diff --git a/Campylobacter_environment_analysis_subset_variables_hum_rain.Rout b/Campylobacter_environment_analysis_subset_variables_hum_rain.Rout
deleted file mode 100644
index 30ce4ae163e5f02f298cf0d76c0ae784e1d6b232..0000000000000000000000000000000000000000
--- a/Campylobacter_environment_analysis_subset_variables_hum_rain.Rout
+++ /dev/null
@@ -1,491 +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 consider only two variables at a time: Relative humidity and maximum air temeprature
-> # It uses original MEDMI data
-> 
-> 
-> 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)
-> 
-> 
-> 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
-
-> 
-> 
-> 
-> ## Variable 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
-> humidity<-unlist(c(humidity))
-> #wt<-which(humidity<=0)
-> #humidity[wt]<-NA
-> 
-> 
-> 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))
-> #wt<-which(max_temp>=39)
-> #max_temp[wt]<-NA
-> 
-> 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
-> min_temp<-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
-> rain<-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
-> wind<-unlist(c(wind))
-> 
-> 
-> width<-14
->   width_char<-paste(width)
-> 
-> ######################## Catchment areas #################
-> 
-> population_df<-read.csv(paste("../../Data_Base/Catchment_areas/LSOA2011Population_SumByLabs.csv",sep=""))
-> colnames(population_df)<-c("PostCode","col2","col3","residents")
-> 
-> 
-> 
-> merged_lab_all2<-data.frame(All_PC,dates,humidity,max_temp,min_temp,rain,wind)
-> colnames(merged_lab_all2)<-c("PostCode","dates","humidity",  "max_temp", "min_temp","rain","wind")
-> merged_lab_all<-merge(merged_lab_all2, population_df,by="PostCode")
-> #
-> #merged_lab<-data.frame(dates,humidity,max_temp,min_temp)
-> merged.data_all<-read.csv("../../Data_Base/OPIE_data_base/OPIE_environment_Campylobacter.csv")
-> 
-> 
-> merged.data_all[,2]<-as.Date((as.character(merged.data_all[,2])))
-> merged.data_all2<-merged.data_all
-> merged.data_all<-merge(merged.data_all2, population_df,by="PostCode")
-> 
-> 
-> 
-> ########################### Weekly average ######################
-> 
-> week_cases<-function(lab_fac){
-+   lab<-as.character(lab_fac)
-+   merged_sub<-subset(merged.data_all,merged.data_all$PostCode==lab)
-+   
-+   if (length(merged_sub[,1])!=0){
-+     
-+     merged_sub2<-merged_sub[order(as.Date(merged_sub$Date, format="%Y-%m-%d")),]
-+     
-+     ep <- endpoints(as.xts(merged_sub2$Date),'weeks')
-+     cum_Cases<-period.apply(merged_sub2$Cases, INDEX=ep, FUN=function(x) sum(na.omit(x,na.rm=TRUE)))
-+     
-+     
-+     
-+     PC<-rep(as.character(unique(merged_sub$PostCode)),times=length(ep)-1)
-+     merged_weekly<-data.frame(PC,merged_sub$Date[ep],cum_Cases)
-+     return(merged_weekly)}
-+ }
-> 
-> 
-> week_PC<-function(lab_fac){
-+   lab<-as.character(lab_fac)
-+   merged_lab_sub<-subset(merged_lab_all,merged_lab_all$PostCode==lab)
-+   merged_lab_sub2<-merged_lab_sub[order(as.Date(merged_lab_sub$dates, format="%Y-%m-%d")),]
-+   
-+ 
-+   
-+   mean_humidity<-rollmean(merged_lab_sub2$humidity,width)
-+   mean_max_temp<-rollmean(merged_lab_sub2$max_temp, width)
-+   mean_min_temp<-rollmean(merged_lab_sub2$min_temp, width)
-+   mean_rain<-rollmean(merged_lab_sub2$rain, width)
-+   cum_rain<-rollsum(merged_lab_sub2$rain, width)
-+   mean_wind<-rollmean(merged_lab_sub2$wind,width)
-+   mean_residents<-rollmean(merged_lab_sub2$residents, width)
-+ 
-+   PC<-rep(as.character(unique(merged_lab_sub$PostCode)),times=length(mean_residents))
-+   ep<-seq(width,length(mean_residents)+width-1)
-+   
-+   merged_lab_weekly<-data.frame(PC,dates[ep],mean_humidity,mean_max_temp,mean_min_temp,mean_rain,cum_rain,mean_wind,mean_residents)
-+   return(merged_lab_weekly)
-+ }
-> 
-> merged_lab_weekly<-c()
-> #merged_cases_weekly<-c()
-> 
-> index_PC<-unique(merged_lab_all$PostCode)
-> for (i in c(1:length(index_PC))){
-+   merged_lab_weekly<-rbind(merged_lab_weekly,lapply(index_PC[i], week_PC)[[1]])
-+   
-+   #merged_cases_weekly<-rbind(merged_cases_weekly,lapply(index_PC[i], week_cases)[[1]])
-+   print(100*i/length(index_PC))
-+ }
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-> 
-> 
-> 
-> 
-> 
-> 
-> colnames(merged_lab_weekly)<-c("PostCode","Date","mean_humidity","mean_max_temp","mean_min_temp","mean_rain","cum_rain","mean_wind","mean_residents")
-> #write.table(merged_lab_weekly,paste("../../Data_Base/OPIE_data_base/Laboratory_",width_char,"_test.csv",sep=""), col.names = FALSE, sep = ",",eol = "\n")
-> 
-> 
-> merged.data_all$Cases<-1
-> merged.data_all_weekly<-merge(merged.data_all, merged_lab_weekly,by=c("PostCode","Date"))
-> #merged.data_all_weekly<-merge(merged.data_all_weekly2, merged_cases_weekly,by=c("PostCode","Date"))
-> 
-> 
-> merged_lab_all<-merged_lab_weekly
-> #sort(merged_lab_all)
-> merged.data_all<-data.frame(merged.data_all_weekly$PostCode, 
-+                             merged.data_all_weekly$Date,
-+                             merged.data_all_weekly$Cases,
-+                             merged.data_all_weekly$mean_humidity, 
-+                             merged.data_all_weekly$mean_max_temp, 
-+                             merged.data_all_weekly$mean_min_temp,
-+                             merged.data_all_weekly$mean_rain,
-+                             merged.data_all_weekly$cum_rain,
-+                             merged.data_all_weekly$mean_wind, 
-+                             merged.data_all_weekly$mean_residents)
-> 
-> colnames(merged.data_all)<-c("PostCode","Date","Cases","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-> colnames(merged_lab_all)<-c("PostCode","Date","humidity","max_temp","min_temp","rain","cum_rain","wind_speed","residents")
-> 
-> merged.data_all<-merged.data_all[order(as.Date(merged.data_all$Date)),]
-> 
-> ########################### END Weekly average ######################
-> 
-> 
-> #PHE_Centre<-merged.data_all$PHE_Centre_Name
-> #n_Centre<-length(levels(PHE_Centre))
-> #i_centre<-6
-> #For Dorset only
-> #merged.data_PHE<-subset(merged.data_all,merged.data_all$PHE_Centre_Name==levels(PHE_Centre)[i_centre])
-> #merged.data<-subset(merged.data_PHE,year(merged.data_PHE$Date)>=2000 & year(merged.data_PHE$Date)<2016)
-> merged.data<-subset(merged.data_all,year(merged.data_all$Date)>=1990 & year(merged.data_all$Date)<=2015)
-> merged_lab<-subset(merged_lab_all,year(merged_lab_all$Date)>=1990 & year(merged_lab_all$Date)<=2015)
-> ################### weekly summary
-> 
-> 
-> ###################
-> 
-> 
-> delta_hum<-5
-> delta_temp<-1
-> delta_rain<-1
-> delta_wind<-0.5
-> breaks_hum<-seq(max(min(na.omit(merged_lab_weekly$mean_humidity))-10,0),max(na.omit(merged_lab_weekly$mean_humidity))+10,by=delta_hum) #i
-> breaks_min_temp<-seq(min(na.omit(merged_lab_weekly$mean_min_temp))-2,   max(na.omit(merged_lab_weekly$mean_min_temp))+2,by=delta_temp)
-> breaks_max_temp<-seq(min(na.omit(merged_lab_weekly$mean_max_temp))-2,   max(na.omit(merged_lab_weekly$mean_max_temp))+2,by=delta_temp)
-> breaks_rain<-seq(min(na.omit(merged_lab_weekly$mean_rain))-1,   max(na.omit(merged_lab_weekly$mean_rain))+1,by=delta_rain)
-> breaks_wind<-seq(max(min(na.omit(merged_lab_weekly$mean_wind))-0.5,0),max(na.omit(merged_lab_weekly$mean_wind))+0.5,by=delta_wind)
-> #breaks_wind<-seq(max(min(na.omit(wind))-2,0),max(na.omit(wind))+2,by=delta_wind) #WARNING
-> breaks_cum_rain<-seq(min(na.omit(merged_lab_weekly$cum_rain))-1,   max(na.omit(mergedab_weekly$cum_rain))+1,by=delta_rain)
-Error in na.omit(mergedab_weekly$cum_rain) : 
-  object 'mergedab_weekly' not found
-Calls: seq -> seq.default -> na.omit
-Execution halted