diff --git a/Campylobacter_environment_analysis_subset_one_variable_quantile_quarters.R b/Campylobacter_environment_analysis_subset_one_variable_quantile_quarters.R
deleted file mode 100644
index b51fa3c63224f1c40c6dcffc46bc2391e70ed73d..0000000000000000000000000000000000000000
--- a/Campylobacter_environment_analysis_subset_one_variable_quantile_quarters.R
+++ /dev/null
@@ -1,293 +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<-30
-width_char<-paste(width)
-
-
-
-variable<-"daylength"
-
-
-#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<-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,quarter_char,"_Simulated_for_rec_original_MEDMI.csv",sep=""))