R/transformJMPnat.R
In mverbyla/pathogenflows: Pathogen Flows for Sanitation Systems
Defines functions
t_JMPnat
Documented in
t_JMPnat
#' The t_JMP function
#'
#' This function transforms national or subnational sanitation data from the UNICEF/WHO Joint Monitoring Program and produces it in a format that can be used directly by the getLoadings function.
#' @param context Either "urban" or "rural" context
#' @keywords pathogens
#' @export
#' @examples
#' t_JMP("urban")
#'
#'
t_JMPnat<-function(context="national",myRegions="all"){
# Get file list
#setwd("data/processed")
#file_list <- list.files()
# Read all csv files in the folder and create a list of dataframes
#ldf <- lapply(file_list , read.csv)
# Combine each dataframe in the list into a single dataframe
#df.final <- do.call("rbind", ldf)
#setwd("..")
#setwd("..")
#write.csv(df.final,"data/jmpSanFac.csv")
sat<-read.csv("data/jmpSanFac.csv",header=T);head(sat)
sat$year<-as.numeric(paste("20",stringr::str_sub(sat$source,-2,-1),sep=""))
sat[sat$year>2020,"year"]<-as.numeric(paste("19",stringr::str_sub(sat[sat$year>2020,"source"],-2,-1),sep=""))
sat$source_ID<-stringr::str_sub(sat$source,-(stringr::str_length(sat$source)-stringr::str_locate(sat$source," - ")[,"end"]),-1)
colnames(sat)[colnames(sat)=="alpha.3"]<-"iso3"
harmon<-data.frame(classific_id=1:17,san=c("flushSewer","flushSeptic","flushPit","flushUnknown","flushOpen",
"pitSlab","pitSlab","pitSlab","pitNoSlab","hangingToilet","bucketLatrine",
"other","compostingToilet","pitSlab","openDefecation","other","other"))
sat<-merge(sat,harmon,by="classific_id");head(sat)
sat$uniqueID<-paste(sat$iso3,sat$source_ID,sep=".")
sums<-aggregate(percentage ~ iso3 + context + source_ID + uniqueID,data=sat,FUN=sum)
complete<-sums[which(sums$percentage==1),]
sat2<-sat[sat$uniqueID %in% unique(complete$uniqueID),]
st<-merge(aggregate(year ~ iso3, sat2, max), sat2);head(st) #this takes surveys from the most recent year
sat<-aggregate(percentage~iso3+country+context+year+source+san,data=st,FUN=sum);head(sat)
sums<-aggregate(percentage ~ iso3 + context + source,data=sat,FUN=sum)
complete<-sums[which(sums$percentage==1),]
suppressWarnings(if(myRegions=="all"){sat<-sat}else{sat<-sat[sat$iso3 %in% myRegions,]})
trt<-read.csv("data/jmpTreatment.csv",header=T)
suppressWarnings(if(myRegions=="all"){trt<-trt}else{trt<-trt[trt$ISO3 %in% myRegions,]})
pop<-read.csv("data/population.csv",header=T) #bring in the inputs CSV file
suppressWarnings(if(myRegions=="all"){pop<-pop}else{pop<-pop[pop$region %in% myRegions,]})
#assume<-data.frame(urban=c(0.01,0.2,0.3),rural=c(0.99,0.1,0.1))
#rownames(assume)<-c("coverBury","sewageTreated","fecalSludgeTreated")
if(context=="urban"){
pop$excreted<-pop$excreted_urban
pop$population<-pop$population*pop$fr_urban
pop<-pop[,-which(names(pop) %in% c("fr_urban","excreted_urban","excreted_rural"))]
ag<-aggregate(percentage~iso3+san+source+year,data=sat[sat$context=="Urban",],FUN=sum);head(ag)
tr<-trt[trt$Residence.Type=="urban",]
d<-aggregate(percentage~san+iso3,data=ag,FUN=mean)
sources<-unique(data.frame(ag$iso3,ag$source))
sources<-with(sources,sources[order(ag.iso3),])
w<-tidyr::spread(d,san,percentage)
}
if(context=="rural"){
pop$excreted<-pop$excreted_rural
pop$population<-pop$population*(1-pop$fr_urban)
pop<-pop[,-which(names(pop) %in% c("fr_urban","excreted_urban","excreted_rural"))]
ag<-aggregate(percentage~iso3+san+source+year,data=sat[sat$context=="Rural",],FUN=sum)
tr<-trt[trt$Residence.Type=="rural",]
d<-aggregate(percentage~san+iso3,data=ag,FUN=mean)
sources<-unique(data.frame(ag$iso3,ag$source))
sources<-with(sources,sources[order(ag.iso3),])
w<-tidyr::spread(d,san,percentage)
}
if(context=="national"){
pop$excreted<-pop$excreted_rural+pop$excreted_urban
pop<-pop[,-which(names(pop) %in% c("fr_urban","excreted_urban","excreted_rural"))]
ag<-aggregate(percentage~iso3+san+source+year,data=sat[sat$context=="National",],FUN=sum)
tr<-trt[trt$Residence.Type=="total",]
d<-aggregate(percentage~san+iso3,data=ag,FUN=mean)
sources<-unique(data.frame(ag$iso3,ag$source))
sources<-with(sources,sources[order(ag.iso3),])
w<-tidyr::spread(d,san,percentage)
}
names(tr)[1]<-"iso3"
x<-tidyr::spread(tr,Safely.managed.element,Coverage)
cb<-aggregate(`Disposed insitu`~iso3,data=x,FUN=sum)
sl<-aggregate(`Sewage treated`~iso3,data=x,FUN=sum)
et<-aggregate(`Faecal sludge treated`~iso3,data=x,FUN=sum)
cb[,-1]<-cb[,-1]/100
sl[,-1]<-sl[,-1]/100
et[,-1]<-et[,-1]/100
t<-merge(merge(cb,sl,by="iso3",all=T),et,by="iso3",all=T)
names(t)<-c("iso3","coverBury","sewageTreated","fecalSludgeTreated")
out<-merge(w,t,by="iso3",all=T)
#out[is.na(out)] <- 0
names(out)[1]<-"region"
if(is.null(out$flushSewer)){out$flushSewer<-NA}
if(is.null(out$flushSeptic)){out$flushSeptic<-NA}
if(is.null(out$flushPit)){out$flushPit<-NA}
if(is.null(out$flushOpen)){out$flushOpen<-NA}
if(is.null(out$flushUnknown)){out$flushUnknown<-NA}
if(is.null(out$pitSlab)){out$pitSlab<-NA}
if(is.null(out$pitNoSlab)){out$pitNoSlab<-NA}
if(is.null(out$bucketLatrine)){out$bucketLatrine<-NA}
if(is.null(out$hangingToilet)){out$hangingToilet<-NA}
if(is.null(out$openDefecation)){out$openDefecation<-NA}
if(is.null(out$containerBased)){out$containerBased<-NA}
if(is.null(out$compostingToilet)){out$compostingToilet<-NA}
if(is.null(out$compostingTwinSlab)){out$compostingTwinSlab<-NA}
if(is.null(out$compostingTwinNoSlab)){out$compostingTwinNoSlab<-NA}
if(is.null(out$other)){out$other<-NA}
out<-merge(pop,out,by="region",all=T)
out<-out[c("region","name","iso2","isonum","population","excreted","flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingTwinSlab","compostingTwinNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other","coverBury","sewageTreated","fecalSludgeTreated")]
out$isWatertight<-out$fecalSludgeTreated
out$hasLeach<-out$fecalSludgeTreated
out$onsiteDumpedLand<-0.1
out$emptyFrequency<-3
out$pitAdditive<-0
out$urine<-0
out$twinPits<-0
out<-out[,!(names(out) %in% c("population","excreted","compostingTwinSlab","compostingTwinNoSlab"))]
check1<-rowSums(out[,names(out) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)
out$warning<-""
out$warning[which(check1>1.1 | (check1<0.9 & check1!=0))]<-"Does not add up to 100%"
return(list(source=sources,output=out))
}
#my_inputU=t_JMPnat(context="urban",myRegions="all")
#write.csv(my_inputU$output,"inputURBAN.csv")
#write.csv(my_inputU$source,"inputURBANsources.csv")
#my_inputR=t_JMPnat(context="rural",myRegions="all")
#write.csv(my_inputR$output,"inputRURAL.csv")
#write.csv(my_inputR$source,"inputRURALsources.csv")
#my_inputN=t_JMPnat(context="national",myRegions="all")
#write.csv(my_inputN$output,"inputNATIONAL.csv")
#write.csv(my_inputN$source,"inputNATIONALsources.csv")
#df1<-read.csv("inputURBAN.csv")
#df2<-read.csv("inputRURAL.csv")
#df3<-read.csv("inputNATIONAL.csv")
#nrow(df3)
#df1$region
#df2$region
#df3<-df3[df3$region!="CHI",]
#df3$region
#missing<-vector()
#for(i in 1:nrow(df1)){
# missing[i]<-all(is.na(df1[i,names(df1) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]))
#}
#whichNA<-which(missing)
#noUrban<-which(rowSums(df1[,names(df1) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)==0);noUrban
#noRural<-which(rowSums(df2[,names(df2) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)==0);noRural
#noNational<-which(rowSums(df3[,names(df3) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)==0);noNational
#df1[noUrban,names(df1) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]<-df3[noUrban,names(df3) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]
#df2[noRural,names(df2) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]<-df3[noRural,names(df3) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]
#df1$notes<-""
#df1$notes[noUrban]<-"Urban sanitation technologies data was not available, so national data was used."
#df2$notes<-""
#df2$notes[noRural]<-"Rural sanitation technologies data was not available, so national data was used."
#
#noUrban<-is.na(df1[,names(df1) %in% c("coverBury","sewageTreated","fecalSludgeTreated")]);which(apply(noUrban, 1, all))
#noRural<-is.na(df2[,names(df2) %in% c("coverBury","sewageTreated","fecalSludgeTreated")]);which(apply(noRural, 1, all))
#noNational<-is.na(df3[,names(df3) %in% c("coverBury","sewageTreated","fecalSludgeTreated")]);which(apply(noNational, 1, all))
#
#write.csv(df1,"inputURBAN_withNational.csv")
#write.csv(df2,"inputRURAL_withNational.csv")
###
df<-read.csv("data/REGRESSIONtreatment.csv")
par(mfrow = c(2, 2))
plot(df$hdi,df$sewageTreated_urb)
plot(df$hdi,df$sewageTreated_rur)
plot(df$hdi,df$Fsmanaged_urb)
plot(df$hdi,df$Fsmanaged_rur)
df$lnsewageTreated_urb<-log(df$sewageTreated_urb+0.000000001)
df$lnsewageTreated_rur<-log(df$sewageTreated_rur+0.000000001)
df$lnFsmanaged_urb<-log(df$Fsmanaged_urb+0.000000001)
df$lnFsmanaged_rur<-log(df$Fsmanaged_rur+0.000000001)
plot(df$hdi,df$sewageTreated_urb)
plot(df$hdi,df$lnsewageTreated_urb)
plot(df$hdi,df$sewageTreated_rur)
plot(df$hdi,df$lnsewageTreated_rur)
plot(df$hdi,df$Fsmanaged_urb)
plot(df$hdi,df$lnFsmanaged_urb)
plot(df$hdi,df$Fsmanaged_rur)
plot(df$hdi,df$lnFsmanaged_rur)
####
fit1<-lm(lnsewageTreated_urb~hdi,data=df)
summary(fit1)
plot(fit1)
####
fit2<-lm(lnsewageTreated_rur~hdi,data=df)
summary(fit2)
plot(fit2)
####
fit3<-lm(Fsmanaged_urb~hdi,data=df)
summary(fit3)
plot(fit3)
mean(df$Fsmanaged_urb)
####
fit4<-lm(Fsmanaged_rur~hdi,data=df)
summary(fit4)
plot(fit4)
mean(df$Fsmanaged_rur)
mverbyla/pathogenflows documentation built on Sept. 19, 2022, 10:05 p.m.
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Defines functions t_JMPnat
Documented in t_JMPnat
#' The t_JMP function
#'
#' This function transforms national or subnational sanitation data from the UNICEF/WHO Joint Monitoring Program and produces it in a format that can be used directly by the getLoadings function.
#' @param context Either "urban" or "rural" context
#' @keywords pathogens
#' @export
#' @examples
#' t_JMP("urban")
#'
#'
t_JMPnat<-function(context="national",myRegions="all"){
# Get file list
#setwd("data/processed")
#file_list <- list.files()
# Read all csv files in the folder and create a list of dataframes
#ldf <- lapply(file_list , read.csv)
# Combine each dataframe in the list into a single dataframe
#df.final <- do.call("rbind", ldf)
#setwd("..")
#setwd("..")
#write.csv(df.final,"data/jmpSanFac.csv")
sat<-read.csv("data/jmpSanFac.csv",header=T);head(sat)
sat$year<-as.numeric(paste("20",stringr::str_sub(sat$source,-2,-1),sep=""))
sat[sat$year>2020,"year"]<-as.numeric(paste("19",stringr::str_sub(sat[sat$year>2020,"source"],-2,-1),sep=""))
sat$source_ID<-stringr::str_sub(sat$source,-(stringr::str_length(sat$source)-stringr::str_locate(sat$source," - ")[,"end"]),-1)
colnames(sat)[colnames(sat)=="alpha.3"]<-"iso3"
harmon<-data.frame(classific_id=1:17,san=c("flushSewer","flushSeptic","flushPit","flushUnknown","flushOpen",
"pitSlab","pitSlab","pitSlab","pitNoSlab","hangingToilet","bucketLatrine",
"other","compostingToilet","pitSlab","openDefecation","other","other"))
sat<-merge(sat,harmon,by="classific_id");head(sat)
sat$uniqueID<-paste(sat$iso3,sat$source_ID,sep=".")
sums<-aggregate(percentage ~ iso3 + context + source_ID + uniqueID,data=sat,FUN=sum)
complete<-sums[which(sums$percentage==1),]
sat2<-sat[sat$uniqueID %in% unique(complete$uniqueID),]
st<-merge(aggregate(year ~ iso3, sat2, max), sat2);head(st) #this takes surveys from the most recent year
sat<-aggregate(percentage~iso3+country+context+year+source+san,data=st,FUN=sum);head(sat)
sums<-aggregate(percentage ~ iso3 + context + source,data=sat,FUN=sum)
complete<-sums[which(sums$percentage==1),]
suppressWarnings(if(myRegions=="all"){sat<-sat}else{sat<-sat[sat$iso3 %in% myRegions,]})
trt<-read.csv("data/jmpTreatment.csv",header=T)
suppressWarnings(if(myRegions=="all"){trt<-trt}else{trt<-trt[trt$ISO3 %in% myRegions,]})
pop<-read.csv("data/population.csv",header=T) #bring in the inputs CSV file
suppressWarnings(if(myRegions=="all"){pop<-pop}else{pop<-pop[pop$region %in% myRegions,]})
#assume<-data.frame(urban=c(0.01,0.2,0.3),rural=c(0.99,0.1,0.1))
#rownames(assume)<-c("coverBury","sewageTreated","fecalSludgeTreated")
if(context=="urban"){
pop$excreted<-pop$excreted_urban
pop$population<-pop$population*pop$fr_urban
pop<-pop[,-which(names(pop) %in% c("fr_urban","excreted_urban","excreted_rural"))]
ag<-aggregate(percentage~iso3+san+source+year,data=sat[sat$context=="Urban",],FUN=sum);head(ag)
tr<-trt[trt$Residence.Type=="urban",]
d<-aggregate(percentage~san+iso3,data=ag,FUN=mean)
sources<-unique(data.frame(ag$iso3,ag$source))
sources<-with(sources,sources[order(ag.iso3),])
w<-tidyr::spread(d,san,percentage)
}
if(context=="rural"){
pop$excreted<-pop$excreted_rural
pop$population<-pop$population*(1-pop$fr_urban)
pop<-pop[,-which(names(pop) %in% c("fr_urban","excreted_urban","excreted_rural"))]
ag<-aggregate(percentage~iso3+san+source+year,data=sat[sat$context=="Rural",],FUN=sum)
tr<-trt[trt$Residence.Type=="rural",]
d<-aggregate(percentage~san+iso3,data=ag,FUN=mean)
sources<-unique(data.frame(ag$iso3,ag$source))
sources<-with(sources,sources[order(ag.iso3),])
w<-tidyr::spread(d,san,percentage)
}
if(context=="national"){
pop$excreted<-pop$excreted_rural+pop$excreted_urban
pop<-pop[,-which(names(pop) %in% c("fr_urban","excreted_urban","excreted_rural"))]
ag<-aggregate(percentage~iso3+san+source+year,data=sat[sat$context=="National",],FUN=sum)
tr<-trt[trt$Residence.Type=="total",]
d<-aggregate(percentage~san+iso3,data=ag,FUN=mean)
sources<-unique(data.frame(ag$iso3,ag$source))
sources<-with(sources,sources[order(ag.iso3),])
w<-tidyr::spread(d,san,percentage)
}
names(tr)[1]<-"iso3"
x<-tidyr::spread(tr,Safely.managed.element,Coverage)
cb<-aggregate(`Disposed insitu`~iso3,data=x,FUN=sum)
sl<-aggregate(`Sewage treated`~iso3,data=x,FUN=sum)
et<-aggregate(`Faecal sludge treated`~iso3,data=x,FUN=sum)
cb[,-1]<-cb[,-1]/100
sl[,-1]<-sl[,-1]/100
et[,-1]<-et[,-1]/100
t<-merge(merge(cb,sl,by="iso3",all=T),et,by="iso3",all=T)
names(t)<-c("iso3","coverBury","sewageTreated","fecalSludgeTreated")
out<-merge(w,t,by="iso3",all=T)
#out[is.na(out)] <- 0
names(out)[1]<-"region"
if(is.null(out$flushSewer)){out$flushSewer<-NA}
if(is.null(out$flushSeptic)){out$flushSeptic<-NA}
if(is.null(out$flushPit)){out$flushPit<-NA}
if(is.null(out$flushOpen)){out$flushOpen<-NA}
if(is.null(out$flushUnknown)){out$flushUnknown<-NA}
if(is.null(out$pitSlab)){out$pitSlab<-NA}
if(is.null(out$pitNoSlab)){out$pitNoSlab<-NA}
if(is.null(out$bucketLatrine)){out$bucketLatrine<-NA}
if(is.null(out$hangingToilet)){out$hangingToilet<-NA}
if(is.null(out$openDefecation)){out$openDefecation<-NA}
if(is.null(out$containerBased)){out$containerBased<-NA}
if(is.null(out$compostingToilet)){out$compostingToilet<-NA}
if(is.null(out$compostingTwinSlab)){out$compostingTwinSlab<-NA}
if(is.null(out$compostingTwinNoSlab)){out$compostingTwinNoSlab<-NA}
if(is.null(out$other)){out$other<-NA}
out<-merge(pop,out,by="region",all=T)
out<-out[c("region","name","iso2","isonum","population","excreted","flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingTwinSlab","compostingTwinNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other","coverBury","sewageTreated","fecalSludgeTreated")]
out$isWatertight<-out$fecalSludgeTreated
out$hasLeach<-out$fecalSludgeTreated
out$onsiteDumpedLand<-0.1
out$emptyFrequency<-3
out$pitAdditive<-0
out$urine<-0
out$twinPits<-0
out<-out[,!(names(out) %in% c("population","excreted","compostingTwinSlab","compostingTwinNoSlab"))]
check1<-rowSums(out[,names(out) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)
out$warning<-""
out$warning[which(check1>1.1 | (check1<0.9 & check1!=0))]<-"Does not add up to 100%"
return(list(source=sources,output=out))
}
#my_inputU=t_JMPnat(context="urban",myRegions="all")
#write.csv(my_inputU$output,"inputURBAN.csv")
#write.csv(my_inputU$source,"inputURBANsources.csv")
#my_inputR=t_JMPnat(context="rural",myRegions="all")
#write.csv(my_inputR$output,"inputRURAL.csv")
#write.csv(my_inputR$source,"inputRURALsources.csv")
#my_inputN=t_JMPnat(context="national",myRegions="all")
#write.csv(my_inputN$output,"inputNATIONAL.csv")
#write.csv(my_inputN$source,"inputNATIONALsources.csv")
#df1<-read.csv("inputURBAN.csv")
#df2<-read.csv("inputRURAL.csv")
#df3<-read.csv("inputNATIONAL.csv")
#nrow(df3)
#df1$region
#df2$region
#df3<-df3[df3$region!="CHI",]
#df3$region
#missing<-vector()
#for(i in 1:nrow(df1)){
# missing[i]<-all(is.na(df1[i,names(df1) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]))
#}
#whichNA<-which(missing)
#noUrban<-which(rowSums(df1[,names(df1) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)==0);noUrban
#noRural<-which(rowSums(df2[,names(df2) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)==0);noRural
#noNational<-which(rowSums(df3[,names(df3) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")],na.rm=T)==0);noNational
#df1[noUrban,names(df1) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]<-df3[noUrban,names(df3) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]
#df2[noRural,names(df2) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]<-df3[noRural,names(df3) %in% c("flushSewer","flushSeptic","flushPit","flushOpen","flushUnknown","pitSlab","pitNoSlab","compostingToilet","bucketLatrine","containerBased","hangingToilet","openDefecation","other")]
#df1$notes<-""
#df1$notes[noUrban]<-"Urban sanitation technologies data was not available, so national data was used."
#df2$notes<-""
#df2$notes[noRural]<-"Rural sanitation technologies data was not available, so national data was used."
#
#noUrban<-is.na(df1[,names(df1) %in% c("coverBury","sewageTreated","fecalSludgeTreated")]);which(apply(noUrban, 1, all))
#noRural<-is.na(df2[,names(df2) %in% c("coverBury","sewageTreated","fecalSludgeTreated")]);which(apply(noRural, 1, all))
#noNational<-is.na(df3[,names(df3) %in% c("coverBury","sewageTreated","fecalSludgeTreated")]);which(apply(noNational, 1, all))
#
#write.csv(df1,"inputURBAN_withNational.csv")
#write.csv(df2,"inputRURAL_withNational.csv")
###
df<-read.csv("data/REGRESSIONtreatment.csv")
par(mfrow = c(2, 2))
plot(df$hdi,df$sewageTreated_urb)
plot(df$hdi,df$sewageTreated_rur)
plot(df$hdi,df$Fsmanaged_urb)
plot(df$hdi,df$Fsmanaged_rur)
df$lnsewageTreated_urb<-log(df$sewageTreated_urb+0.000000001)
df$lnsewageTreated_rur<-log(df$sewageTreated_rur+0.000000001)
df$lnFsmanaged_urb<-log(df$Fsmanaged_urb+0.000000001)
df$lnFsmanaged_rur<-log(df$Fsmanaged_rur+0.000000001)
plot(df$hdi,df$sewageTreated_urb)
plot(df$hdi,df$lnsewageTreated_urb)
plot(df$hdi,df$sewageTreated_rur)
plot(df$hdi,df$lnsewageTreated_rur)
plot(df$hdi,df$Fsmanaged_urb)
plot(df$hdi,df$lnFsmanaged_urb)
plot(df$hdi,df$Fsmanaged_rur)
plot(df$hdi,df$lnFsmanaged_rur)
####
fit1<-lm(lnsewageTreated_urb~hdi,data=df)
summary(fit1)
plot(fit1)
####
fit2<-lm(lnsewageTreated_rur~hdi,data=df)
summary(fit2)
plot(fit2)
####
fit3<-lm(Fsmanaged_urb~hdi,data=df)
summary(fit3)
plot(fit3)
mean(df$Fsmanaged_urb)
####
fit4<-lm(Fsmanaged_rur~hdi,data=df)
summary(fit4)
plot(fit4)
mean(df$Fsmanaged_rur)
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