<-variable_df_2[,-c(1,2)]
-> max_temp<-max_temp[-c(1,2),]
-> names(max_temp) <- NULL
-> Maximum_air_temperature<-unlist(c(max_temp))
->
->
->
-> ######################## Read Linked Data from file ##################
->
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
->
-> #PHE_Centre<-Env_Campylobacter_data_all2$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-which(levels(PHE_Centre)=='Devon Cornwall and Somerset')
-> #Env_Campylobacter_data_PHE<-subset(Env_Campylobacter_data_all2,Env_Campylobacter_data_all2$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
-> #####This is required to identy all PostCodes in England and Wales ########
-> dates_s<-subset(dates_s, year(dates_s)>=1990 & year(dates_s)<=2015)
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_2<-"light"
->
->
->
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
->
-> #Env_laboratory$PostCode<-as.character(Env_laboratory$PostCode)
-> #Env_Campylobacter_data$PostCode<-as.character(Env_Campylobacter_data$PostCode)
->
-> wt<-c(0)
-> for (i in c(1:length(levels(Env_Campylobacter_data$PostCode)) )){
-+ wt<-c(wt,which(Env_laboratory$PostCode==levels(Env_Campylobacter_data$PostCode)[i]))
-+ print(c(100*i/length(levels(Env_Campylobacter_data$PostCode)),levels(Env_Campylobacter_data$PostCode)[i]) )
-+ }
-[1] "0.485436893203884" "AL74HQ"
-[1] "0.970873786407767" "B152TG"
-[1] "1.45631067961165" "B187QH"
-[1] "1.94174757281553" "B46NH"
-[1] "2.42718446601942" "B714HJ"
-[1] "2.9126213592233" "B757RR"
-[1] "3.39805825242718" "B95SS"
-[1] "3.88349514563107" "B987UB"
-[1] "4.36893203883495" "BA13NG"
-[1] "4.85436893203883" "BA214AT"
-[1] "5.33980582524272" "BB102PQ"
-[1] "5.8252427184466" "BB23HH"
-[1] "6.31067961165049" "BB23LR"
-[1] "6.79611650485437" "BD206TD"
-[1] "7.28155339805825" "BD96RJ"
-[1] "7.76699029126214" "BH152JB"
-[1] "8.25242718446602" "BH77DW"
-[1] "8.7378640776699" "BL40JR"
-[1] "9.22330097087379" "BL96PG"
-[1] "9.70873786407767" "BN112DH"
-[1] "10.1941747572816" "BN212UD"
-[1] "10.6796116504854" "BN25BE"
-[1] "11.1650485436893" "BR68ND"
-[1] "11.6504854368932" "BS105NB"
-[1] "12.1359223300971" "BS161LE"
-[1] "12.621359223301" "BS234TQ"
-[1] "13.1067961165049" "BS28EL"
-[1] "13.5922330097087" "CA27HY"
-[1] "14.0776699029126" "CA288JG"
-[1] "14.5631067961165" "CB22QQ"
-[1] "15.0485436893204" "CB38RE"
-[1] "15.5339805825243" "CF311RQ"
-[1] "16.0194174757282" "CF479DT"
-[1] "16.504854368932" "CF728XR"
-[1] "16.9902912621359" "CF82WW"
-[1] "17.4757281553398" "CH21UL"
-[1] "17.9611650485437" "CM201XQ"
-[1] "18.4466019417476" "CM20YX"
-[1] "18.9320388349515" "CO45JR"
-[1] "19.4174757281553" "CR77YE"
-[1] "19.9029126213592" "CT94AN"
-[1] "20.3883495145631" "CV107DJ"
-[1] "20.873786407767" "CV14FH"
-[1] "21.3592233009709" "CV345BW"
-[1] "21.8446601941748" "CW14QJ"
-[1] "22.3300970873786" "DA146LT"
-[1] "22.8155339805825" "DA28DA"
-[1] "23.3009708737864" "DE12QY"
-[1] "23.7864077669903" "DE130RB"
-[1] "24.2718446601942" "DE223NE"
-[1] "24.7572815533981" "DH15TW"
-[1] "25.2427184466019" "DL146AD"
-[1] "25.7281553398058" "DL36HX"
-[1] "26.2135922330097" "DN171RS"
-[1] "26.6990291262136" "DN25LT"
-[1] "27.1844660194175" "DN332BA"
-[1] "27.6699029126214" "DT12JY"
-[1] "28.1553398058252" "DY12HQ"
-[1] "28.6407766990291" "E111NR"
-[1] "29.126213592233" "E11BB"
-[1] "29.6116504854369" "E96SR"
-[1] "30.0970873786408" "EN53DJ"
-[1] "30.5825242718447" "EX25AD"
-[1] "31.0679611650485" "EX314JB"
-[1] "31.5533980582524" "FY38NR"
-[1] "32.0388349514563" "GL13NN"
-[1] "32.5242718446602" "GL537AN"
-[1] "33.0097087378641" "GU167UJ"
-[1] "33.495145631068" "HA13UJ"
-[1] "33.9805825242718" "HD33EA"
-[1] "34.4660194174757" "HG27SX"
-[1] "34.9514563106796" "HP112TT"
-[1] "35.4368932038835" "HP218AL"
-[1] "35.9223300970874" "HP24AD"
-[1] "36.4077669902913" "HR12ER"
-[1] "36.8932038834951" "HU32JZ"
-[1] "37.378640776699" "IG119LX"
-[1] "37.8640776699029" "IP332QZ"
-[1] "38.3495145631068" "IP45PD"
-[1] "38.8349514563107" "KT160PZ"
-[1] "39.3203883495146" "KT198PB"
-[1] "39.8058252427184" "KT27QB"
-[1] "40.2912621359223" "L122AP"
-[1] "40.7766990291262" "L355DR"
-[1] "41.2621359223301" "L634JY"
-[1] "41.747572815534" "L78XP"
-[1] "42.2330097087379" "L97AL"
-[1] "42.7184466019417" "LA144LF"
-[1] "43.2038834951456" "LA14RP"
-[1] "43.6893203883495" "LA97RG"
-[1] "44.1747572815534" "LE15WW"
-[1] "44.6601941747573" "LL137TP"
-[1] "45.1456310679612" "LL185UJ"
-[1] "45.6310679611651" "LL572TP"
-[1] "46.1165048543689" "LN25QY"
-[1] "46.6019417475728" "LS157TR"
-[1] "47.0873786407767" "LS29JT"
-[1] "47.5728155339806" "LS97TF"
-[1] "48.0582524271845" "LU40EP"
-[1] "48.5436893203884" "M208LR"
-[1] "49.0291262135922" "M415SL"
-[1] "49.5145631067961" "M68WH"
-[1] "50" "M85RB"
-[1] "50.4854368932039" "M97AA"
-[1] "50.9708737864078" "ME169QQ"
-[1] "51.4563106796116" "ME207NJ"
-[1] "51.9417475728155" "ME75NY"
-[1] "52.4271844660194" "MK429DJ"
-[1] "52.9126213592233" "MK65LD"
-[1] "53.3980582524272" "N181QX"
-[1] "53.8834951456311" "N195NF"
-[1] "54.3689320388349" "NE298NH"
-[1] "54.8543689320388" "NE340PL"
-[1] "55.3398058252427" "NE46BE"
-[1] "55.8252427184466" "NE77DN"
-[1] "56.3106796116505" "NE96SX"
-[1] "56.7961165048544" "NG174JL"
-[1] "57.2815533980583" "NG318DG"
-[1] "57.7669902912621" "NG72UH"
-[1] "58.252427184466" "NN15BD"
-[1] "58.7378640776699" "NN168UZ"
-[1] "59.2233009708738" "NP77EG"
-[1] "59.7087378640777" "NP92UB"
-[1] "60.1941747572816" "NR23TX"
-[1] "60.6796116504854" "NR316LA"
-[1] "61.1650485436893" "NW107NS"
-[1] "61.6504854368932" "NW32QG"
-[1] "62.1359223300971" "NW95HT"
-[1] "62.621359223301" "OL129QB"
-[1] "63.1067961165049" "OL12JH"
-[1] "63.5922330097087" "OL69RW"
-[1] "64.0776699029126" "OX39DU"
-[1] "64.5631067961165" "PE188NT"
-[1] "65.0485436893204" "PE219QS"
-[1] "65.5339805825243" "PE304ET"
-[1] "66.0194174757282" "PE36DA"
-[1] "66.504854368932" "PL68DH"
-[1] "66.9902912621359" "PO194SE"
-[1] "67.4757281553398" "PO305TG"
-[1] "67.9611650485437" "PO36AQ"
-[1] "68.4466019417476" "PR29HT"
-[1] "68.9320388349515" "PR86PN"
-[1] "69.4174757281553" "RG15AN"
-[1] "69.9029126213592" "RG249NA"
-[1] "70.3883495145631" "RH117DH"
-[1] "70.873786407767" "RM30BE"
-[1] "71.3592233009709" "RM70AG"
-[1] "71.8446601941748" "S445BL"
-[1] "72.3300970873786" "S57BQ"
-[1] "72.8155339805825" "S602UD"
-[1] "73.3009708737864" "S752EP"
-[1] "73.7864077669903" "S810BD"
-[1] "74.2718446601942" "SA28QA"
-[1] "74.7572815533981" "SA312AF"
-[1] "75.2427184466019" "SA612PZ"
-[1] "75.7281553398058" "SE136LH"
-[1] "76.2135922330097" "SE17EH"
-[1] "76.6990291262136" "SE184QH"
-[1] "77.1844660194175" "SE59RS"
-[1] "77.6699029126214" "SG14AB"
-[1] "78.1553398058252" "SK103BL"
-[1] "78.6407766990291" "SK27JE"
-[1] "79.126213592233" "SL24HL"
-[1] "79.6116504854369" "SM51AA"
-[1] "80.0970873786408" "SN36BB"
-[1] "80.5825242718447" "SO166YD"
-[1] "81.0679611650485" "SO226ZB"
-[1] "81.5533980582524" "SP28BJ"
-[1] "82.0388349514563" "SR47TP"
-[1] "82.5242718446602" "SS00RY"
-[1] "83.0097087378641" "SS165NL"
-[1] "83.495145631068" "ST163SA"
-[1] "83.9805825242718" "ST47PX"
-[1] "84.4660194174757" "SW109NH"
-[1] "84.9514563106796" "SW170QT"
-[1] "85.4368932038835" "SW36JJ"
-[1] "85.9223300970874" "SW36NP"
-[1] "86.4077669902913" "SY231ER"
-[1] "86.8932038834951" "SY38XQ"
-[1] "87.378640776699" "TA15DB"
-[1] "87.8640776699029" "TN240LZ"
-[1] "88.3495145631068" "TQ27AA"
-[1] "88.8349514563107" "TR13LQ"
-[1] "89.3203883495146" "TS198PE"
-[1] "89.8058252427184" "TS249AH"
-[1] "90.2912621359223" "TS43BW"
-[1] "90.7766990291262" "TW76AF"
-[1] "91.2621359223301" "UB13HW"
-[1] "91.747572815534" "UB83NN"
-[1] "92.2330097087379" "UB96JH"
-[1] "92.7184466019417" "W120NN"
-[1] "93.2038834951456" "W21NY"
-[1] "93.6893203883495" "W68RF"
-[1] "94.1747572815534" "WA51QG"
-[1] "94.6601941747573" "WC1E6DB"
-[1] "95.1456310679612" "WC1N3JH"
-[1] "95.631067961165" "WD18HB"
-[1] "96.1165048543689" "WF134HS"
-[1] "96.6019417475728" "WF14DG"
-[1] "97.0873786407767" "WF81PL"
-[1] "97.5728155339806" "WN12NN"
-[1] "98.0582524271845" "WR13AS"
-[1] "98.5436893203884" "WS29PS"
-[1] "99.0291262135922" "WV100QP"
-[1] "99.5145631067961" "YO126QL"
-[1] "100" "YO318HE"
->
-> ### I this was I can select those postcodes labs wher Campylobacter cases occur
-> Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
->
->
->
-> ########################
->
->
->
->
->
-> All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> All_residents<-sum(All_residents_lab$tot)
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
->
->
-> ###################
->
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
-> delta_light<-1
-> breaks_hum<-seq(max(min(floor(na.omit(Env_laboratory$Relative_humidity)))-10,0),max(floor(na.omit(Env_laboratory$Relative_humidity)))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(floor(na.omit(Env_laboratory$Minimum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Minimum_air_temperature)))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(floor(na.omit(Env_laboratory$Maximum_air_temperature)))-2, max(floor(na.omit(Env_laboratory$Maximum_air_temperature)))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_light<-seq(max(min(floor(na.omit(Env_laboratory$daylength)))-1,0),max(floor(na.omit(Env_laboratory$daylength)))+1,by=delta_light)
->
-> # First find right domain where the values have no NA
->
->
->
->
-> time_series<-c()
->
->
->
-> for (i in c(1: length(All_PC_s))){
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ variable_df_check<-data.frame(variable_df$daylength,variable_df$Relative_humidity,variable_df$Maximum_air_temperature)
-+ if (length(na.omit(variable_df_check)[,1])!=0){
-+
-+ variable_df_dis_int<-subset(variable_df,year(variable_df$Date)>=1990 & year(variable_df$Date)<=2015)
-+ variable_df$Maximum_air_temperature<-14 #Here I impose constant and uniform relative humidity
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_dis_int$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_dis_int$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_dis_int$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ var_x_loc_df$Relative_humidity<-(breaks_hum[findInterval(var_x_loc_df$Relative_humidity, breaks_hum)])##floor(var_x_loc_df$Relative_humidity)
-+ var_x_loc_df$daylength<-(breaks_light[findInterval(var_x_loc_df$daylength, breaks_light)])##floor(var_x_loc_df$light)
-+ var_x_loc_df$Maximum_air_temperature<-(breaks_max_temp[findInterval(var_x_loc_df$Maximum_air_temperature, breaks_max_temp)])##floor(var_x_loc_df$breaks)
-+
-+
-+
-+ ###############
-+ # variable_df_dis_int$Maximum_air_temperature<-floor(variable_df_dis_int$Maximum_air_temperature)
-+ variable_df_dis<-merge(variable_df_dis_int,var_x_loc_df, by=c("Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ variable_df_dis<-variable_df_dis[,c(1:5,11:13,15,16,17:19)]
-+ colnames(variable_df_dis)<-c(variable_y, variable_x,variable,
-+ "PostCode","Date",
-+ "breaks","prop","incidence","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+ variable_df_dis<-na.omit(variable_df_dis)
-+ #variable_df_dis<-merge(variable_df_dis_int,variable_df_5_dis, by=c("dates","Relative_humidity","Maximum_air_temperature","daylength") )
-+
-+ #variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ #variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ #variable_df_dis<-variable_df_dis[,c(1:3,5:12)]################from here
-+ #colnames(variable_df_dis)<-c("dates",variable, variable_y,variable_x,"prop","incidence","month","counts","counts_tot","residents","residents_tot","Numb_Lab")
-+
-+
-+ lambda<-variable_df_dis$incidence
-+ lambda2<-variable_df_dis$prop
-+ lambda3<-variable_df_dis$counts
-+
-+ #library(Hmisc)
-+ #plot(as.Date(as.character(variable_df_dis$dates)),lambda,type="l")
-+
-+ #day<-seq(1:length(variable_df_dis$dates))
-+ #day<-day(variable_df_dis$dates)+yearDays(variable_df_dis$dates)*(year(variable_df_dis$dates)-year(variable_df_dis$dates[1])+1)+1
-+
-+ #comp_cases<-unlist(lapply(day,cases))
-+
-+
-+ comp_cases<-lambda*All_residents_lab$tot[i]
-+ comp_cases2<-lambda2
-+ comp_cases3<-lambda3*All_residents_lab$tot[i]
-+ comp_cases4<-variable_df_dis$Numb_Lab[i]
-+
-+ time_series_1<-
-+ data.frame(variable_df_dis$Date,comp_cases,comp_cases2,comp_cases3,comp_cases4,lambda, rep(All_PC_s[i],times=length(variable_df_dis$Date)))
-+ colnames(time_series_1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab")
-+ time_series<-rbind(time_series,time_series_1)}
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
->
->
->
->
->
-> write.table(time_series,paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,"_",width_char,const_hum,"_Simulated_for_reconstriction_uniform_separation_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
->
->
-> proc.time()
- user system elapsed
-195.104 1.845 196.951
diff --git a/Paper_Taylor_Campylobacter_environment_hum_max_for_rec_delay_original.R b/Paper_Taylor_Campylobacter_environment_hum_max_for_rec_delay_original.R
deleted file mode 100644
index 3fe04f43e05bb6df31fd7ff134c244a10be320e7..0000000000000000000000000000000000000000
--- a/Paper_Taylor_Campylobacter_environment_hum_max_for_rec_delay_original.R
+++ /dev/null
@@ -1,482 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# this to calculate delay
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-60
-width_char<-paste(width)
-n_seas<-1
-
-variable_x<-"max_air_temp"
-variable_y<-"humidity"
-variable_z<-"light"
-
-
-
-## Variable files
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-#colnames(Env_Campylobacter_data_all2)<-c("PostCode","PHE_Centre_Name","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-#colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory_weekly[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<2010)
-
-dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2010-01-31"), by = "day")
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2010)
-
-Env_laboratory_PHE<-Env_laboratory
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-All_residents<-sum(All_residents_lab$tot)
-
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable,"_",variable_x,"_",width_char,"_n_seas_",n_seas,"_for_rec.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-names(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-###################
-
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-10
-delta_wind<-2
-breaks_hum<-floor(seq(max(min(na.omit(Env_laboratory$humidity))-10,0),max(na.omit(Env_laboratory$humidity))+10,by=delta_hum)) #i
-breaks_min_temp<-floor(seq(min(na.omit(Env_laboratory$min_temp))-2, max(na.omit(Env_laboratory$min_temp))+2,by=delta_temp))
-breaks_max_temp<-floor(seq(min(na.omit(Env_laboratory$max_temp))-2, max(na.omit(Env_laboratory$max_temp))+2,by=delta_temp))
-
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$rain)),0), max(na.omit(Env_laboratory$rain))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$wind))-2,0),max(na.omit(Env_laboratory$wind))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$cum_rain)),0), max(na.omit(Env_laboratory$cum_rain))+2,by=delta_cum_rain)
-breaks_mean_temp<-seq(min(na.omit(Env_laboratory$min_temp))-2,max(na.omit(Env_laboratory$max_temp))+2,by=delta_temp)
-
-
-
-
-i_hum_min<-max(which(breaks_hum<=min(na.omit(Env_Campylobacter_data$humidity))))
-i_hum_max<-max(which(breaks_hum<=max(na.omit(Env_Campylobacter_data$humidity))))
-
-i_min_temp_min<-max(which(breaks_min_temp<=min(na.omit(Env_Campylobacter_data$min_temp))))
-i_min_temp_max<-max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$min_temp))))
-
-i_max_temp_min<-max(which(breaks_max_temp<=min(na.omit(Env_Campylobacter_data$max_temp))))
-i_max_temp_max<-max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$max_temp))))
-
-i_rain_min<-max(which(breaks_rain<=min(na.omit(Env_Campylobacter_data$rain))))
-i_rain_max<-max(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$rain))))
-
-i_cum_rain_min<-max(which(breaks_cum_rain<=min(na.omit(Env_Campylobacter_data$cum_rain))))
-i_cum_rain_max<-max(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$cum_rain))))
-
-i_wind_min<-max(which(breaks_wind<=min(na.omit(Env_Campylobacter_data$wind))))
-i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$wind))))
-
-
-
-
-
-if (variable=="max_air_temp"){
-
- breaks_var<-breaks_max_temp
- i_var_min<-i_max_temp_min
- i_var_max<-i_max_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$max_temp
- Env_laboratory_var<-Env_laboratory$max_temp
-}
-if (variable_x=="max_air_temp"){
-
- i_var_x_min<-i_max_temp_min
- i_var_x_max<-i_max_temp_max
- breaks_var_x<-breaks_max_temp
-}
-
-if (variable=="min_air_temp"){
-
- breaks_var<-breaks_min_temp
- i_var_min<-i_min_temp_min
- i_var_max<-i_min_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$min_temp
- Env_laboratory_var<-Env_laboratory$min_temp
-}
-if (variable_x=="min_air_temp"){
-
- i_var_x_min<-i_min_temp_min
- i_var_x_max<-i_min_temp_max
- breaks_var_x<-breaks_min_temp
-}
-
-
-if (variable=="humidity"){
-
- breaks_var<-breaks_hum
- i_var_min<-i_hum_min
- i_var_max<-i_hum_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$humidity
- Env_laboratory_var<-Env_laboratory$humidity
-}
-if (variable_x=="humidity"){
-
- i_var_x_min<-i_hum_min
- i_var_x_max<-i_hum_max
- breaks_var_x<-breaks_hum
-}
-
-
-
-if (variable=="mean_temp"){
-
- breaks_var<-breaks_mean_temp
- i_var_min<-i_mean_temp_min
- i_var_max<-i_mean_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$mean_temp
- Env_laboratory_var<-Env_laboratory$mean_temp
-}
-if (variable_x=="mean_temp"){
-
- i_var_x_min<-i_mean_temp_min
- i_var_x_max<-i_mean_temp_max
- breaks_var_x<-breaks_mean_temp
-}
-
-if (variable=="rain"){
-
- breaks_var<-breaks_rain
- i_var_min<-i_rain_min
- i_var_max<-i_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$rain
- Env_laboratory_var<-Env_laboratory$rain
-}
-if (variable_x=="rain"){
-
- i_var_x_min<-i_rain_min
- i_var_x_max<-i_rain_max
- breaks_var_x<-breaks_rain
-}
-
-
-if (variable=="cum_rain"){
-
- breaks_var<-breaks_cum_rain
- i_var_min<-i_cum_rain_min
- i_var_max<-i_cum_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$cum_rain
- Env_laboratory_var<-Env_laboratory$cum_rain
-}
-if (variable_x=="cum_rain"){
-
- i_var_x_min<-i_cum_rain_min
- i_var_x_max<-i_cum_rain_max
- breaks_var_x<-breaks_cum_rain
-}
-
-
-
-if (variable=="wind"){
-
- breaks_var<-breaks_wind
- i_var_min<-i_wind_min
- i_var_max<-i_wind_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$wind
- Env_laboratory_var<-Env_laboratory$wind
-}
-if (variable_x=="wind"){
-
- i_var_x_min<-i_wind_min
- i_var_x_max<-i_wind_max
- breaks_var_x<-breaks_wind
-}
-
-
-
-D_eta_var_x<-function(i_var,i_var_x)
-{
-
-
-var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt1<-subset(var_x_loc_df,var_x_loc_df$humidity>=breaks_var[i_var])
- } else {
- Yt1<-subset(var_x_loc_df,var_x_loc_df$humidity>=breaks_var[i_var] & var_x_loc_df$humidity<breaks_var[i_var+1])
- }
-
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$max_air_temp>=breaks_var_x[i_var_x])
- } else {
- Yt2<-subset(Yt1,Yt1$max_air_temp>=breaks_var_x[i_var_x] & Yt1$max_air_temp<breaks_var_x[i_var_x+1])
- }
-
-
- if(is.na(breaks_var_x[i_var_x+2])==TRUE)
- {
- Yt3<-subset(Yt1,Yt1$max_air_temp>=breaks_var_x[i_var_x+1])
- } else {
- Yt3<-subset(Yt1,Yt1$max_air_temp>=breaks_var_x[i_var_x+1] & Yt1$max_air_temp<breaks_var_x[i_var_x+2])
- }
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_x<- (mean(Yt3$eta- Yt2$eta))/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_x<- 0.5*mean(Yt3$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_x<- 0.5*mean(Yt2$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_x<-NA
- }
-
- return(D_eta_D_x)
- }
-
-
-
-D_eta_var<-function(i_var,i_var_x)
-{
-
-
-var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt1<-subset(var_x_loc_df,var_x_loc_df$max_air_temp>=breaks_var_x[i_var_x])
- } else {
- Yt1<-subset(var_x_loc_df,var_x_loc_df$max_air_temp>=breaks_var_x[i_var_x] & var_x_loc_df$max_air_temp<breaks_var_x[i_var_x+1])
- }
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt2<-subset( Yt1, Yt1$humidity>=breaks_var[i_var])
- } else {
- Yt2<-subset( Yt1, Yt1$humidity>=breaks_var[i_var] & Yt1$humidity<breaks_var[i_var+1])
- }
-
-if(is.na(breaks_var[i_var+2])==TRUE)
-{
- Yt3<-subset( Yt1, Yt1$humidity>=breaks_var[i_var+1])
-} else {
- Yt3<-subset( Yt1, Yt1$humidity>=breaks_var[i_var+1] & Yt1$humidity<breaks_var[i_var+2])
-}
-
-
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_v<- mean(Yt3$eta- Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_v<- 0.5*mean(Yt3$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_v<- 0.5*mean(Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_v<-NA
- }
-
- return(D_eta_D_v)
- }
-
-
-
-
-
-df_all<-c()
-#i_var<-seq(i_var_min,i_var_max)
-#i_var_x<-seq(i_var_x_min,i_var_x_max)
-
- for (j in c(1: n_seas)){
-
-var_x_loc_df<-subset(var_x_loc_df_all2,var_x_loc_df_all2$month==j)
-
-for (i_var in c(seq(i_var_min,i_var_max))){
-for (i_var_x in c(seq(i_var_x_min,i_var_x_max))){
-
-
-
-#df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-#df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-
-df1<-data.frame(breaks_hum[i_var],breaks_max_temp[i_var_x],D_eta_var_x(i_var,i_var_x),D_eta_var(i_var,i_var_x),j )
-colnames(df1)<-c("humidity","max_air_temp","D_eta_D_max_temp","D_eta_D_hum","month")
-df_all<-rbind(df_all,df1)
-#mapply(D_eta_var_x,i_var, MoreArgs =list(i_var_x))
-}
-
-}
-}
-
-
-time_series<-c()
-
-for (i in c(1: length(All_PC_s))){
- #for (i in c(1: 15)){
-
-
-
- for (j in c(1: n_seas)){
-
- n_months<-12/n_seas
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i] & month(Env_laboratory$Date)>(j-1)*n_months & month(Env_laboratory$Date)<=(j)*n_months)
- if (length(variable_df[,1])!=0){
-
-
-
-Delta_T<-diff(variable_df$max_temp)
-Delta_RH<-diff(variable_df$humidity)
-Delta_var<-data.frame(variable_df$Date[-1],Delta_T,Delta_RH)
-colnames(Delta_var)<-c("dates","diff_max_temp","diff_humidity")
-
-x<-variable_df$humidity
-y<-variable_df$max_temp
-
-variable_df_1_dis<-data.frame(variable_df$Date,variable_df$humidity)
-variable_df_2_dis<-data.frame(variable_df$Date,variable_df$max_temp)
-colnames(variable_df_1_dis)<-c("dates","humidity")
-colnames(variable_df_2_dis)<-c("dates","max_air_temp")
-
- variable_df_1_dis$dates<-as.Date(as.character((variable_df_1_dis$dates)))
- variable_df_2_dis$dates<-as.Date(as.character((variable_df_2_dis$dates)))
-
- variable_df_1_dis<-subset(variable_df_1_dis,year(variable_df_1_dis$dates)>=2001 & year(variable_df_1_dis$dates)<2012)
- variable_df_2_dis<-subset(variable_df_2_dis,year(variable_df_2_dis$dates)>=2001 & year(variable_df_2_dis$dates)<2012)
-
-
- variable_df_1_dis$humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_2_dis$max_air_temp<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-
-
-
-# var_x_loc_df<-subset(var_x_loc_df_all2,var_x_loc_df_all2$month==j)
-# var_x_loc_df$humidity<-floor(var_x_loc_df$humidity)
-# var_x_loc_df$max_air_temp<-floor(var_x_loc_df$breaks)
- df<-subset(df_all,df_all$month==j)
-
- variable_df_3_dis<-merge(variable_df_1_dis,df, by="humidity")
-
- #colnames(var_x_loc_df)[1]<-"max_air_temp_mids"
- #colnames(var_x_loc_df)[2]<-"max_air_temp"
-
- variable_df_4_dis<-merge(variable_df_2_dis,df, by="max_air_temp")
-
- variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
- variable_df_3_dis<-variable_df_3_dis[order(variable_df_3_dis$dates),]
- variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
- variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
-
- variable_df_3_dis$dates<-as.factor(variable_df_3_dis$dates)
- variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
- variable_df_dis<-merge(variable_df_3_dis,variable_df_4_dis, by=c("dates","max_air_temp","humidity") )
-
- variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- variable_df_dis$dates<-as.factor(variable_df_dis$dates)
- Delta_var$dates<-as.factor(Delta_var$dates)
- variable_df_Taylor<-merge(variable_df_dis,Delta_var, by=c("dates"))
-
- variable_df_Taylor<-variable_df_Taylor[,c(1:6,10:11)]
- colnames(variable_df_Taylor)<-c("dates","month",variable, variable_x,"D_eta_D_max_temp","D_eta_D_hum","diff_max_temp","diff_humidity")
-
-
- lambda_temp<-variable_df_Taylor$D_eta_D_max_temp*variable_df_Taylor$diff_max_temp
- lambda_hum<-variable_df_Taylor$D_eta_D_hum*variable_df_Taylor$diff_humidity
-
-
-
-
- time_series_1<-
- data.frame(variable_df_Taylor$dates,lambda_temp,lambda_hum, rep(All_PC_s[i],times=length(variable_df_Taylor$dates)),
- variable_df_Taylor$D_eta_D_max_temp,variable_df_Taylor$diff_max_temp,
- variable_df_Taylor$D_eta_D_hum,variable_df_Taylor$diff_humidity,
- rep(j,times=length(variable_df_Taylor$dates)))
- colnames(time_series_1)<-c("Date","Contr_temp","Contr_hum","Lab","seas","delta_eta_temp","delta_temp","delta_eta_hum","delta_hum")
- time_series<-rbind(time_series,time_series_1)
- }
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
- }
-}
-
-#time_series1<-subset(time_series,time_series$Lab=="B152TG")
-#time_series1$Date<-as.Date(as.character(time_series1$Date))
-#time_series1$year<-as.factor(year(as.Date(as.character(time_series1$Date))))
-#time_series1<-subset(time_series1,time_series1$year=="2005")
-
-#time_series1<-na.omit(time_series1)
-##temp_contr<-cumsum(time_series1$Contr_temp)
-#temp_hum<-cumsum(time_series1$Contr_hum)
-#
-#df2<-data.frame(as.Date(as.character(time_series1$Date)),temp_contr,temp_hum)
-#colnames(df2)<-c("Date","contr","humidity")
-#temp_contr<-ddply(time_series1,~year,summarise,Cum_Cases=cumsum(Contr_temp))
-#time_series<-na.omit(time_series)
-#time_series$temp_contr<-cumsum(time_series$Contr_temp)
-#time_series$temp_hum<-cumsum(time_series$Contr_hum)
-#time_series$yday<-yday(time_series$Date)
-#time_series$week<-week(time_series$Date)
-#time_series$month<-month(time_series$Date)
-#time_series$Lab<-as.factor(time_series$Lab)
-
-#time_series_lab<-ddply(time_series,~Date,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~yday,summarise,tot=mean(Contr_temp))
-
-#time_series_lab<-ddply(time_series,~week,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~week,summarise,tot=mean(Contr_hum))
-
-#time_series_lab<-ddply(time_series,~month,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~month,summarise,tot=mean(Contr_hum))
-#time_series_lab2<-ddply(time_series,~Date,summarise,tot=mean(Contr_hum))
-
-write.table(time_series,paste("../../Data_Base/Cases/Taylor_contribution_Time_series_",variable,"_",variable_x,width_char,"_n_seas_",n_seas,".csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
diff --git a/Paper_plot_Simulated_Reconstruct_3_var.R b/Paper_plot_Simulated_Reconstruct_3_var.R
deleted file mode 100644
index 93d2e9baaee6df5ee1092dc1da5444d9e6022bac..0000000000000000000000000000000000000000
--- a/Paper_plot_Simulated_Reconstruct_3_var.R
+++ /dev/null
@@ -1,374 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# The code uses old MEDMI data (not corrected for altitude) and analysis done on regular division of the range of the environemtal varaibles rather than quantile.
-
-
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(zoo)
-library(plyr)
-
-
-width<-14
-width_char<-paste(width)
-#situation<-"_const_hum_70"
-situation<-"_Av_Glous_Wilt"
-#situation<-""
-
-variable<-"humidity"
-variable_df_1<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-humidity<-variable_df_1[,-c(1,2)]
-
-dates_s<-as.Date(as.character(variable_df_1[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_1)-2)
-All_PC_s<-names(variable_df_1[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-humidity<-humidity[-c(1,2),]
-names(humidity) <- NULL
-humidity<-unlist(c(humidity))
-
-
-variable<-"max_air_temp"
-variable_df_2<-read.csv(paste("../../Data_Base/OPIE_data_base/",variable,".csv",sep=""))
-max_temp<-variable_df_2[,-c(1,2)]
-max_temp<-max_temp[-c(1,2),]
-names(max_temp) <- NULL
-max_temp<-unlist(c(max_temp))
-
-
-
-
-
-
-variable_x<-"max_air_temp"
-variable_y<-"humidity"
-variable<-"light"
-#variable<-"rain"
-n_seas<-1
-
-
-#Env_laboratory<-read.csv(paste("../DataBase/Cases_Environment/Laboratory_",width_char,".csv",sep=""))
-#Env_laboratory<-Env_laboratory[,-1]
-#colnames(Env_laboratory)<-c("PostCode","Date","humidity","max_temp","min_temp","rain","cum_rain","wind","residents")
-#Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<2015)
-
-
-######################## Catchment areas #################
-
-population_df<-read.csv(paste("../../Data_Base/Catchment_areas/LSOA2011Population_SumByLabs.csv",sep=""))
-colnames(population_df)<-c("Lab","col2","col3","residents_Lab")
-
-
-#var_x_loc_df<-read.csv(paste("../DataBase/Cases_Environment/",variable,"_",variable_x,"_",width_char,"_n_seas",n_seas,".csv",sep=""))
-#var_x_loc_df<-var_x_loc_df[,-1]
-#names(var_x_loc_df)<-c(variable_x,"breaks","prop","incidence","month",variable,"counts","counts_tot","residents","residents_tot")
-
-
-#norm<-sum(na.omit(var_x_loc_df$incidence))
-#var_x_loc_df$incidence<-100*var_x_loc_df$incidence/norm
-#var_x_loc_df<-na.omit(var_x_loc_df)
-
-
-
-
-
-real_cases_all<-read.csv("../../Data_Base/Cases/Campylobacter_Simulated_Data_final_UK.csv")
-real_cases_all<-real_cases_all[,-1]
-colnames(real_cases_all)<-c("Count","Adjusted","Spec_Date", "POSTCODE","Date","Simulation")
-
-real_cases<-ddply(real_cases_all,~Date,summarise,mean=sum(Count))
-colnames(real_cases)<-c("Date","Cases")
-
-
-#real_cases_all$Date<-as.Date(as.character(real_cases_all$Date),format="%d/%m/%Y")
-#real_cases<-real_cases_all[-c(1:13),]
-
-real_cases<-subset(real_cases,year(real_cases$Date)>=1990 & year(real_cases$Date)<2010)
-
-
-
-time_series1<-read.csv(paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,width_char,"_n_seas_",n_seas,"_Simulated_for_rec_test.csv",sep=""))
-#time_series1<-read.csv(paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_y,"_",variable_x,"_",width_char,"_n_seas",n_seas,"_Simulated_for_rec_multiple_delays.csv",sep=""))
-
-#time_series1<-read.csv(paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_y,"_",variable_x,"_",width_char,"_n_seas",n_seas,"for_rec_multiple_delays.csv",sep=""))
-#time_series1<-read.csv(paste("../../Data_Base/Cases/Time_series_",variable,"_",variable_x,"_",variable_y,width_char,"_n_seas_",n_seas,"for_rec",situation,".csv",sep=""))
-
-
-time_series1<-time_series1[,-1]
-colnames(time_series1)<-c("Date","Cases","Cases2","Cases3","Cases4","Lambda","Lab","seas")
-time_series1$Date<-as.Date(as.character(time_series1$Date))
-
-time_series<-merge(population_df,time_series1,by="Lab")
-#time_series$Cases4<-time_series$Lambda*time_series$residents_Lab
-
-
-time_series<-subset(time_series,year(time_series$Date)>=1990 & year(time_series$Date)<2010)
-time_series<-na.omit(time_series)
-
-
-##############Entire Timeseries #################
-time_series$Cases5<-time_series$Cases*time_series$Cases4
-time_series$Date<-as.Date(as.character(time_series$Date))
-time_series<-time_series[order(time_series$Date),]
-library(zoo)
-#time_series$Cases<-sum(time_series$Cases)
-
-time_series$Date<-as.factor(time_series$Date)
-ts_roll_mean<-rollmean(time_series$Cases5,7,align = "right")
-#ts_roll_mean<-rollmean(time_series$Depleted,7)
-time_series<-time_series[-c(1:6),]
-time_series$rolling_mean<-ts_roll_mean
-
-time_series_mean<-ddply(time_series,~Date,summarise,mean=mean(rolling_mean))
-#time_series_mean<-ddply(time_series,~week(time_series$Date),summarise,mean=mean(Cases5))
-
-time_series_mean[,1]<-as.Date(as.character(time_series_mean[,1]))
-time_series_quantile<-ddply(time_series,~Date, function (x) quantile(x$Cases5, c(.25,.5,.75)))
-time_series_summary<-data.frame(time_series_mean,time_series_quantile[,-1],rep("Model",times=length(time_series_mean[,1])))
-colnames(time_series_summary)<-c("Date","Mean","f_quant","median","s_quant","source")
-
-real_cases_summary<-data.frame(as.Date(real_cases$Date),as.numeric(real_cases$Cases),
- rep("NA",times=length(real_cases[,1])),
- rep("NA",times=length(real_cases[,1])),
- rep("NA",times=length(real_cases[,1])),
- rep("Cases",times=length(real_cases[,1])))
-colnames(real_cases_summary)<-c("Date","Mean","f_quant","median","s_quant","source")
-
-#time_series_mean<-ddply(time_series,~Date,summarise,mean=mean(rolling_mean),sd=sd(rolling_mean))
-#time_series_mean[,1]<-as.Date(as.character(time_series_mean[,1]))
-#time_series_quantile<-ddply(time_series,~Date, function (x) quantile(x$rolling_mean, c(.25,.5,.75)))
-#time_series_summary<-cbind(time_series_mean,time_series_quantile[,-1])
-
-#end_point<-365
-#end_point<-5*365
-#plot(time_series_mean$Date[1:5000],time_series_mean$mean[1:5000]/sum(time_series_mean$mean[1:5000]),type="l")
-#lines(as.Date(as.character(real_cases$Date[1:5000]),format="%d/%m/%Y"),as.numeric(real_cases$Cases[1:5000])/(sum(as.numeric(real_cases$Cases[1:5000]))),col="red",lwd = 2)
-#time_series_summary<-time_series_summary[c(1:end_point),]
-#real_cases_summary<-real_cases_summary[c(1:end_point),]
-
-
-time_series_summary[,c(2:5)]<-time_series_summary[,c(2:5)]
-real_cases_summary$Mean<-real_cases_summary$Mean
-
-#time_series_all<-rbind(time_series_summary[c(1:end_point),c(1,2,6)],real_cases_summary[c(1:end_point),c(1,2,6)])
-time_series_all<-rbind(time_series_summary[,c(1,2,6)],real_cases_summary[,c(1,2,6)])
-
-
-pal<-wes_palette("Cavalcanti")
-time_series_plot<-ggplot(time_series_all,aes(x=Date,y=Mean,colour=source))
-#time_series_plot<-time_series_plot+scale_color_brewer(palette=pal)
-#time_series_plot<-time_series_plot+geom_line(data=real_cases_summary)
-time_series_plot<-time_series_plot+geom_line(size=0.75)
-#time_series_plot<-time_series_plot+scale_color_manual(values=c( "#E69F00", "#56B4E9"))
-time_series_plot<-time_series_plot+scale_color_manual(values=wes_palette(n=2, name="Cavalcanti"))
-
-time_series_plot<-time_series_plot+ xlab("Date")+ ylab("Campylobacter Cases")+scale_y_continuous(limit=c(0,600))
-#time_series_plot<-time_series_plot+ scale_x_date(breaks=c(
-# as.Date("2000-01-01"),
-# as.Date("2001-01-01"),
-# as.Date("2002-01-01"),
-# as.Date("2003-01-01"),
-# as.Date("2004-01-01"),
-# as.Date("2005-01-01"),
-# as.Date("2006-01-01"),
-# as.Date("2007-01-01"),
-# as.Date("2008-01-01"),
-# as.Date("2009-01-01"),
-# as.Date("2010-01-01"),
- # as.Date("2011-01-01"),
- # as.Date("2012-01-01")#,
- #as.Date("2013-01-01"),
- #as.Date("2014-01-01"),
- #as.Date("2015-01-01"),
- #as.Date("2016-01-01")
-#),
-#labels=c("2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016")
-#labels=c("2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012")
-#)
-
-
-time_series_plot<-time_series_plot+
- theme(legend.position= c(0.125,0.85),legend.title = element_blank(),
- legend.text = element_text( size = 10),legend.background = element_blank(),
- legend.key=element_rect(colour=NA,fill=NA))
-
-#scale_fill_discrete(name="Humidity ")
-time_series_plot<-time_series_plot+ theme(axis.title.x =element_text( colour="#990000", size=13))
-time_series_plot<-time_series_plot+ theme(axis.title.y =element_text( colour="#990000", size=13))
-
-
-
-time_series_plot<-time_series_plot+ theme(axis.title.x =element_text(size=13))
-time_series_plot<-time_series_plot+ theme(axis.title.y =element_text(size=13))
- time_series_plot
-
-#file_name_pdf<-paste("../Graphs//time_series_plot_",variable_x,"_",variable,"n_seas",n_seas,"_multiple_delays.pdf", sep = "")
-
-
-
-
-############## normalized #################
-
-
-time_series_summary[,c(2:5)]<-time_series_summary[,c(2:5)]/sum(time_series_summary$Mean)
-real_cases_summary$Mean<-real_cases_summary$Mean/sum(real_cases_summary$Mean)
-time_series_all2<-rbind(time_series_summary[,c(1,2,6)],real_cases_summary[,c(1,2,6)])
-
-
-pal<-wes_palette("Cavalcanti")
-time_series_plot2<-ggplot(time_series_all2,aes(x=Date,y=Mean,colour=source))
-#time_series_plot<-time_series_plot+scale_color_brewer(palette=pal)
-#time_series_plot<-time_series_plot+geom_line(data=real_cases_summary)
-time_series_plot2<-time_series_plot2+geom_line(size=0.75)
-#time_series_plot<-time_series_plot+scale_color_manual(values=c( "#E69F00", "#56B4E9"))
-time_series_plot2<-time_series_plot2+scale_color_manual(values=wes_palette(n=2, name="Cavalcanti"))
-
-time_series_plot2<-time_series_plot2+ xlab("Date")+ ylab("Campylobacter Cases (Normalized)")+scale_y_continuous(limit=c(0,4e-4))
-
-time_series_plot2<-time_series_plot2+
- theme(legend.position= c(0.125,0.85),legend.title = element_blank(),
- legend.text = element_text( size = 10),legend.background = element_blank(),
- legend.key=element_rect(colour=NA,fill=NA))
-
-#scale_fill_discrete(name="Humidity ")
-time_series_plot2<-time_series_plot2+ theme(axis.title.x =element_text( colour="#990000", size=13))
-time_series_plot2<-time_series_plot2+ theme(axis.title.y =element_text( colour="#990000", size=13))
-
-
-
-time_series_plot2<-time_series_plot2+ theme(axis.title.x =element_text(size=13))
-time_series_plot2<-time_series_plot2+ theme(axis.title.y =element_text(size=13))
-time_series_plot2
-
-############## Average per day of the year #################
-time_series$yday<-as.factor(yday(time_series$Date))
-
-#time_series$year<-as.factor(year(time_series$Date))
-#time_series_total<-ddply(time_series,~year,function (x) x$Cases/sum(x$Cases))
-
-#time_series$Cases4<-time_series$Cases4/sum(time_series$Cases4)
-time_series_average1<-ddply(time_series,~yday,summarise,mean=mean(Cases5))
-time_series_quantile1<-ddply(time_series,~yday, function (x) quantile(x$Cases5, c(.25,.5,.75)))
-time_series_average2<-cbind(time_series_average1,time_series_quantile1[,-1])
-time_series_average<-
- data.frame(time_series_average2[,1],time_series_average2[,c(2:5)]/sum(time_series_average2$mean))
-
-#real_cases$norm<-(real_cases$Cases/sum(real_cases$Cases))
-real_cases$yday<-as.factor(yday(as.Date(as.character(real_cases$Date))))
-real_cases_average1<-ddply(real_cases,~yday,summarise,mean=mean(Cases))
-real_cases_quantile1<-ddply(real_cases,~yday,function (x) quantile(x$Cases, c(.25,.5,.75)))
-real_cases_average2<-cbind(real_cases_average1,real_cases_quantile1[,-1])
-real_cases_average<-
- data.frame(real_cases_average2[,1],real_cases_average2[,c(2:5)]/sum(real_cases_average2$mean))
-
-
-df1<-data.frame(real_cases_average,rep("Cases",times=length(real_cases_average[,1])))
-colnames(df1)<-c("Day","Mean","f_quant","median","s_quant","source")
-df2<-data.frame(time_series_average,rep("Model",times=length(time_series_average[,1])))
-colnames(df2)<-c("Day","Mean","f_quant","median","s_quant","source")
-
-average_data<-rbind(df1,df2)
-average_data$Day<-as.numeric(average_data$Day)
-
-yearly_average<-ggplot(average_data,aes(x=Day,y=Mean,colour=source))
-
-yearly_average<-yearly_average+geom_line(size=2)
-yearly_average <-yearly_average+ geom_ribbon(aes(ymin=f_quant, ymax=s_quant,fill=source),alpha=0.15)
-yearly_average<-yearly_average+ xlab("Day of the Year")+ ylab("Campylobacter Cases (Normalized)")
-
-
-#yearly_average<-yearly_average+ scale_y_continuous(limit=c(0,0.05))
-
-
-month_2014<-month.abb[unique(month(as.Date(average_data$Day, origin = "2014-01-01")))]
-
-tick_pos<-yday(as.Date(as.character(
- c("2014-01-01","2014-02-01","2014-03-01","2014-04-01","2014-05-01","2014-06-01",
- "2014-07-01","2014-08-01","2014-09-01","2014-10-01","2014-11-01","2014-12-01")
-)))
-
-yearly_average <-yearly_average+scale_x_continuous(breaks = tick_pos, labels = month_2014)
-yearly_average<-yearly_average+
- theme(legend.position= c(0.125,0.75),legend.title = element_text( size = 10),
- legend.text = element_text( size = 10),legend.background = element_blank(),
- legend.key=element_rect(colour=NA,fill=NA))
-
-#scale_fill_discrete(name="Humidity ")
-
-yearly_average<-yearly_average+ theme(axis.title.x =element_text( colour="#990000", size=13))
-yearly_average<-yearly_average+ theme(axis.title.y =element_text( colour="#990000", size=13))
-
-
-
-yearly_average<-yearly_average+ theme(axis.title.x =element_text(size=13))
-yearly_average<-yearly_average+ theme(axis.title.y =element_text(size=13))
-yearly_average
-
-
-
-
-
-file_name_pdf<-paste("../../Graphs/yearly_average_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_multiple_delays_Simulated.pdf", sep = "")
-#file_name_pdf<-paste("../../Graphs/yearly_average_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_Simulated.pdf", sep = "")
-#file_name_pdf<-paste("../../Graphs/yearly_average_",variable_x,"_",variable_y,"_",variable,"n_seas",n_seas,"_",width_char,situation,".pdf", sep = "")
-
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-yearly_average
-
-dev.off()
-
-
-#file_name<-paste("../Graphs/yearly_average_",variable_x,"_",variable,"n_seas",n_seas,"_multiple_delays.tiff", sep = "")
-file_name<-paste("../../Graphs/yearly_average_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_multiple_delays_Simulated.tiff", sep = "")
-#file_name<-paste("../../Graphs/yearly_average_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_Simulated.tiff", sep = "")
-
-tiff(filename = file_name,width = 17.35, height = 17.35, units = "cm", pointsize = 9, res = 600,compression = "lzw",antialias="default")
-yearly_average
-
-dev.off()
-
-file_name_pdf<-paste("../../Graphs/time_series_plot_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_multiple_delays_Simulated.pdf", sep = "")
-#file_name_pdf<-paste("../../Graphs/time_series_plot_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_Simulated.pdf", sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-time_series_plot2
-
-dev.off()
-
-
-#file_name<-paste("../Graphs/time_series_plot_",variable_x,"_",variable,"n_seas",n_seas,"_multiple_delays.tiff", sep = "")
-file_name<-paste("../../Graphs/time_series_plot_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_multiple_delays_Simulated.tiff", sep = "")
-#file_name<-paste("../../Graphs/time_series_plot_",variable_x,"_",variable_y,"_",variable,"_",width_char,"_Simulated.tiff", sep = "")
-tiff(filename = file_name,width = 17.35, height = 17.35, units = "cm", pointsize = 9, res = 600,compression = "lzw",antialias="default")
-time_series_plot2
-
-dev.off()
-
-#file_name_pdf<-paste("../../Graphs/time_series_plot_",variable_x,"_",variable_y,"_",variable,"n_seas",n_seas,"_",width_char,situation,"_norm.pdf", sep = "")
-#pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-#time_series_plot2
-
-#dev.off()
-
-
-#file_name<-paste("../Graphs/time_series_plot_",variable_x,"_",variable,"n_seas",n_seas,"_multiple_delays.tiff", sep = "")
-#file_name<-paste("../../Graphs/time_series_plot_",variable_x,"_",variable_y,"_",variable,"n_seas",n_seas,"_",width_char,situation,"_norm.tiff", sep = "")
-
-#tiff(filename = file_name,width = 17.35, height = 17.35, units = "cm", pointsize = 9, res = 600,compression = "lzw",antialias="default")
-time_series_plot2
-
-#dev.off()
\ No newline at end of file
diff --git a/Paper_time_lag.R b/Paper_time_lag.R
deleted file mode 100644
index 5454e27ce93584cd63b9ff171f03a1654b562850..0000000000000000000000000000000000000000
--- a/Paper_time_lag.R
+++ /dev/null
@@ -1,368 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# this to calculate delay
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-14
-width_char<-paste(width)
-n_seas<-1
-
-variable_x<-"max_air_temp"
-#variable_x<-"humidity"
-#variable<-"humidity"
-
-variable<-"humidity"
-#variable<-"max_temp"
-
-
-time_series<-read.csv(paste("../DataBase/Cases/Taylor_contribution_Time_series_",variable,"_",variable_x,width_char,"_n_seas",n_seas,".csv",sep=""))
-
-time_series<-na.omit(time_series)
-time_series<-time_series[,-1]
-colnames(time_series)<-c("Date","Contr_temp","Contr_hum","Lab","seas")
-
-time_series$temp_contr<-cumsum(time_series$Contr_temp)
-time_series$temp_hum<-cumsum(time_series$Contr_hum)
-time_series$yday<-yday(time_series$Date)
-time_series$week<-week(time_series$Date)
-time_series$month<-month(time_series$Date)
-time_series$Lab<-as.factor(time_series$Lab)
-
-time_series_lab<-ddply(time_series,~Date,summarise,tot=mean(Contr_temp))
-
-time_series_lab<-ddply(time_series,~yday,summarise,tot=mean(Contr_temp))
-time_series_lab2<-ddply(time_series,~yday,summarise,tot=mean(Contr_hum))
-
-time_series_lab<-ddply(time_series,~week,summarise,tot=mean(Contr_temp))
-time_series_lab2<-ddply(time_series,~week,summarise,tot=mean(Contr_hum))
-
-
-
-#time_series_lab_temp<-ddply(time_series,~month,summarise,
-# N = length(Contr_temp),
-# mean = mean(Contr_temp),
-# sd = sd(Contr_temp),
-# se = sd / sqrt(N)
-# )
-#time_series_lab2<-ddply(time_series,~month,summarise)
-#time_series_lab2<-ddply(time_series,~Date,summarise,tot=mean(Contr_hum))
-
-
-#plot(time_series_lab2,type="l")
-#plot(time_series_lab,type="l")
-
-
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(Contr_temp)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature (Absolute values)")+xlab("week")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+model$fitted.values
-#monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=TRUE, color="black", aes(group=factor(week)))
-monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 10,raw = TRUE),se=TRUE, aes(group=1))
-onthly_summary<-monthly_summary+ geom_smooth(method = "auto",se=TRUE, aes(group=1))
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 3))
-
-#monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-model<-lm(formula=abs(time_series$Contr_temp) ~ poly(time_series$week, 10,raw = TRUE))
-file_name<-paste("../DataBase/Cases/Campylobacter_coefficients_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_temp_week.csv",sep = "")
-write.table(as.numeric(model$coefficients),file_name,sep=",")
-
-#wt<-0
-#for (n_coeff in (1:11)){
-# wt<-wt+unlist(model$coefficients)[[n_coeff]]*seq(1:53)^(n_coeff-1)
-#}
-#wt_temp<-wt/sum(wt)
-
-
-
-
-
-
-
-file_name_tiff<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_temp_week.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_temp.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=Contr_temp))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = 2,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0.3, 0.3))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_temp.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_temp.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=Contr_hum))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = 2,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Humidity")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-
-#monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0.3, 0.3))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_hum.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_hum.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(Contr_temp)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature (Absolute values)")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+model$fitted.values
-#monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=FALSE, color="black", aes(group=x))
-
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 3))
-
-#monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_temp.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_temp.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(Contr_hum)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Relative Humidity (Absolute values)")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 2.))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_hum.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_hum.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(Contr_hum)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Relative Humidity (Absolute values)")+xlab("Week")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-
-
-
-monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 10,raw = TRUE),se=TRUE, aes(group=1))
-
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 3))
-
-#monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-model<-lm(formula=abs(time_series$Contr_temp) ~ poly(time_series$week, 10,raw = TRUE))
-file_name<-paste("../DataBase/Cases/Campylobacter_coefficients_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_hum_week.csv",sep = "")
-write.table(as.numeric(model$coefficients),file_name,sep=",")
-
-
-
-
-file_name_tiff<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_hum_week.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../Graphs/Campylobacter_delay_effect_",variable_x,"_",variable,"_n_seas_",n_seas,"_",width_char,"_abs_hum_week.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-time_series1<-subset(time_series,time_series$Lab=="B152TG")
-time_series1$Date<-as.Date(as.character(time_series1$Date))
-time_series1$year<-as.factor(year(as.Date(as.character(time_series1$Date))))
-time_series1<-subset(time_series1,time_series1$year=="2005")
-
-time_series1<-na.omit(time_series1)
-temp_contr<-cumsum(time_series1$Contr_temp)
-temp_hum<-cumsum(time_series1$Contr_hum)
-
-df2<-data.frame(as.Date(as.character(time_series1$Date)),temp_contr,temp_hum)
-colnames(df2)<-c("Date","contr","humidity")
-#temp_contr<-ddply(time_series1,~year,summarise,Cum_Cases=cumsum(Contr_temp))
diff --git a/Paper_time_lag_3_var_Simulated.R b/Paper_time_lag_3_var_Simulated.R
deleted file mode 100644
index f945a36e724b1fbf73837a85ad46ad017a562df0..0000000000000000000000000000000000000000
--- a/Paper_time_lag_3_var_Simulated.R
+++ /dev/null
@@ -1,470 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# this to calculate delay
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-width<-1
-width_char<-paste(width)
-n_seas<-1
-
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable_z<-"daylength"
-#
-#variable_x2<-"max_air_temp"
-#variable_y2<-"humidity"
-#variable_z2<-"light"
-
-
-
-
-time_series<-read.csv(
- paste("../../Data_Base/Cases/Taylor_contribution_Time_series_",variable_z,"_",variable_y,"_",variable_x,width_char,"_Simulated_original_MEDMI.csv",sep=""))
-
-time_series<-na.omit(time_series)
-time_series<-time_series[,-1]
-colnames(time_series)<-c("Date","Contr_temp","Contr_hum","Contr_light",
- "Lab","delta_eta_temp","delta_temp","delta_eta_hum","delta_hum","delta_eta_light","delta_light")
-
-time_series$temp_contr<-(time_series$Contr_temp)
-time_series$hum_contr<-(time_series$Contr_hum)
-time_series$light_contr<-(time_series$Contr_light)
-
-time_series$yday<-yday(time_series$Date)
-time_series$week<-week(time_series$Date)
-time_series$month<-month(time_series$Date)
-time_series$Lab<-as.factor(time_series$Lab)
-
-#time_series_lab<-ddply(time_series,~Date,summarise,tot=mean(Contr_temp))
-#time_series_lab<-ddply(time_series,~yday,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~yday,summarise,tot=mean(Contr_hum))
-
-#time_series_lab<-ddply(time_series,~week,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~week,summarise,tot=mean(Contr_hum))
-#time_series_lab3<-ddply(time_series,~week,summarise,tot=mean(Contr_light))
-
-
-#time_series_lab_temp<-ddply(time_series,~month,summarise,
-# N = length(Contr_temp),
-# mean = mean(Contr_temp),
-# sd = sd(Contr_temp),
-# se = sd / sqrt(N)
-# )
-#time_series_lab2<-ddply(time_series,~month,summarise)
-#time_series_lab2<-ddply(time_series,~Date,summarise,tot=mean(Contr_hum))
-
-
-#plot(time_series_lab2,type="l")
-#plot(time_series_lab,type="l")
-
-
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(temp_contr)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature (Absolute values)")+xlab("week")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+model$fitted.values
-#monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=TRUE, color="black", aes(group=factor(week)))
-monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
-onthly_summary<-monthly_summary+ geom_smooth(method = "auto",se=TRUE, aes(group=1))
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 50))
-
-#monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-model<-lm(formula=abs(time_series$temp_contr) ~ poly(time_series$week, 15,raw = FALSE))
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.csv",sep = "")
-write.table(as.numeric(model$coefficients),file_name,sep=",")
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=temp_contr))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = 2,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0.3, 0.3))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_temp.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_temp.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=hum_contr))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = 2,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Humidity")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-
-#monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0.3, 0.3))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_hum.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_hum.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(temp_contr)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature (Absolute values)")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+model$fitted.values
-#monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=FALSE, color="black", aes(group=x))
-
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 50))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_temp.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_temp.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(hum_contr)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Relative Humidity (Absolute values)")+xlab("Month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(0., 50))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(hum_contr)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Relative Humidity (Absolute values)")+xlab("Week")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-
-
-
-monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
-
-monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(0., 50))
-
-#monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-model<-lm(formula=abs(time_series$hum_contr) ~ poly(time_series$week, 15,raw = FALSE))
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_hum_week.csv",sep = "")
-write.table(as.numeric(model$coefficients),file_name,sep=",")
-
-
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum_week.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum_week.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(light_contr)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of daylength (Absolute values)")+xlab("week")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+model$fitted.values
-#monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=TRUE, color="black", aes(group=factor(week)))
-monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
-#monthly_summary<-monthly_summary+ geom_smooth(method = "auto",se=TRUE, aes(group=1))
-#monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 0))
-
-#monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-model<-lm(formula=abs(time_series$light_contr) ~ poly(time_series$week, 15,raw = FALSE))
-file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_week.csv",sep = "")
-write.table(as.numeric(model$coefficients),file_name,sep=",")
-
-#wt<-0
-#for (n_coeff in (1:11)){
-# wt<-wt+unlist(model$coefficients)[[n_coeff]]*seq(1:53)^(n_coeff-1)
-#}
-#wt_temp<-wt/sum(wt)
-
-
-
-
-
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light_week.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light_week.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(light_contr)))
-monthly_summary<-monthly_summary+theme_bw(20)
-monthly_summary<-monthly_summary+
- geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
-
-monthly_summary<-monthly_summary+
- theme(axis.title.x =element_text(face="bold", size=13))+
- theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of daylength (Absolute values)")+xlab("month")+
- theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
- axis.text.y=element_text(size=12))+
- scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
- guides(fill=FALSE)#+ geom_jitter()
-#monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Araw=pr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-#monthly_summary<-monthly_summary+model$fitted.values
-#monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=TRUE, color="black", aes(group=factor(week)))
-monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
-monthly_summary<-monthly_summary+ geom_smooth(method = "auto",se=TRUE, aes(group=1))
-#monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 0.2))
-
-monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
-
-monthly_summary
-
-#model<-lm(formula=abs(time_series$light_contr) ~ poly(time_series$week, 15,raw = FALSE))
-#file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_month.csv",sep = "")
-#write.table(as.numeric(model$coefficients),file_name,sep=",")
-
-#wt<-0
-#for (n_coeff in (1:11)){
-# wt<-wt+unlist(model$coefficients)[[n_coeff]]*seq(1:53)^(n_coeff-1)
-#}
-#wt_temp<-wt/sum(wt)
-
-
-
-
-
-
-
-file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light.tiff",sep = "")
-tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
- compression = "lzw",antialias="default")
-
-monthly_summary
-
-dev.off()
-
-
-
-file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light.pdf",sep = "")
-pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-monthly_summary
-dev.off()
-
-
-
-
-
-
diff --git a/Paper_time_lag_3_var_Simulated.Rout b/Paper_time_lag_3_var_Simulated.Rout
deleted file mode 100644
index 08e63d33006dd5002254f8960ac7f99d9c376add..0000000000000000000000000000000000000000
--- a/Paper_time_lag_3_var_Simulated.Rout
+++ /dev/null
@@ -1,565 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # this to calculate delay
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> width<-1
-> width_char<-paste(width)
-> n_seas<-1
->
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable_z<-"daylength"
-> #
-> #variable_x2<-"max_air_temp"
-> #variable_y2<-"humidity"
-> #variable_z2<-"light"
->
->
->
->
-> time_series<-read.csv(
-+ paste("../../Data_Base/Cases/Taylor_contribution_Time_series_",variable_z,"_",variable_y,"_",variable_x,width_char,"_Simulated_original_MEDMI.csv",sep=""))
->
-> time_series<-na.omit(time_series)
-> time_series<-time_series[,-1]
-> colnames(time_series)<-c("Date","Contr_temp","Contr_hum","Contr_light",
-+ "Lab","delta_eta_temp","delta_temp","delta_eta_hum","delta_hum","delta_eta_light","delta_light")
->
-> time_series$temp_contr<-(time_series$Contr_temp)
-> time_series$hum_contr<-(time_series$Contr_hum)
-> time_series$light_contr<-(time_series$Contr_light)
->
-> time_series$yday<-yday(time_series$Date)
-> time_series$week<-week(time_series$Date)
-> time_series$month<-month(time_series$Date)
-> time_series$Lab<-as.factor(time_series$Lab)
->
-> #time_series_lab<-ddply(time_series,~Date,summarise,tot=mean(Contr_temp))
-> #time_series_lab<-ddply(time_series,~yday,summarise,tot=mean(Contr_temp))
-> #time_series_lab2<-ddply(time_series,~yday,summarise,tot=mean(Contr_hum))
->
-> #time_series_lab<-ddply(time_series,~week,summarise,tot=mean(Contr_temp))
-> #time_series_lab2<-ddply(time_series,~week,summarise,tot=mean(Contr_hum))
-> #time_series_lab3<-ddply(time_series,~week,summarise,tot=mean(Contr_light))
->
->
-> #time_series_lab_temp<-ddply(time_series,~month,summarise,
-> # N = length(Contr_temp),
-> # mean = mean(Contr_temp),
-> # sd = sd(Contr_temp),
-> # se = sd / sqrt(N)
-> # )
-> #time_series_lab2<-ddply(time_series,~month,summarise)
-> #time_series_lab2<-ddply(time_series,~Date,summarise,tot=mean(Contr_hum))
->
->
-> #plot(time_series_lab2,type="l")
-> #plot(time_series_lab,type="l")
->
->
->
->
->
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(temp_contr)))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature (Absolute values)")+xlab("week")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-> #monthly_summary<-monthly_summary+model$fitted.values
-> #monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=TRUE, color="black", aes(group=factor(week)))
-> monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
-> onthly_summary<-monthly_summary+ geom_smooth(method = "auto",se=TRUE, aes(group=1))
-> monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 50))
->
-> #monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
->
-> model<-lm(formula=abs(time_series$temp_contr) ~ poly(time_series$week, 15,raw = FALSE))
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.csv",sep = "")
-> write.table(as.numeric(model$coefficients),file_name,sep=",")
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp_week.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_temp.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-> dev.off()
-pdf
- 2
->
->
->
->
->
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(month),y=temp_contr))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = 2,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature")+xlab("Month")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-> #monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0.3, 0.3))
->
-> monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
->
->
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_temp.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_temp.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-> dev.off()
-pdf
- 2
->
->
->
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(month),y=hum_contr))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = 2,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Humidity")+xlab("Month")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
->
-> #monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0.3, 0.3))
->
-> monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
->
->
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_hum.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_hum.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-> dev.off()
-pdf
- 2
->
->
->
->
->
->
->
->
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(temp_contr)))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Temperature (Absolute values)")+xlab("Month")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-> #monthly_summary<-monthly_summary+model$fitted.values
-> #monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=FALSE, color="black", aes(group=x))
->
-> monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 50))
->
-> monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
->
->
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_temp.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_temp.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-> dev.off()
-pdf
- 2
->
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(hum_contr)))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Relative Humidity (Absolute values)")+xlab("Month")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
->
-> monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(0., 50))
->
-> monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
->
->
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-> dev.off()
-pdf
- 2
->
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(hum_contr)))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of Relative Humidity (Absolute values)")+xlab("Week")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
->
->
->
-> monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
->
-> monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(0., 50))
->
-> #monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
->
-> model<-lm(formula=abs(time_series$hum_contr) ~ poly(time_series$week, 15,raw = FALSE))
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_hum_week.csv",sep = "")
-> write.table(as.numeric(model$coefficients),file_name,sep=",")
->
->
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum_week.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_hum_week.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-> dev.off()
-pdf
- 2
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(week),y=abs(light_contr)))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of daylength (Absolute values)")+xlab("week")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Apr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-> #monthly_summary<-monthly_summary+model$fitted.values
-> #monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=TRUE, color="black", aes(group=factor(week)))
-> monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
-> #monthly_summary<-monthly_summary+ geom_smooth(method = "auto",se=TRUE, aes(group=1))
-> #monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 0))
->
-> #monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
->
-> model<-lm(formula=abs(time_series$light_contr) ~ poly(time_series$week, 15,raw = FALSE))
-> file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_week.csv",sep = "")
-> write.table(as.numeric(model$coefficients),file_name,sep=",")
->
-> #wt<-0
-> #for (n_coeff in (1:11)){
-> # wt<-wt+unlist(model$coefficients)[[n_coeff]]*seq(1:53)^(n_coeff-1)
-> #}
-> #wt_temp<-wt/sum(wt)
->
->
->
->
->
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light_week.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light_week.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-> dev.off()
-pdf
- 2
->
->
->
->
->
-> monthly_summary <- ggplot(time_series,aes(x=factor(month),y=abs(light_contr)))
-> monthly_summary<-monthly_summary+theme_bw(20)
-> monthly_summary<-monthly_summary+
-+ geom_boxplot(stat = "boxplot", position = "dodge", outlier.colour = "#990000", outlier.size = NA,notch = FALSE,aes(fill = "gray"))
->
-> monthly_summary<-monthly_summary+
-+ theme(axis.title.x =element_text(face="bold", size=13))+
-+ theme(axis.title.y =element_text(face="bold", size=13))+ylab("Contribution of daylength (Absolute values)")+xlab("month")+
-+ theme(axis.text.x=element_text(angle=25, hjust=0.8, vjust=1, size=9),
-+ axis.text.y=element_text(size=12))+
-+ scale_fill_manual(name = "", values = c("bisque"),labels = " ")+
-+ guides(fill=FALSE)#+ geom_jitter()
-> #monthly_summary<-monthly_summary+scale_x_discrete(label= c("Mon","Tue","Mar","Araw=pr","May","Jun","Jul","Ago","Sep","Oct","Nov","Dec")) #+scale_y_continuous(breaks= c(0,1,3,6,9,12))
-> #monthly_summary<-monthly_summary+model$fitted.values
-> #monthly_summary<-monthly_summary+ geom_smooth(method = "lm", formula=y ~ poly(x, 10),se=TRUE, color="black", aes(group=factor(week)))
-> monthly_summary<-monthly_summary+ geom_smooth(method = "lm",formula=y ~ poly(x, 15,raw = FALSE),se=TRUE, aes(group=1))
-> monthly_summary<-monthly_summary+ geom_smooth(method = "auto",se=TRUE, aes(group=1))
-> #monthly_summary<-monthly_summary+ coord_cartesian(ylim = c(-0., 0.2))
->
-> monthly_summary<-monthly_summary+scale_x_discrete(label=c("January", "February","March","April","May","June","July","August","September","October","November","December"))
->
-> monthly_summary
-`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
-Warning message:
-Computation failed in `stat_smooth()`:
-'degree' must be less than number of unique points
->
-> #model<-lm(formula=abs(time_series$light_contr) ~ poly(time_series$week, 15,raw = FALSE))
-> #file_name<-paste("../../Data_Base/Cases/Simulated_Campylobacter_coefficients_delay_effect_",variable_z,"_",variable_y,"_",variable_x,"_",width_char,"_abs_light_month.csv",sep = "")
-> #write.table(as.numeric(model$coefficients),file_name,sep=",")
->
-> #wt<-0
-> #for (n_coeff in (1:11)){
-> # wt<-wt+unlist(model$coefficients)[[n_coeff]]*seq(1:53)^(n_coeff-1)
-> #}
-> #wt_temp<-wt/sum(wt)
->
->
->
->
->
->
->
-> file_name_tiff<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light.tiff",sep = "")
-> tiff(file_name_tiff, width = 17.35, height = 17.35, units = "cm", pointsize = 12, res = 600,
-+ compression = "lzw",antialias="default")
->
-> monthly_summary
-`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
-Warning message:
-Computation failed in `stat_smooth()`:
-'degree' must be less than number of unique points
->
-> dev.off()
-pdf
- 2
->
->
->
-> file_name_pdf<-paste("../../Graphs/Simulated_Campylobacter_delay_effect_",variable_x,"_",variable_y,"_",variable_z,"_",width_char,"_abs_light.pdf",sep = "")
-> pdf(file = file_name_pdf,width = 6.94, height = 6.5)
-> monthly_summary
-`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
-Warning message:
-Computation failed in `stat_smooth()`:
-'degree' must be less than number of unique points
-> dev.off()
-pdf
- 2
->
->
->
->
->
->
->
-> proc.time()
- user system elapsed
-500.055 27.379 527.660
diff --git a/Taylor_Simulated_Campylobacter_environment_light_hum_max_for_rec_delay_original_modified.R b/Taylor_Simulated_Campylobacter_environment_light_hum_max_for_rec_delay_original_modified.R
deleted file mode 100644
index d69b64da7bbfb4708e7224e68490a28cfb157b7f..0000000000000000000000000000000000000000
--- a/Taylor_Simulated_Campylobacter_environment_light_hum_max_for_rec_delay_original_modified.R
+++ /dev/null
@@ -1,716 +0,0 @@
-# The code does look at how the risk of Campylobacter in humans depends on environmental variables
-# this to calculate delay
-
-rm(list=ls(all=TRUE))
-#
-library(ISOweek)
-library(lubridate)
-library(ggplot2)
-require(MASS)
-library(scales)
-require(pheno)
-library(timeDate)
-library(pastecs)
-library(stringi)
-library(timeSeries)
-library(wesanderson)
-library(plyr)
-
-n_seas<-1
-width<-1
-width_char<-paste(width)
-## Varaible file
-
-
-variable_x<-"Maximum_air_temperature"
-variable_y<-"Relative_humidity"
-variable_z<-"daylength"
-
-variable_x2<-"max_air_temp"
-variable_y2<-"humidity"
-variable_z2<-"light"
-
-
-## Varaible file
-
-
-
-Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
-
-dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2015-12-31"), by = "day")
-All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-Env_laboratory<-Env_laboratory[,-1]
-colnames(Env_laboratory)<-c("PostCode","Date",
- "Maximum_air_temperature",
- "Minimum_air_temperature",
- "Mean_wind_speed",
- "Cumul_Precipitation",
- "Mean_Precipitation",
- "Relative_humidity",
- "daylength",
- "residents")
-
-Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-Env_laboratory$Date<-as.Date(Env_laboratory$Date)
-
-
-
-#Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-Env_laboratory_PHE<-Env_laboratory
-#All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-#All_residents<-sum(All_residents_lab$tot)
-
-######################## include daylength ##################
-
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data,lat_long_lab,by="PostCode")
-#Env_laboratory_int2<-Env_laboratory_int2[,-c(16,17)]
-#names(Env_laboratory_int2)[10]<-"lat"
-#names(Env_laboratory_int2)[11]<-"long"
-#names(Env_laboratory_int2)[14]<-"Date2"
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Env_laboratory_int2$lat
-day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-
-Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
-
-### repeat for the data only ####
-
-latitude<-Env_Campylobacter_data_int2$lat
-day_of_the_year<-yday(as.Date(Env_Campylobacter_data_int2$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Campylobacter_data_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-#daylength_df$lat<-as.factor(daylength_df$lat)
-#daylength_df$Date<-as.factor(daylength_df$Date)
-#Env_Lyme_data_int2$lat<-as.factor(Env_Lyme_data_int2$lat)
-#Env_Lyme_data_int2$Date<-as.factor(Env_Lyme_data_int2$Date)
-
-Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int2,daylength_df)
-
-
-
-
-########################
-
-
-
-
-
-
-
-var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_z2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-var_x_loc_df_all<-var_x_loc_df_all[,-1]
-colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable_z,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
-
-var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
-
-
-###################
-
-
-delta_light<-1
-delta_hum<-5
-delta_temp<-1
-delta_rain<-2
-delta_cum_rain<-2
-delta_wind<-1
-
-breaks_hum<-seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum) #i
-breaks_min_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp)
-breaks_max_temp<-seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-breaks_light<-seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light)
-
-
-i_hum_min<-max(which(breaks_hum<=min(na.omit(Env_Campylobacter_data$Relative_humidity))))
-i_hum_max<-max(which(breaks_hum<=max(na.omit(Env_Campylobacter_data$Relative_humidity))))
-
-i_min_temp_min<-max(which(breaks_min_temp<=min(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-i_min_temp_max<-max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-
-i_max_temp_min<-max(which(breaks_max_temp<=min(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-i_max_temp_max<-max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-
-i_rain_min<-max(which(breaks_rain<=min(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-i_rain_max<-max(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-
-i_cum_rain_min<-max(which(breaks_cum_rain<=min(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-i_cum_rain_max<-max(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-
-i_wind_min<-max(which(breaks_wind<=min(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-
-
-i_light_min<-max(which(breaks_light<=min(na.omit(Env_Campylobacter_data$daylength))))
-i_light_max<-max(which(breaks_light<=max(na.omit(Env_Campylobacter_data$daylength))))
-
-############## from here '###################
-
-
-if (variable_z=="Maximum_air_temperature"){
-
- breaks_var<-breaks_max_temp
- i_var_min<-i_max_temp_min
- i_var_max<-i_max_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Maximum_air_temperature
- Env_laboratory_var<-Env_laboratory$Maximum_air_temperature
-}
-if (variable_x=="Maximum_air_temperature"){
-
- i_var_x_min<-i_max_temp_min
- i_var_x_max<-i_max_temp_max
- breaks_var_x<-breaks_max_temp
-}
-
-if (variable_y=="Maximum_air_temperature"){
-
- i_var_y_min<-i_max_temp_min
- i_var_y_max<-i_max_temp_max
- breaks_var_y<-breaks_max_temp
-}
-
-if (variable_z=="Minimum_air_temperature"){
-
- breaks_var<-breaks_min_temp
- i_var_min<-i_min_temp_min
- i_var_max<-i_min_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$min_temp
- Env_laboratory_var<-Env_laboratory$min_temp
-}
-if (variable_x=="Minimum_air_temperature"){
-
- i_var_x_min<-i_min_temp_min
- i_var_x_max<-i_min_temp_max
- breaks_var_x<-breaks_min_temp
-}
-
-if (variable_y=="Minimum_air_temperature"){
-
- i_var_y_min<-i_min_temp_min
- i_var_y_max<-i_min_temp_max
- breaks_var_y<-breaks_min_temp
-}
-
-if (variable_z=="Relative_humidity"){
-
- breaks_var<-breaks_hum
- i_var_min<-i_hum_min
- i_var_max<-i_hum_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Relative_humidity
- Env_laboratory_var<-Env_laboratory$Relative_humidity
-}
-if (variable_x=="Relative_humidity"){
-
- i_var_x_min<-i_hum_min
- i_var_x_max<-i_hum_max
- breaks_var_x<-breaks_hum
-}
-
-if (variable_y=="Relative_humidity"){
-
- i_var_y_min<-i_hum_min
- i_var_y_max<-i_hum_max
- breaks_var_y<-breaks_hum
-}
-
-
-if (variable_z=="mean_temp"){
-
- breaks_var<-breaks_mean_temp
- i_var_min<-i_mean_temp_min
- i_var_max<-i_mean_temp_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$mean_temp
- Env_laboratory_var<-Env_laboratory$mean_temp
-}
-if (variable_x=="mean_temp"){
-
- i_var_x_min<-i_mean_temp_min
- i_var_x_max<-i_mean_temp_max
- breaks_var_x<-breaks_mean_temp
-}
-
-if (variable_y=="mean_temp"){
-
- i_var_y_min<-i_mean_temp_min
- i_var_y_max<-i_mean_temp_max
- breaks_var_y<-breaks_mean_temp
-}
-
-if (variable_z=="Mean_Precipitation"){
-
- breaks_var<-breaks_rain
- i_var_min<-i_rain_min
- i_var_max<-i_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_Precipitation
- Env_laboratory_var<-Env_laboratory$Mean_Precipitation
-}
-if (variable_x=="Mean_Precipitation"){
-
- i_var_x_min<-i_rain_min
- i_var_x_max<-i_rain_max
- breaks_var_x<-breaks_rain
-}
-
-if (variable_y=="Mean_Precipitation"){
-
- i_var_y_min<-i_rain_min
- i_var_y_max<-i_rain_max
- breaks_var_y<-breaks_rain
-}
-
-if (variable_z=="Cumul_Precipitation"){
-
- breaks_var<-breaks_cum_rain
- i_var_min<-i_cum_rain_min
- i_var_max<-i_cum_rain_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Cumul_Precipitation
- Env_laboratory_var<-Env_laboratory$Cumul_Precipitation
-}
-if (variable_x=="Cumul_Precipitation"){
-
- i_var_x_min<-i_cum_rain_min
- i_var_x_max<-i_cum_rain_max
- breaks_var_x<-breaks_cum_rain
-}
-
-if (variable_y=="Cumul_Precipitation"){
-
- i_var_y_min<-i_cum_rain_min
- i_var_y_max<-i_cum_rain_max
- breaks_var_y<-breaks_cum_rain
-}
-
-
-if (variable_z=="Mean_wind_speed"){
-
- breaks_var<-breaks_wind
- i_var_min<-i_wind_min
- i_var_max<-i_wind_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_wind_speed
- Env_laboratory_var<-Env_laboratory$Mean_wind_speed
-}
-if (variable_x=="Mean_wind_speed"){
-
- i_var_x_min<-i_wind_min
- i_var_x_max<-i_wind_max
- breaks_var_x<-breaks_wind
-}
-
-if (variable_y=="Mean_wind_speed"){
-
- i_var_y_min<-i_wind_min
- i_var_y_max<-i_wind_max
- breaks_var_y<-breaks_wind
-}
-
-
-
-
-if (variable_z=="daylength"){
-
- breaks_var<-breaks_light
- i_var_min<-i_light_min
- i_var_max<-i_light_max
- Env_Campylobacter_data_var<-Env_Campylobacter_data$daylength
- Env_laboratory_var<-Env_laboratory$daylength
-}
-if (variable_x=="daylength"){
-
- i_var_x_min<-i_light_min
- i_var_x_max<-i_light_max
- breaks_var_x<-breaks_light
-}
-
-if (variable_y=="daylength"){
-
- i_var_y_min<-i_light_min
- i_var_y_max<-i_light_max
- breaks_var_y<-breaks_light
-}
-
-
-
-
-D_eta_var_x<-function(i_var,i_var_y,i_var_x)
-{
-
-
-
-
-#var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab #All_residents_lab[,2] this change by postcode only
-
-if(is.na(breaks_var[i_var+1])==TRUE)
-{
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var])
-} else {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var] & var_x_loc_df$daylength<breaks_var[i_var+1])
-}
-
-
-if(is.na(breaks_var_y[i_var_y+1])==TRUE)
-{
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y])
-} else {
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y] & Yt0$Relative_humidity<breaks_var_y[i_var_y+1])
-}
-
-
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x])
- } else {
- Yt2<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x] & Yt1$Maximum_air_temperature<breaks_var_x[i_var_x+1])
- }
-
-
- if(is.na(breaks_var_x[i_var_x+2])==TRUE)
- {
- Yt3<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x+1])
- } else {
- Yt3<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x+1] & Yt1$Maximum_air_temperature<breaks_var_x[i_var_x+2])
- }
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_x<- (mean(Yt3$eta- Yt2$eta))/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_x<- 0.5*mean(Yt3$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_x<- 0.5*mean(Yt2$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_x<-NA
- }
-
- return(D_eta_D_x)
- }
-
-
-
-
-D_eta_var_y<-function(i_var,i_var_y,i_var_x)
-{
-
-
- #var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
- var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab
-
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var])
- } else {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var] & var_x_loc_df$daylength<breaks_var[i_var+1])
- }
-
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt1<-subset(Yt0,Yt0$Maximum_air_temperature>=breaks_var_x[i_var_x])
- } else {
- Yt1<-subset(Yt0,Yt0$Maximum_air_temperature>=breaks_var_x[i_var_x] & Yt0$Maximum_air_temperature<breaks_var_x[i_var_x+1])
- }
-
-
-
- if(is.na(breaks_var_y[i_var_y+1])==TRUE)
- {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y])
- } else {
- Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y] & Yt1$Relative_humidity<breaks_var_y[i_var_y+1])
- }
-
-
- if(is.na(breaks_var_y[i_var_y+2])==TRUE)
- {
- Yt3<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y+1])
- } else {
- Yt3<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y+1] & Yt1$Relative_humidity<breaks_var_y[i_var_y+2])
- }
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_y<- (mean(Yt3$eta- Yt2$eta))/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_y<- 0.5*mean(Yt3$eta)/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_y<- 0.5*mean(Yt2$eta)/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_y<-NA
- }
-
- return(D_eta_D_y)
-}
-
-
-
-
-D_eta_var<-function(i_var,i_var_y,i_var_x)
-{
-
-
-#var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab
-
- if(is.na(breaks_var_x[i_var_x+1])==TRUE)
- {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$Maximum_air_temperature>=breaks_var_x[i_var_x])
- } else {
- Yt0<-subset(var_x_loc_df,var_x_loc_df$Maximum_air_temperature>=breaks_var_x[i_var_x] & var_x_loc_df$Maximum_air_temperature<breaks_var_x[i_var_x+1])
- }
-
-if(is.na(breaks_var_y[i_var_y+1])==TRUE)
-{
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y])
-} else {
- Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y] & Yt0$Relative_humidity<breaks_var_y[i_var_y+1])
-}
-
-
- if(is.na(breaks_var[i_var+1])==TRUE)
- {
- Yt2<-subset( Yt1, Yt1$daylength>=breaks_var[i_var])
- } else {
- Yt2<-subset( Yt1, Yt1$daylength>=breaks_var[i_var] & Yt1$daylength<breaks_var[i_var+1])
- }
-
-if(is.na(breaks_var[i_var+2])==TRUE)
-{
- Yt3<-subset( Yt1, Yt1$daylength>=breaks_var[i_var+1])
-} else {
- Yt3<-subset( Yt1, Yt1$Relative_humiditylight>=breaks_var[i_var+1] & Yt1$daylength<breaks_var[i_var+2])
-}
-
-
-
- if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
- D_eta_D_v<- mean(Yt3$eta- Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
- D_eta_D_v<- 0.5*mean(Yt3$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
- D_eta_D_v<- 0.5*mean(Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
- } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
- D_eta_D_v<-NA
- }
-
- return(D_eta_D_v)
- }
-
-
-
-
-
-df_all_1<-c()
-#i_var<-seq(i_var_min,i_var_max)
-#i_var_x<-seq(i_var_x_min,i_var_x_max)
-
- for (j in c(1: length(All_PC_s))){
-
-var_x_loc_df<-var_x_loc_df_all2
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[j])
-
-All_residents_lab<-ddply(variable_df,~PostCode,summarise,tot=mean(residents))
-for (i_var in c(seq(i_var_min,i_var_max))){
-for (i_var_y in c(seq(i_var_y_min,i_var_y_max))){
- for (i_var_x in c(seq(i_var_x_min,i_var_x_max))){
-
-
-#df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-#df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-
-df1<-data.frame(breaks_light[i_var],breaks_hum[i_var_y],breaks_max_temp[i_var_x],D_eta_var_x(i_var,i_var_y,i_var_x),D_eta_var_y(i_var,i_var_y,i_var_x),D_eta_var(i_var,i_var_y,i_var_x),All_PC_s[j] )
-colnames(df1)<-c("daylength","Relative_humidity","Maximum_air_temperature","D_eta_D_max_temp","D_eta_D_hum","D_eta_D_light","PostCode")
-df_all_1<-rbind(df_all_1,df1)
-#mapply(D_eta_var_x,i_var, MoreArgs =list(i_var_x))
-}
-}
-
-}
-}
-
-#df_all<-ddply(df_all_1,~PostCode,summarise,tot=mean())
-df_all<-df_all_1
-
-time_series<-c()
-
-for (i in c(1: length(All_PC_s))){
- #for (i in c(1: 15)){
-
-
-
- #for (j in c(1: n_seas)){
-
- # n_months<-12/n_seas
-
- variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
- if (length(variable_df[,1])!=0){
-
-
-
-Delta_T<-diff(variable_df$Maximum_air_temperature)
-Delta_RH<-diff(variable_df$Relative_humidity)
-Delta_L<-diff(variable_df$daylength)
-Delta_var<-data.frame(variable_df$Date[-1],Delta_T,Delta_RH,Delta_L)
-colnames(Delta_var)<-c("dates","diff_max_temp","diff_humidity","diff_light")
-
-x<-variable_df$Relative_humidity
-y<-variable_df$Maximum_air_temperature
-z<-variable_df$daylength
-
-variable_df_1_dis<-data.frame(variable_df$Date,variable_df$Relative_humidity)
-variable_df_2_dis<-data.frame(variable_df$Date,variable_df$Maximum_air_temperature)
-variable_df_3_dis<-data.frame(variable_df$Date,variable_df$daylength)
-
-colnames(variable_df_1_dis)<-c("dates","Relative_humidity")
-colnames(variable_df_2_dis)<-c("dates","Maximum_air_temperature")
-colnames(variable_df_3_dis)<-c("dates","daylength")
-
- variable_df_1_dis$dates<-as.Date(as.character((variable_df_1_dis$dates)))
- variable_df_2_dis$dates<-as.Date(as.character((variable_df_2_dis$dates)))
- variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
-
- variable_df_1_dis<-subset(variable_df_1_dis,year(variable_df_1_dis$dates)>=1990 & year(variable_df_1_dis$dates)<=2015)
- variable_df_2_dis<-subset(variable_df_2_dis,year(variable_df_2_dis$dates)>=1990 & year(variable_df_2_dis$dates)<=2015)
- variable_df_3_dis<-subset(variable_df_3_dis,year(variable_df_3_dis$dates)>=1990 & year(variable_df_3_dis$dates)<=2015)
-
- variable_df_1_dis$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
- variable_df_2_dis$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
- variable_df_3_dis$daylength<-(breaks_light[findInterval(z, breaks_light)])
-
-
-# var_x_loc_df<-subset(var_x_loc_df_all2,var_x_loc_df_all2$month==j)
-# var_x_loc_df$Relative_humidity<-floor(var_x_loc_df$Relative_humidity)
-# var_x_loc_df$Maximum_air_temperature<-floor(var_x_loc_df$breaks)
- #df<-subset(df_all,df_all$month==j)
- df<-subset(df_all,df_all$PostCode==All_PC_s[i])
-
- variable_df_4_dis<-merge(variable_df_1_dis,df, by="Relative_humidity")
- variable_df_5_dis<-merge(variable_df_2_dis,df, by="Maximum_air_temperature")
- variable_df_6_dis<-merge(variable_df_3_dis,df, by="daylength")
-
-
-
- variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
- variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
- variable_df_5_dis$dates<-as.Date(as.character((variable_df_5_dis$dates)))
- variable_df_5_dis<-variable_df_5_dis[order(variable_df_5_dis$dates),]
- variable_df_6_dis$dates<-as.Date(as.character((variable_df_6_dis$dates)))
- variable_df_6_dis<-variable_df_6_dis[order(variable_df_6_dis$dates),]
-
-
- variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
- variable_df_5_dis$dates<-as.factor(variable_df_5_dis$dates)
- variable_df_6_dis$dates<-as.factor(variable_df_6_dis$dates)
-
- variable_df_dis0<-merge(variable_df_4_dis,variable_df_5_dis, by=c("dates","Maximum_air_temperature","Relative_humidity","daylength") )
- variable_df_dis<-merge(variable_df_dis0,variable_df_6_dis, by=c("dates","Maximum_air_temperature","Relative_humidity","daylength"))
-
- variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
- variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
- variable_df_dis$dates<-as.factor(variable_df_dis$dates)
- Delta_var$dates<-as.factor(Delta_var$dates)
- variable_df_Taylor<-merge(variable_df_dis,Delta_var, by=c("dates"))
-
- variable_df_Taylor<-variable_df_Taylor[,c(1:7,17:19)]
- colnames(variable_df_Taylor)<-c("dates",variable_x, variable_y, variable_z,"D_eta_D_max_temp","D_eta_D_hum","D_eta_D_light",
- "diff_max_temp","diff_humidity","diff_light")
-
-
- lambda_temp<-variable_df_Taylor$D_eta_D_max_temp*variable_df_Taylor$diff_max_temp
- lambda_hum<-variable_df_Taylor$D_eta_D_hum*variable_df_Taylor$diff_humidity
- lambda_light<-variable_df_Taylor$D_eta_D_light*variable_df_Taylor$diff_light
-
-
-
- time_series_1<-
- data.frame(variable_df_Taylor$dates,
- lambda_temp,lambda_hum,lambda_light,
- rep(All_PC_s[i],times=length(variable_df_Taylor$dates)),
- variable_df_Taylor$D_eta_D_max_temp,variable_df_Taylor$diff_max_temp,
- variable_df_Taylor$D_eta_D_hum,variable_df_Taylor$diff_humidity,
- variable_df_Taylor$D_eta_D_light,variable_df_Taylor$diff_light)
- colnames(time_series_1)<-c("Date","Contr_temp","Contr_hum","Contr_light",
- "Lab","delta_eta_temp","delta_temp","delta_eta_hum","delta_hum","delta_eta_light","delta_light")
- time_series<-rbind(time_series,time_series_1)
-
- #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
- print(100*c(i/length(All_PC_s) ))
- #print(" ")
- #print(lambda)
- }
-}
-
-#time_series1<-subset(time_series,time_series$Lab=="B152TG")
-#time_series1$Date<-as.Date(as.character(time_series1$Date))
-#time_series1$year<-as.factor(year(as.Date(as.character(time_series1$Date))))
-#time_series1<-subset(time_series1,time_series1$year=="2005")
-
-#time_series1<-na.omit(time_series1)
-##temp_contr<-cumsum(time_series1$Contr_temp)
-#temp_hum<-cumsum(time_series1$Contr_hum)
-#
-#df2<-data.frame(as.Date(as.character(time_series1$Date)),temp_contr,temp_hum)
-#colnames(df2)<-c("Date","contr","humidity")
-#temp_contr<-ddply(time_series1,~year,summarise,Cum_Cases=cumsum(Contr_temp))
-#time_series<-na.omit(time_series)
-#time_series$temp_contr<-cumsum(time_series$Contr_temp)
-#time_series$temp_hum<-cumsum(time_series$Contr_hum)
-#time_series$yday<-yday(time_series$Date)
-#time_series$week<-week(time_series$Date)
-#time_series$month<-month(time_series$Date)
-#time_series$Lab<-as.factor(time_series$Lab)
-
-#time_series_lab<-ddply(time_series,~Date,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~yday,summarise,tot=mean(Contr_temp))
-
-#time_series_lab<-ddply(time_series,~week,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~week,summarise,tot=mean(Contr_hum))
-
-#time_series_lab<-ddply(time_series,~month,summarise,tot=mean(Contr_temp))
-#time_series_lab2<-ddply(time_series,~month,summarise,tot=mean(Contr_hum))
-#time_series_lab2<-ddply(time_series,~Date,summarise,tot=mean(Contr_hum))
-
-write.table(time_series,paste("../../Data_Base/Cases/Taylor_contribution_Time_series_",variable_z,"_",variable_y,"_",variable_x,width_char,"_Simulated_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
diff --git a/Taylor_Simulated_Campylobacter_environment_light_hum_max_for_rec_delay_original_modified.Rout b/Taylor_Simulated_Campylobacter_environment_light_hum_max_for_rec_delay_original_modified.Rout
deleted file mode 100644
index a1c9fcba1bdd224f9096f1c1056e5fdef09a6b68..0000000000000000000000000000000000000000
--- a/Taylor_Simulated_Campylobacter_environment_light_hum_max_for_rec_delay_original_modified.Rout
+++ /dev/null
@@ -1,979 +0,0 @@
-
-R version 3.5.3 (2019-03-11) -- "Great Truth"
-Copyright (C) 2019 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
-
-R is free software and comes with ABSOLUTELY NO WARRANTY.
-You are welcome to redistribute it under certain conditions.
-Type 'license()' or 'licence()' for distribution details.
-
- Natural language support but running in an English locale
-
-R is a collaborative project with many contributors.
-Type 'contributors()' for more information and
-'citation()' on how to cite R or R packages in publications.
-
-Type 'demo()' for some demos, 'help()' for on-line help, or
-'help.start()' for an HTML browser interface to help.
-Type 'q()' to quit R.
-
-[Previously saved workspace restored]
-
-> # The code does look at how the risk of Campylobacter in humans depends on environmental variables
-> # this to calculate delay
->
-> rm(list=ls(all=TRUE))
-> #
-> library(ISOweek)
-> library(lubridate)
-
-Attaching package: ‘lubridate’
-
-The following object is masked from ‘package:base’:
-
- date
-
-> library(ggplot2)
-> require(MASS)
-Loading required package: MASS
-> library(scales)
-> require(pheno)
-Loading required package: pheno
-Loading required package: nlme
-Loading required package: SparseM
-
-Attaching package: ‘SparseM’
-
-The following object is masked from ‘package:base’:
-
- backsolve
-
-Loading required package: quantreg
-> library(timeDate)
-> library(pastecs)
-> library(stringi)
-> library(timeSeries)
-> library(wesanderson)
-> library(plyr)
-
-Attaching package: ‘plyr’
-
-The following object is masked from ‘package:lubridate’:
-
- here
-
->
-> n_seas<-1
-> width<-1
-> width_char<-paste(width)
-> ## Varaible file
->
->
-> variable_x<-"Maximum_air_temperature"
-> variable_y<-"Relative_humidity"
-> variable_z<-"daylength"
->
-> variable_x2<-"max_air_temp"
-> variable_y2<-"humidity"
-> variable_z2<-"light"
->
->
-> ## Varaible file
->
->
->
-> Env_Campylobacter_data_all2<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Campylobacter_environment_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_Campylobacter_data_all2<-Env_Campylobacter_data_all2[,-1]
-> colnames(Env_Campylobacter_data_all2)<-c("PostCode","Date","Cases",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_Campylobacter_data<-subset(Env_Campylobacter_data_all2,year(Env_Campylobacter_data_all2$Date)>=1990 & year(Env_Campylobacter_data_all2$Date)<=2015)
->
-> dates_s<- dates_s<- seq(as.Date("1990-01-01"), as.Date("2015-12-31"), by = "day")
-> All_PC_s<-levels(as.factor(Env_Campylobacter_data$PostCode))
-> All_PC<-rep(All_PC_s,each=length(dates_s))
->
->
->
-> Env_laboratory<-read.csv(paste("../../Data_Base/Cases_Environment/Simulated_Laboratory_",width_char,"_original_MEDMI.csv",sep=""))
-> Env_laboratory<-Env_laboratory[,-1]
-> colnames(Env_laboratory)<-c("PostCode","Date",
-+ "Maximum_air_temperature",
-+ "Minimum_air_temperature",
-+ "Mean_wind_speed",
-+ "Cumul_Precipitation",
-+ "Mean_Precipitation",
-+ "Relative_humidity",
-+ "daylength",
-+ "residents")
->
-> Env_laboratory<-subset(Env_laboratory,year(Env_laboratory$Date)>=1990 & year(Env_laboratory$Date)<=2015)
-> Env_laboratory$Date<-as.Date(Env_laboratory$Date)
->
->
->
-> #Env_laboratory_PHE<-Env_laboratory[wt[-1],]
-> ##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-> Env_laboratory_PHE<-Env_laboratory
-> #All_residents_lab<-ddply(Env_laboratory_PHE,~PostCode,summarise,tot=mean(residents))
-> #All_residents<-sum(All_residents_lab$tot)
->
-> ######################## include daylength ##################
->
-> Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
->
->
-> lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-> colnames(lat_long_lab)<-c("PostCode","lat","long")
-> Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-> Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data,lat_long_lab,by="PostCode")
-> #Env_laboratory_int2<-Env_laboratory_int2[,-c(16,17)]
-> #names(Env_laboratory_int2)[10]<-"lat"
-> #names(Env_laboratory_int2)[11]<-"long"
-> #names(Env_laboratory_int2)[14]<-"Date2"
->
-> daylength<-function(lat,day_year)
-+ {
-+ #Latitude measure in degrees
-+ P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
-+ Denom<-cos(lat*pi/180)*cos(P)
-+ Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
-+ D<-24-(24/pi)*acos(Numer/Denom)
-+ return(D)
-+ }
->
-> latitude<-Env_laboratory_int2$lat
-> day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
->
-> Env_laboratory<-data.frame(Env_laboratory_int2,daylength_df)
->
-> ### repeat for the data only ####
->
-> latitude<-Env_Campylobacter_data_int2$lat
-> day_of_the_year<-yday(as.Date(Env_Campylobacter_data_int2$Date))
->
-> daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-> daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_Campylobacter_data_int2$Date),daylength_int1)
-> colnames(daylength_df)<-c("lat","day_year","Date","daylength")
->
-> #daylength_df$lat<-as.factor(daylength_df$lat)
-> #daylength_df$Date<-as.factor(daylength_df$Date)
-> #Env_Lyme_data_int2$lat<-as.factor(Env_Lyme_data_int2$lat)
-> #Env_Lyme_data_int2$Date<-as.factor(Env_Lyme_data_int2$Date)
->
-> Env_Campylobacter_data<-data.frame(Env_Campylobacter_data_int2,daylength_df)
->
->
->
->
-> ########################
->
->
->
->
->
->
->
-> var_x_loc_df_all<-read.csv(paste("../../Data_Base/Cases_Environment/",variable_z2,"_",variable_y2,"_",variable_x2,"_",width_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))
-> var_x_loc_df_all<-var_x_loc_df_all[,-1]
-> colnames(var_x_loc_df_all)<-c(variable_x,"breaks","prop","incidence","month",variable_z,variable_y,"counts","counts_tot","residents","residents_tot","Numb_Lab")
->
-> var_x_loc_df_all2<-na.omit(var_x_loc_df_all)
->
->
-> ###################
->
->
-> delta_light<-1
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-2
-> delta_cum_rain<-2
-> delta_wind<-1
->
-> breaks_hum<-seq(max(min(na.omit(Env_laboratory$Relative_humidity))-10,0),max(na.omit(Env_laboratory$Relative_humidity))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2, max(na.omit(Env_laboratory$Minimum_air_temperature))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(na.omit(Env_laboratory$Maximum_air_temperature))-2, max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-> breaks_rain<-seq(max(min(na.omit(Env_laboratory$Mean_Precipitation)),0), max(na.omit(Env_laboratory$Mean_Precipitation))+2,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(Env_laboratory$Mean_wind_speed))-2,0),max(na.omit(Env_laboratory$Mean_wind_speed))+2,by=delta_wind)
-> breaks_cum_rain<-seq(max(min(na.omit(Env_laboratory$Cumul_Precipitation)),0), max(na.omit(Env_laboratory$Cumul_Precipitation))+2,by=delta_cum_rain)
-> breaks_mean_temp<-seq(min(na.omit(Env_laboratory$Minimum_air_temperature))-2,max(na.omit(Env_laboratory$Maximum_air_temperature))+2,by=delta_temp)
-> breaks_light<-seq(max(min(na.omit(Env_laboratory$daylength))-1,0),max(na.omit(Env_laboratory$daylength))+1,by=delta_light)
->
->
-> i_hum_min<-max(which(breaks_hum<=min(na.omit(Env_Campylobacter_data$Relative_humidity))))
-> i_hum_max<-max(which(breaks_hum<=max(na.omit(Env_Campylobacter_data$Relative_humidity))))
->
-> i_min_temp_min<-max(which(breaks_min_temp<=min(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
-> i_min_temp_max<-max(which(breaks_min_temp<=max(na.omit(Env_Campylobacter_data$Minimum_air_temperature))))
->
-> i_max_temp_min<-max(which(breaks_max_temp<=min(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
-> i_max_temp_max<-max(which(breaks_max_temp<=max(na.omit(Env_Campylobacter_data$Maximum_air_temperature))))
->
-> i_rain_min<-max(which(breaks_rain<=min(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
-Warning message:
-In max(which(breaks_rain <= min(na.omit(Env_Campylobacter_data$Mean_Precipitation)))) :
- no non-missing arguments to max; returning -Inf
-> i_rain_max<-max(which(breaks_rain<=max(na.omit(Env_Campylobacter_data$Mean_Precipitation))))
->
-> i_cum_rain_min<-max(which(breaks_cum_rain<=min(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
-Warning message:
-In max(which(breaks_cum_rain <= min(na.omit(Env_Campylobacter_data$Cumul_Precipitation)))) :
- no non-missing arguments to max; returning -Inf
-> i_cum_rain_max<-max(which(breaks_cum_rain<=max(na.omit(Env_Campylobacter_data$Cumul_Precipitation))))
->
-> i_wind_min<-max(which(breaks_wind<=min(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
-> i_wind_max<-max(which(breaks_wind<=max(na.omit(Env_Campylobacter_data$Mean_wind_speed))))
->
->
-> i_light_min<-max(which(breaks_light<=min(na.omit(Env_Campylobacter_data$daylength))))
-> i_light_max<-max(which(breaks_light<=max(na.omit(Env_Campylobacter_data$daylength))))
->
-> ############## from here '###################
->
->
-> if (variable_z=="Maximum_air_temperature"){
-+
-+ breaks_var<-breaks_max_temp
-+ i_var_min<-i_max_temp_min
-+ i_var_max<-i_max_temp_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Maximum_air_temperature
-+ Env_laboratory_var<-Env_laboratory$Maximum_air_temperature
-+ }
-> if (variable_x=="Maximum_air_temperature"){
-+
-+ i_var_x_min<-i_max_temp_min
-+ i_var_x_max<-i_max_temp_max
-+ breaks_var_x<-breaks_max_temp
-+ }
->
-> if (variable_y=="Maximum_air_temperature"){
-+
-+ i_var_y_min<-i_max_temp_min
-+ i_var_y_max<-i_max_temp_max
-+ breaks_var_y<-breaks_max_temp
-+ }
->
-> if (variable_z=="Minimum_air_temperature"){
-+
-+ breaks_var<-breaks_min_temp
-+ i_var_min<-i_min_temp_min
-+ i_var_max<-i_min_temp_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$min_temp
-+ Env_laboratory_var<-Env_laboratory$min_temp
-+ }
-> if (variable_x=="Minimum_air_temperature"){
-+
-+ i_var_x_min<-i_min_temp_min
-+ i_var_x_max<-i_min_temp_max
-+ breaks_var_x<-breaks_min_temp
-+ }
->
-> if (variable_y=="Minimum_air_temperature"){
-+
-+ i_var_y_min<-i_min_temp_min
-+ i_var_y_max<-i_min_temp_max
-+ breaks_var_y<-breaks_min_temp
-+ }
->
-> if (variable_z=="Relative_humidity"){
-+
-+ breaks_var<-breaks_hum
-+ i_var_min<-i_hum_min
-+ i_var_max<-i_hum_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Relative_humidity
-+ Env_laboratory_var<-Env_laboratory$Relative_humidity
-+ }
-> if (variable_x=="Relative_humidity"){
-+
-+ i_var_x_min<-i_hum_min
-+ i_var_x_max<-i_hum_max
-+ breaks_var_x<-breaks_hum
-+ }
->
-> if (variable_y=="Relative_humidity"){
-+
-+ i_var_y_min<-i_hum_min
-+ i_var_y_max<-i_hum_max
-+ breaks_var_y<-breaks_hum
-+ }
->
->
-> if (variable_z=="mean_temp"){
-+
-+ breaks_var<-breaks_mean_temp
-+ i_var_min<-i_mean_temp_min
-+ i_var_max<-i_mean_temp_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$mean_temp
-+ Env_laboratory_var<-Env_laboratory$mean_temp
-+ }
-> if (variable_x=="mean_temp"){
-+
-+ i_var_x_min<-i_mean_temp_min
-+ i_var_x_max<-i_mean_temp_max
-+ breaks_var_x<-breaks_mean_temp
-+ }
->
-> if (variable_y=="mean_temp"){
-+
-+ i_var_y_min<-i_mean_temp_min
-+ i_var_y_max<-i_mean_temp_max
-+ breaks_var_y<-breaks_mean_temp
-+ }
->
-> if (variable_z=="Mean_Precipitation"){
-+
-+ breaks_var<-breaks_rain
-+ i_var_min<-i_rain_min
-+ i_var_max<-i_rain_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_Precipitation
-+ Env_laboratory_var<-Env_laboratory$Mean_Precipitation
-+ }
-> if (variable_x=="Mean_Precipitation"){
-+
-+ i_var_x_min<-i_rain_min
-+ i_var_x_max<-i_rain_max
-+ breaks_var_x<-breaks_rain
-+ }
->
-> if (variable_y=="Mean_Precipitation"){
-+
-+ i_var_y_min<-i_rain_min
-+ i_var_y_max<-i_rain_max
-+ breaks_var_y<-breaks_rain
-+ }
->
-> if (variable_z=="Cumul_Precipitation"){
-+
-+ breaks_var<-breaks_cum_rain
-+ i_var_min<-i_cum_rain_min
-+ i_var_max<-i_cum_rain_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Cumul_Precipitation
-+ Env_laboratory_var<-Env_laboratory$Cumul_Precipitation
-+ }
-> if (variable_x=="Cumul_Precipitation"){
-+
-+ i_var_x_min<-i_cum_rain_min
-+ i_var_x_max<-i_cum_rain_max
-+ breaks_var_x<-breaks_cum_rain
-+ }
->
-> if (variable_y=="Cumul_Precipitation"){
-+
-+ i_var_y_min<-i_cum_rain_min
-+ i_var_y_max<-i_cum_rain_max
-+ breaks_var_y<-breaks_cum_rain
-+ }
->
->
-> if (variable_z=="Mean_wind_speed"){
-+
-+ breaks_var<-breaks_wind
-+ i_var_min<-i_wind_min
-+ i_var_max<-i_wind_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$Mean_wind_speed
-+ Env_laboratory_var<-Env_laboratory$Mean_wind_speed
-+ }
-> if (variable_x=="Mean_wind_speed"){
-+
-+ i_var_x_min<-i_wind_min
-+ i_var_x_max<-i_wind_max
-+ breaks_var_x<-breaks_wind
-+ }
->
-> if (variable_y=="Mean_wind_speed"){
-+
-+ i_var_y_min<-i_wind_min
-+ i_var_y_max<-i_wind_max
-+ breaks_var_y<-breaks_wind
-+ }
->
->
->
->
-> if (variable_z=="daylength"){
-+
-+ breaks_var<-breaks_light
-+ i_var_min<-i_light_min
-+ i_var_max<-i_light_max
-+ Env_Campylobacter_data_var<-Env_Campylobacter_data$daylength
-+ Env_laboratory_var<-Env_laboratory$daylength
-+ }
-> if (variable_x=="daylength"){
-+
-+ i_var_x_min<-i_light_min
-+ i_var_x_max<-i_light_max
-+ breaks_var_x<-breaks_light
-+ }
->
-> if (variable_y=="daylength"){
-+
-+ i_var_y_min<-i_light_min
-+ i_var_y_max<-i_light_max
-+ breaks_var_y<-breaks_light
-+ }
->
->
->
->
-> D_eta_var_x<-function(i_var,i_var_y,i_var_x)
-+ {
-+
-+
-+
-+
-+ #var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-+ var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab #All_residents_lab[,2] this change by postcode only
-+
-+ if(is.na(breaks_var[i_var+1])==TRUE)
-+ {
-+ Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var])
-+ } else {
-+ Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var] & var_x_loc_df$daylength<breaks_var[i_var+1])
-+ }
-+
-+
-+ if(is.na(breaks_var_y[i_var_y+1])==TRUE)
-+ {
-+ Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y])
-+ } else {
-+ Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y] & Yt0$Relative_humidity<breaks_var_y[i_var_y+1])
-+ }
-+
-+
-+
-+ if(is.na(breaks_var_x[i_var_x+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x] & Yt1$Maximum_air_temperature<breaks_var_x[i_var_x+1])
-+ }
-+
-+
-+ if(is.na(breaks_var_x[i_var_x+2])==TRUE)
-+ {
-+ Yt3<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x+1])
-+ } else {
-+ Yt3<-subset(Yt1,Yt1$Maximum_air_temperature>=breaks_var_x[i_var_x+1] & Yt1$Maximum_air_temperature<breaks_var_x[i_var_x+2])
-+ }
-+
-+ if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
-+ D_eta_D_x<- (mean(Yt3$eta- Yt2$eta))/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
-+ } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
-+ D_eta_D_x<- 0.5*mean(Yt3$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
-+ } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
-+ D_eta_D_x<- 0.5*mean(Yt2$eta)/(breaks_var_x[i_var_x+1]-breaks_var_x[i_var_x])
-+ } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
-+ D_eta_D_x<-NA
-+ }
-+
-+ return(D_eta_D_x)
-+ }
->
->
->
->
-> D_eta_var_y<-function(i_var,i_var_y,i_var_x)
-+ {
-+
-+
-+ #var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-+ var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab
-+
-+
-+ if(is.na(breaks_var[i_var+1])==TRUE)
-+ {
-+ Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var])
-+ } else {
-+ Yt0<-subset(var_x_loc_df,var_x_loc_df$daylength>=breaks_var[i_var] & var_x_loc_df$daylength<breaks_var[i_var+1])
-+ }
-+
-+
-+ if(is.na(breaks_var_x[i_var_x+1])==TRUE)
-+ {
-+ Yt1<-subset(Yt0,Yt0$Maximum_air_temperature>=breaks_var_x[i_var_x])
-+ } else {
-+ Yt1<-subset(Yt0,Yt0$Maximum_air_temperature>=breaks_var_x[i_var_x] & Yt0$Maximum_air_temperature<breaks_var_x[i_var_x+1])
-+ }
-+
-+
-+
-+ if(is.na(breaks_var_y[i_var_y+1])==TRUE)
-+ {
-+ Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y])
-+ } else {
-+ Yt2<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y] & Yt1$Relative_humidity<breaks_var_y[i_var_y+1])
-+ }
-+
-+
-+ if(is.na(breaks_var_y[i_var_y+2])==TRUE)
-+ {
-+ Yt3<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y+1])
-+ } else {
-+ Yt3<-subset(Yt1,Yt1$Relative_humidity>=breaks_var_y[i_var_y+1] & Yt1$Relative_humidity<breaks_var_y[i_var_y+2])
-+ }
-+
-+ if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
-+ D_eta_D_y<- (mean(Yt3$eta- Yt2$eta))/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
-+ } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
-+ D_eta_D_y<- 0.5*mean(Yt3$eta)/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
-+ } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
-+ D_eta_D_y<- 0.5*mean(Yt2$eta)/(breaks_var_y[i_var_y+1]-breaks_var_y[i_var_y])
-+ } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
-+ D_eta_D_y<-NA
-+ }
-+
-+ return(D_eta_D_y)
-+ }
->
->
->
->
-> D_eta_var<-function(i_var,i_var_y,i_var_x)
-+ {
-+
-+
-+ #var_x_loc_df$eta<-var_x_loc_df$prop*var_x_loc_df$Numb_Lab
-+ var_x_loc_df$eta<-var_x_loc_df$incidence*All_residents_lab[,2]*var_x_loc_df$Numb_Lab
-+
-+ if(is.na(breaks_var_x[i_var_x+1])==TRUE)
-+ {
-+ Yt0<-subset(var_x_loc_df,var_x_loc_df$Maximum_air_temperature>=breaks_var_x[i_var_x])
-+ } else {
-+ Yt0<-subset(var_x_loc_df,var_x_loc_df$Maximum_air_temperature>=breaks_var_x[i_var_x] & var_x_loc_df$Maximum_air_temperature<breaks_var_x[i_var_x+1])
-+ }
-+
-+ if(is.na(breaks_var_y[i_var_y+1])==TRUE)
-+ {
-+ Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y])
-+ } else {
-+ Yt1<-subset(Yt0,Yt0$Relative_humidity>=breaks_var_y[i_var_y] & Yt0$Relative_humidity<breaks_var_y[i_var_y+1])
-+ }
-+
-+
-+ if(is.na(breaks_var[i_var+1])==TRUE)
-+ {
-+ Yt2<-subset( Yt1, Yt1$daylength>=breaks_var[i_var])
-+ } else {
-+ Yt2<-subset( Yt1, Yt1$daylength>=breaks_var[i_var] & Yt1$daylength<breaks_var[i_var+1])
-+ }
-+
-+ if(is.na(breaks_var[i_var+2])==TRUE)
-+ {
-+ Yt3<-subset( Yt1, Yt1$daylength>=breaks_var[i_var+1])
-+ } else {
-+ Yt3<-subset( Yt1, Yt1$Relative_humiditylight>=breaks_var[i_var+1] & Yt1$daylength<breaks_var[i_var+2])
-+ }
-+
-+
-+
-+ if(length(Yt3[,1])!=0 & length(Yt2[,1])!=0){
-+ D_eta_D_v<- mean(Yt3$eta- Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
-+ } else if (length(Yt3[,1])!=0 & length(Yt2[,1])==0){
-+ D_eta_D_v<- 0.5*mean(Yt3$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
-+ } else if (length(Yt3[,1])==0 & length(Yt2[,1])!=0){
-+ D_eta_D_v<- 0.5*mean(Yt2$eta)/(breaks_var[i_var+1]-breaks_var[i_var])
-+ } else if (length(Yt3[,1])==0 & length(Yt2[,1])==0){
-+ D_eta_D_v<-NA
-+ }
-+
-+ return(D_eta_D_v)
-+ }
->
->
->
->
->
-> df_all_1<-c()
-> #i_var<-seq(i_var_min,i_var_max)
-> #i_var_x<-seq(i_var_x_min,i_var_x_max)
->
-> for (j in c(1: length(All_PC_s))){
-+
-+ var_x_loc_df<-var_x_loc_df_all2
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[j])
-+
-+ All_residents_lab<-ddply(variable_df,~PostCode,summarise,tot=mean(residents))
-+ for (i_var in c(seq(i_var_min,i_var_max))){
-+ for (i_var_y in c(seq(i_var_y_min,i_var_y_max))){
-+ for (i_var_x in c(seq(i_var_x_min,i_var_x_max))){
-+
-+
-+ #df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-+ #df1<-data.frame(i_var,i_var_x,t(unlist(lapply(i_var,D_eta_var_x,i_var_x=i_var_x))))
-+
-+ df1<-data.frame(breaks_light[i_var],breaks_hum[i_var_y],breaks_max_temp[i_var_x],D_eta_var_x(i_var,i_var_y,i_var_x),D_eta_var_y(i_var,i_var_y,i_var_x),D_eta_var(i_var,i_var_y,i_var_x),All_PC_s[j] )
-+ colnames(df1)<-c("daylength","Relative_humidity","Maximum_air_temperature","D_eta_D_max_temp","D_eta_D_hum","D_eta_D_light","PostCode")
-+ df_all_1<-rbind(df_all_1,df1)
-+ #mapply(D_eta_var_x,i_var, MoreArgs =list(i_var_x))
-+ }
-+ }
-+
-+ }
-+ }
->
-> #df_all<-ddply(df_all_1,~PostCode,summarise,tot=mean())
-> df_all<-df_all_1
->
-> time_series<-c()
->
-> for (i in c(1: length(All_PC_s))){
-+ #for (i in c(1: 15)){
-+
-+
-+
-+ #for (j in c(1: n_seas)){
-+
-+ # n_months<-12/n_seas
-+
-+ variable_df<-subset(Env_laboratory,Env_laboratory$PostCode==All_PC_s[i])
-+ if (length(variable_df[,1])!=0){
-+
-+
-+
-+ Delta_T<-diff(variable_df$Maximum_air_temperature)
-+ Delta_RH<-diff(variable_df$Relative_humidity)
-+ Delta_L<-diff(variable_df$daylength)
-+ Delta_var<-data.frame(variable_df$Date[-1],Delta_T,Delta_RH,Delta_L)
-+ colnames(Delta_var)<-c("dates","diff_max_temp","diff_humidity","diff_light")
-+
-+ x<-variable_df$Relative_humidity
-+ y<-variable_df$Maximum_air_temperature
-+ z<-variable_df$daylength
-+
-+ variable_df_1_dis<-data.frame(variable_df$Date,variable_df$Relative_humidity)
-+ variable_df_2_dis<-data.frame(variable_df$Date,variable_df$Maximum_air_temperature)
-+ variable_df_3_dis<-data.frame(variable_df$Date,variable_df$daylength)
-+
-+ colnames(variable_df_1_dis)<-c("dates","Relative_humidity")
-+ colnames(variable_df_2_dis)<-c("dates","Maximum_air_temperature")
-+ colnames(variable_df_3_dis)<-c("dates","daylength")
-+
-+ variable_df_1_dis$dates<-as.Date(as.character((variable_df_1_dis$dates)))
-+ variable_df_2_dis$dates<-as.Date(as.character((variable_df_2_dis$dates)))
-+ variable_df_3_dis$dates<-as.Date(as.character((variable_df_3_dis$dates)))
-+
-+ variable_df_1_dis<-subset(variable_df_1_dis,year(variable_df_1_dis$dates)>=1990 & year(variable_df_1_dis$dates)<=2015)
-+ variable_df_2_dis<-subset(variable_df_2_dis,year(variable_df_2_dis$dates)>=1990 & year(variable_df_2_dis$dates)<=2015)
-+ variable_df_3_dis<-subset(variable_df_3_dis,year(variable_df_3_dis$dates)>=1990 & year(variable_df_3_dis$dates)<=2015)
-+
-+ variable_df_1_dis$Relative_humidity<-(breaks_hum[findInterval(x, breaks_hum)])
-+ variable_df_2_dis$Maximum_air_temperature<-(breaks_max_temp[findInterval(y, breaks_max_temp)])
-+ variable_df_3_dis$daylength<-(breaks_light[findInterval(z, breaks_light)])
-+
-+
-+ # var_x_loc_df<-subset(var_x_loc_df_all2,var_x_loc_df_all2$month==j)
-+ # var_x_loc_df$Relative_humidity<-floor(var_x_loc_df$Relative_humidity)
-+ # var_x_loc_df$Maximum_air_temperature<-floor(var_x_loc_df$breaks)
-+ #df<-subset(df_all,df_all$month==j)
-+ df<-subset(df_all,df_all$PostCode==All_PC_s[i])
-+
-+ variable_df_4_dis<-merge(variable_df_1_dis,df, by="Relative_humidity")
-+ variable_df_5_dis<-merge(variable_df_2_dis,df, by="Maximum_air_temperature")
-+ variable_df_6_dis<-merge(variable_df_3_dis,df, by="daylength")
-+
-+
-+
-+ variable_df_4_dis$dates<-as.Date(as.character((variable_df_4_dis$dates)))
-+ variable_df_4_dis<-variable_df_4_dis[order(variable_df_4_dis$dates),]
-+ variable_df_5_dis$dates<-as.Date(as.character((variable_df_5_dis$dates)))
-+ variable_df_5_dis<-variable_df_5_dis[order(variable_df_5_dis$dates),]
-+ variable_df_6_dis$dates<-as.Date(as.character((variable_df_6_dis$dates)))
-+ variable_df_6_dis<-variable_df_6_dis[order(variable_df_6_dis$dates),]
-+
-+
-+ variable_df_4_dis$dates<-as.factor(variable_df_4_dis$dates)
-+ variable_df_5_dis$dates<-as.factor(variable_df_5_dis$dates)
-+ variable_df_6_dis$dates<-as.factor(variable_df_6_dis$dates)
-+
-+ variable_df_dis0<-merge(variable_df_4_dis,variable_df_5_dis, by=c("dates","Maximum_air_temperature","Relative_humidity","daylength") )
-+ variable_df_dis<-merge(variable_df_dis0,variable_df_6_dis, by=c("dates","Maximum_air_temperature","Relative_humidity","daylength"))
-+
-+ variable_df_dis$dates<-as.Date(as.character((variable_df_dis$dates)))
-+ variable_df_dis<-variable_df_dis[order(variable_df_dis$dates),]
-+ variable_df_dis$dates<-as.factor(variable_df_dis$dates)
-+ Delta_var$dates<-as.factor(Delta_var$dates)
-+ variable_df_Taylor<-merge(variable_df_dis,Delta_var, by=c("dates"))
-+
-+ variable_df_Taylor<-variable_df_Taylor[,c(1:7,17:19)]
-+ colnames(variable_df_Taylor)<-c("dates",variable_x, variable_y, variable_z,"D_eta_D_max_temp","D_eta_D_hum","D_eta_D_light",
-+ "diff_max_temp","diff_humidity","diff_light")
-+
-+
-+ lambda_temp<-variable_df_Taylor$D_eta_D_max_temp*variable_df_Taylor$diff_max_temp
-+ lambda_hum<-variable_df_Taylor$D_eta_D_hum*variable_df_Taylor$diff_humidity
-+ lambda_light<-variable_df_Taylor$D_eta_D_light*variable_df_Taylor$diff_light
-+
-+
-+
-+ time_series_1<-
-+ data.frame(variable_df_Taylor$dates,
-+ lambda_temp,lambda_hum,lambda_light,
-+ rep(All_PC_s[i],times=length(variable_df_Taylor$dates)),
-+ variable_df_Taylor$D_eta_D_max_temp,variable_df_Taylor$diff_max_temp,
-+ variable_df_Taylor$D_eta_D_hum,variable_df_Taylor$diff_humidity,
-+ variable_df_Taylor$D_eta_D_light,variable_df_Taylor$diff_light)
-+ colnames(time_series_1)<-c("Date","Contr_temp","Contr_hum","Contr_light",
-+ "Lab","delta_eta_temp","delta_temp","delta_eta_hum","delta_hum","delta_eta_light","delta_light")
-+ time_series<-rbind(time_series,time_series_1)
-+
-+ #print(100*c(i/length(All_PC_s),comp_cases,comp_cases2,comp_cases3,lambda ))
-+ print(100*c(i/length(All_PC_s) ))
-+ #print(" ")
-+ #print(lambda)
-+ }
-+ }
-[1] 0.4854369
-[1] 0.9708738
-[1] 1.456311
-[1] 1.941748
-[1] 2.427184
-[1] 2.912621
-[1] 3.398058
-[1] 3.883495
-[1] 4.368932
-[1] 4.854369
-[1] 5.339806
-[1] 5.825243
-[1] 6.31068
-[1] 6.796117
-[1] 7.281553
-[1] 7.76699
-[1] 8.252427
-[1] 8.737864
-[1] 9.223301
-[1] 9.708738
-[1] 10.19417
-[1] 10.67961
-[1] 11.16505
-[1] 11.65049
-[1] 12.13592
-[1] 12.62136
-[1] 13.1068
-[1] 13.59223
-[1] 14.07767
-[1] 14.56311
-[1] 15.04854
-[1] 15.53398
-[1] 16.01942
-[1] 16.50485
-[1] 16.99029
-[1] 17.47573
-[1] 17.96117
-[1] 18.4466
-[1] 18.93204
-[1] 19.41748
-[1] 19.90291
-[1] 20.38835
-[1] 20.87379
-[1] 21.35922
-[1] 21.84466
-[1] 22.3301
-[1] 22.81553
-[1] 23.30097
-[1] 23.78641
-[1] 24.27184
-[1] 24.75728
-[1] 25.24272
-[1] 25.72816
-[1] 26.21359
-[1] 26.69903
-[1] 27.18447
-[1] 27.6699
-[1] 28.15534
-[1] 28.64078
-[1] 29.12621
-[1] 29.61165
-[1] 30.09709
-[1] 30.58252
-[1] 31.06796
-[1] 31.5534
-[1] 32.03883
-[1] 32.52427
-[1] 33.00971
-[1] 33.49515
-[1] 33.98058
-[1] 34.46602
-[1] 34.95146
-[1] 35.43689
-[1] 35.92233
-[1] 36.40777
-[1] 36.8932
-[1] 37.37864
-[1] 37.86408
-[1] 38.34951
-[1] 38.83495
-[1] 39.32039
-[1] 39.80583
-[1] 40.29126
-[1] 40.7767
-[1] 41.26214
-[1] 41.74757
-[1] 42.23301
-[1] 42.71845
-[1] 43.20388
-[1] 43.68932
-[1] 44.17476
-[1] 44.66019
-[1] 45.14563
-[1] 45.63107
-[1] 46.1165
-[1] 46.60194
-[1] 47.08738
-[1] 47.57282
-[1] 48.05825
-[1] 48.54369
-[1] 49.02913
-[1] 49.51456
-[1] 50
-[1] 50.48544
-[1] 50.97087
-[1] 51.45631
-[1] 51.94175
-[1] 52.42718
-[1] 52.91262
-[1] 53.39806
-[1] 53.8835
-[1] 54.36893
-[1] 54.85437
-[1] 55.33981
-[1] 55.82524
-[1] 56.31068
-[1] 56.79612
-[1] 57.28155
-[1] 57.76699
-[1] 58.25243
-[1] 58.73786
-[1] 59.2233
-[1] 59.70874
-[1] 60.19417
-[1] 60.67961
-[1] 61.16505
-[1] 61.65049
-[1] 62.13592
-[1] 62.62136
-[1] 63.1068
-[1] 63.59223
-[1] 64.07767
-[1] 64.56311
-[1] 65.04854
-[1] 65.53398
-[1] 66.01942
-[1] 66.50485
-[1] 66.99029
-[1] 67.47573
-[1] 67.96117
-[1] 68.4466
-[1] 68.93204
-[1] 69.41748
-[1] 69.90291
-[1] 70.38835
-[1] 70.87379
-[1] 71.35922
-[1] 71.84466
-[1] 72.3301
-[1] 72.81553
-[1] 73.30097
-[1] 73.78641
-[1] 74.27184
-[1] 74.75728
-[1] 75.24272
-[1] 75.72816
-[1] 76.21359
-[1] 76.69903
-[1] 77.18447
-[1] 77.6699
-[1] 78.15534
-[1] 78.64078
-[1] 79.12621
-[1] 79.61165
-[1] 80.09709
-[1] 80.58252
-[1] 81.06796
-[1] 81.5534
-[1] 82.03883
-[1] 82.52427
-[1] 83.00971
-[1] 83.49515
-[1] 83.98058
-[1] 84.46602
-[1] 84.95146
-[1] 85.43689
-[1] 85.92233
-[1] 86.40777
-[1] 86.8932
-[1] 87.37864
-[1] 87.86408
-[1] 88.34951
-[1] 88.83495
-[1] 89.32039
-[1] 89.80583
-[1] 90.29126
-[1] 90.7767
-[1] 91.26214
-[1] 91.74757
-[1] 92.23301
-[1] 92.71845
-[1] 93.20388
-[1] 93.68932
-[1] 94.17476
-[1] 94.66019
-[1] 95.14563
-[1] 95.63107
-[1] 96.1165
-[1] 96.60194
-[1] 97.08738
-[1] 97.57282
-[1] 98.05825
-[1] 98.54369
-[1] 99.02913
-[1] 99.51456
-[1] 100
->
-> #time_series1<-subset(time_series,time_series$Lab=="B152TG")
-> #time_series1$Date<-as.Date(as.character(time_series1$Date))
-> #time_series1$year<-as.factor(year(as.Date(as.character(time_series1$Date))))
-> #time_series1<-subset(time_series1,time_series1$year=="2005")
->
-> #time_series1<-na.omit(time_series1)
-> ##temp_contr<-cumsum(time_series1$Contr_temp)
-> #temp_hum<-cumsum(time_series1$Contr_hum)
-> #
-> #df2<-data.frame(as.Date(as.character(time_series1$Date)),temp_contr,temp_hum)
-> #colnames(df2)<-c("Date","contr","humidity")
-> #temp_contr<-ddply(time_series1,~year,summarise,Cum_Cases=cumsum(Contr_temp))
-> #time_series<-na.omit(time_series)
-> #time_series$temp_contr<-cumsum(time_series$Contr_temp)
-> #time_series$temp_hum<-cumsum(time_series$Contr_hum)
-> #time_series$yday<-yday(time_series$Date)
-> #time_series$week<-week(time_series$Date)
-> #time_series$month<-month(time_series$Date)
-> #time_series$Lab<-as.factor(time_series$Lab)
->
-> #time_series_lab<-ddply(time_series,~Date,summarise,tot=mean(Contr_temp))
-> #time_series_lab2<-ddply(time_series,~yday,summarise,tot=mean(Contr_temp))
->
-> #time_series_lab<-ddply(time_series,~week,summarise,tot=mean(Contr_temp))
-> #time_series_lab2<-ddply(time_series,~week,summarise,tot=mean(Contr_hum))
->
-> #time_series_lab<-ddply(time_series,~month,summarise,tot=mean(Contr_temp))
-> #time_series_lab2<-ddply(time_series,~month,summarise,tot=mean(Contr_hum))
-> #time_series_lab2<-ddply(time_series,~Date,summarise,tot=mean(Contr_hum))
->
-> write.table(time_series,paste("../../Data_Base/Cases/Taylor_contribution_Time_series_",variable_z,"_",variable_y,"_",variable_x,width_char,"_Simulated_original_MEDMI.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
->
-> proc.time()
- user system elapsed
-193723.047 4481.237 198225.164