diff --git a/paper_plot_presentation_variables.R b/paper_plot_presentation_variables.R
deleted file mode 100644
index 18f6b2dabc95872cfbf0473a4a13e38f51fd830b..0000000000000000000000000000000000000000
--- a/paper_plot_presentation_variables.R
+++ /dev/null
@@ -1,402 +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(zoo)
-library(plyr)
-
-
-width<-60
-width_char<-paste(width)
-situation<-"_const_hum_70"
-#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))
-
-time_series_1<-variable_df_1[-c(1,2),]
-#names(time_series_1)<-NULL
-
-time_series_2<-as.data.frame(time_series_1)[,-1]
-time_series<-c()
-time_series$Date<-rep(as.Date(time_series_2[,1]),times=length(time_series_2)-1)
-time_series$humidity<-c(unlist(time_series_2[,-1]))
-
-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_mean<-apply(time_series,1,mean)
-#time_series_mean<-apply(time_series,1,mean)
-
-hum_df<-data.frame(time_series)
-colnames(hum_df)<-c("Date","humidity")
-hum_df$month<-month(hum_df$Date)
-hum_df$week<-month(hum_df$Date)
-
-
-
-
-############## Average per day of the year #################
-rownames(hum_df)<-NULL
-
-hum_df$yday<-as.factor(yday(hum_df$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$Cases4)
-time_series_average1<-ddply(hum_df,~yday,summarise,mean=mean(humidity))
-time_series_quantile1<-ddply(hum_df,~yday, function (x) quantile(x$humidity, 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)])
-
-
-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<-df2
-average_data$Day<-as.numeric(average_data$Day)
-
-yearly_average<-ggplot(average_data,aes(x=Day,y=Mean))
-
-yearly_average<-yearly_average+geom_line(size=2,colour="blue")
-yearly_average <-yearly_average+ geom_ribbon(aes(ymin=f_quant, ymax=s_quant),fill="blue",alpha=0.25)
-yearly_average<-yearly_average+ xlab("Day of the Year")+ ylab("Humidity %")
-
-
-#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,"n_seas",n_seas,"_multiple_delays.pdf", sep = "")
-file_name_pdf<-paste("../../Graphs/yearly_average_humidity.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_humidity.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()
-
-
-
-############## temperature #################
-
-
-
-
-
-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))
-
-
-
-dates_s<-as.Date(as.character(variable_df_2[-c(1,2),2]),format="%Y-%m-%d")
-dates<-rep(dates_s,times=length(variable_df_2)-2)
-All_PC_s<-names(variable_df_2[1,])
-All_PC_s<-All_PC_s[-c(1,2)]
-All_PC<-rep(All_PC_s,each=length(dates_s))
-
-time_series_1<-variable_df_2[-c(1,2),]
-#names(time_series_1)<-NULL
-
-time_series_2<-as.data.frame(time_series_1)[,-1]
-time_series<-c()
-time_series$Date<-rep(as.Date(time_series_2[,1]),times=length(time_series_2)-1)
-time_series$max_air_temp<-c(unlist(time_series_2[,-1]))
-
-time_series$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$Lab)
-
-#time_series_mean<-apply(time_series,1,mean)
-#time_series_mean<-apply(time_series,1,mean)
-
-max_temp_df<-data.frame(time_series)
-colnames(max_temp_df)<-c("Date","max_air_temp")
-max_temp_df$month<-month(max_temp_df$Date)
-max_temp_df$week<-month(max_temp_df$Date)
-
-
-
-
-############## Average per day of the year #################
-rownames(max_temp_df)<-NULL
-
-max_temp_df$yday<-as.factor(yday(max_temp_df$Date))
-
-#time_series$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(max_temp_df,~yday,summarise,mean=mean(max_air_temp))
-time_series_quantile1<-ddply(max_temp_df,~yday, function (x) quantile(x$max_air_temp, c(.25,.5,.75)))
-time_series_average2<-cbind(time_series_average1,time_series_quantile1[,-1])
-time_series_average<-
- data.frame(time_series_average2[,1],time_series_average2[,c(2:5)])
-
-
-df2<-data.frame(time_series_average,rep("Model",times=length(time_series_average[,1])))
-colnames(df2)<-c("Day","Mean","f_quant","median","s_quant","source")
-
-average_data<-df2
-average_data$Day<-as.numeric(average_data$Day)
-
-yearly_average<-ggplot(average_data,aes(x=Day,y=Mean))
-
-yearly_average<-yearly_average+geom_line(size=2,colour="red")
-yearly_average <-yearly_average+ geom_ribbon(aes(ymin=f_quant, ymax=s_quant),fill="red",alpha=0.25)
-yearly_average<-yearly_average+ xlab("Day of the Year")+ ylab(
- expression("Maximum Air Temperature "*~degree*C))
-
-
-#yearly_average<-yearly_average+ scale_y_continuous(limit=c(0,0.05))
-
-
-month_2014<-month.abb[unique(month(as.Date(average_data$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,"n_seas",n_seas,"_multiple_delays.pdf", sep = "")
-file_name_pdf<-paste("../../Graphs/yearly_average_temperature.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_temperature.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()
-
-
-
-
-
-
-
-Env_laboratory<-read.csv(paste("../../Data_Base/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$Date)>=1999 & year(Env_laboratory$Date)<2000)
-
-#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$PostCode)[i]) )
-#}
-
-
-Env_laboratory_PHE<-Env_laboratory
-##Env_laboratory_PHE<-merge(Env_laboratory,Env_Campylobacter_data,by='PostCode',all=T) not sure why this is not working, so loop above
-
-######################## include daylength ##################
-
-Coord_laboratory<-read.csv(paste("../../Data_Base/Cases/Lab_PostCodes.csv",sep=""))
-
-lat_long_lab<-data.frame(names(Coord_laboratory),as.numeric(Coord_laboratory[1,]),as.numeric(Coord_laboratory[2,]))
-colnames(lat_long_lab)<-c("PostCode","lat","long")
-Env_laboratory_int2<-merge(Env_laboratory,lat_long_lab,by="PostCode")
-Env_Campylobacter_data_int2<-merge(Env_Campylobacter_data,lat_long_lab,by="PostCode")
-
-daylength<-function(lat,day_year)
-{
- #Latitude measure in degrees
- P <- asin(.39795*cos(.2163108 + 2*atan(.9671396*tan(.00860*(day_year-186)))))
- Denom<-cos(lat*pi/180)*cos(P)
- Numer<-sin(0.8333*pi/180) + sin(lat*pi/180)*sin(P)
- D<-24-(24/pi)*acos(Numer/Denom)
- return(D)
-}
-
-latitude<-Env_laboratory_int2$lat
-day_of_the_year<-yday(as.Date(Env_laboratory_int2$Date))
-
-daylength_int1<-mapply(daylength, latitude, day_of_the_year)
-daylength_df<-data.frame(latitude, day_of_the_year,as.Date(Env_laboratory_int2$Date),daylength_int1)
-colnames(daylength_df)<-c("lat","day_year","Date","daylength")
-
-
-daylength_df$month<-month(daylength_df$Date)
-daylength_df$week<-month(daylength_df$Date)
-
-
-
-
-############## Average per day of the year #################
-#rownames(daylength_df)<-NULL
-
-daylength_df$yday<-as.factor(yday(daylength_df$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$Cases4)
-time_series_average1<-ddply(daylength_df,~yday,summarise,mean=mean(daylength))
-time_series_quantile1<-ddply(daylength_df,~yday, function (x) quantile(x$mdaylength, 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)])
-
-
-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<-df2
-average_data$Day<-as.numeric(average_data$Day)
-
-yearly_average<-ggplot(average_data,aes(x=Day,y=Mean))
-
-yearly_average<-yearly_average+geom_line(size=2,colour="yellow")
-yearly_average <-yearly_average+ geom_ribbon(aes(ymin=f_quant, ymax=s_quant),fill="yellow",alpha=0.25)
-yearly_average<-yearly_average+ xlab("Day of the Year")+ ylab("Day Light (hours)")
-
-
-#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,"n_seas",n_seas,"_multiple_delays.pdf", sep = "")
-file_name_pdf<-paste("../../Graphs/lat_average_daylength.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/lat_average_daylength.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()
-
-
-
-
-