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Commit 40b8723d authored by Lo Iacono, Giovanni Dr (School of Vet Med.)'s avatar Lo Iacono, Giovanni Dr (School of Vet Med.)
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Delete PAPER_Conditional_probability_quantile_original_MEDMI.Rout

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R version 3.5.3 (2019-03-11) -- "Great Truth"
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[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_x<-"Minimum_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",
> #variable_y<-"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_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))
+
+ }}}
+ }
+
+
+ }}
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[1] 3 2 3 16150
[1] 4 2 3 16992
[1] 5 2 3 14596
[1] 6 2 3 13371
[1] 7 2 3 14098
[1] 8 2 3 15812
[1] 9 2 3 10176
[1] 10 2 3 234
[1] 1 3 3 17392
[1] 2 3 3 19078
[1] 3 3 3 20950
[1] 4 3 3 16899
[1] 5 3 3 14247
[1] 6 3 3 14301
[1] 7 3 3 12903
[1] 8 3 3 12235
[1] 9 3 3 4330
[1] 10 3 3 466
[1] 1 4 3 26419
[1] 2 4 3 23008
[1] 3 4 3 18176
[1] 4 4 3 14720
[1] 5 4 3 13315
[1] 6 4 3 10989
[1] 7 4 3 8562
[1] 8 4 3 7437
[1] 9 4 3 7089
[1] 10 4 3 1464
[1] 1 1 4 22461
[1] 2 1 4 21072
[1] 3 1 4 19861
[1] 4 1 4 17345
[1] 5 1 4 14625
[1] 6 1 4 10554
[1] 7 1 4 9255
[1] 8 1 4 7423
[1] 9 1 4 1941
[1] 10 1 4 0
[1] 1 2 4 15750
[1] 2 2 4 14027
[1] 3 2 4 15929
[1] 4 2 4 19809
[1] 5 2 4 20409
[1] 6 2 4 18734
[1] 7 2 4 14422
[1] 8 2 4 7644
[1] 9 2 4 1299
[1] 10 2 4 1
[1] 1 3 4 17374
[1] 2 3 4 17005
[1] 3 3 4 19541
[1] 4 3 4 18688
[1] 5 3 4 19172
[1] 6 3 4 18817
[1] 7 3 4 12496
[1] 8 3 4 4924
[1] 9 3 4 1242
[1] 10 3 4 54
[1] 1 4 4 25124
[1] 2 4 4 21675
[1] 3 4 4 23812
[1] 4 4 4 22987
[1] 5 4 4 17857
[1] 6 4 4 11482
[1] 7 4 4 3924
[1] 8 4 4 939
[1] 9 4 4 488
[1] 10 4 4 133
>
>
> write.csv(var_x_loc_df,paste("../../Data_Base/Cases_Environment/Conditional_probability_",variable,"_",variable_y,"_",variable_x,"_",width_char,"_Simulated_for_rec_original_MEDMI_quantile.csv",sep=""))
>
> proc.time()
user system elapsed
715.909 10.599 726.577
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