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Rainfall_Data_Analysis_-_Source_Code.R
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Rainfall_Data_Analysis_-_Source_Code.R
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#---------------------------------------------------------------------------------------------------------------------------------------------------------
# Reading in Data as data frame, converting into Time series.
#---------------------------------------------------------------------------------------------------------------------------------------------------------
# Loading library "readxl" for reading in .xlsx file via R.
# Loading library "openxlsx" for writing in .xlsx file via R.
library(readxl)
library(openxlsx)
# Data from file is stored in the data frame named as 'Intermediate'.
# "read_excel" function of "readxl" library is used.
Intermediate<-read_excel("Rainfall_Data_(111_years).xlsx")
# Removing NA values from 'Intermediate'.
Intermediate<-na.omit(Intermediate)
# Finding names of each district seperately using "unique" function.
Unique<-unique(Intermediate$DIST)
# "which" function is used to find the row number for which certain value is present in the data frame.
# This function can be used to create subset of data for each district.
# In order to view an example for showing the working of "which" function, remove hash from the following snippet.
# Intermediate[which(Intermediate$DIST==Unique[1])[1]:which(Intermediate$DIST==Unique[1])[length(which(Intermediate$DIST==Unique[1]))],]
# Creating a folder for storing all .xlsx files having data for each district seperately.
dir.create("Intermediate")
# Creating loop for writing an excel file with a particular district data.
for(i in 1:length(Unique))
{
write.xlsx(Intermediate[which(Intermediate$DIST==Unique[i])[1]:which(Intermediate$DIST==Unique[i])[length(which(Intermediate$DIST==Unique[i]))],-1:-3],
file=paste0("Intermediate/",Unique[i],".xlsx"),
sheetName = Unique[i], col.names = TRUE, row.names = TRUE, append = TRUE)
}
# Reading each file and changing its rownames with years.
# Removing all "WC" data.
# Storing each district's data in a separate data frame having the name of that particular district.
for(j in 1:length(Unique))
{
Data<-read_excel(paste0("Intermediate/",Unique[j],".xlsx"))
Data<-as.data.frame(Data)
row.names(Data)<-Intermediate$Year[1:length(row.names(Data))]
Data<-Data[,-1]
assign(Unique[j],Data)
}
# Deleting "Intermediate" directory.
unlink("Intermediate",recursive=T)
#---------------------------------------------------------------------------------------------------------------------------------------------------------
# Plotting Monthwise and Annual data for each district.
#---------------------------------------------------------------------------------------------------------------------------------------------------------
# Creating a folder for storing all analysis charts for each district seperately.
dir.create("Analysis")
for(k in 1:length(Unique))
{
# Creating a folder for each district.
dir.create(paste0("Analysis/",Unique[k]))
}
# Converting each data frame containing district rainfall data into time series and plotting its graph.
# Saving each plot in its respective folder.
for(l in 1:length(Unique))
{
# Creating dataframes each having a seperate district data with the same name as the respective district.
Data<-eval(parse(text=paste0("`",Unique[1],"`")))
# For plotting monthwise.
Month<-Data
Month<-(as.vector(t(as.matrix(Month[,-13:-18]))))
Month<-ts(Month,frequency=12,start=Intermediate$Year[1],
end=Intermediate$Year[length(Intermediate$Year)])
# Saving the plot on monthwise rainfall in working directory.
png(file=paste0("Analysis/",Unique[l],"/Monthwise.png"),width=800,height=600)
plot(Month,main=paste0("Monthwise Data Visualization - ",Unique[l]),
ylab="Rainfall in mm",xlab="Years")
dev.off(which=dev.cur())
}
for(m in 1:length(Unique))
{
# Creating dataframes each having a seperate district data with the same name as the respective district.
Data<-eval(parse(text=paste0("`",Unique[m],"`")))
# For plotting Annual Rainfall.
Annual<-Data
Annual<-(as.vector(t(as.matrix(Annual[,13]))))
Annual<-ts(Annual,frequency=1,start=Intermediate$Year[1],
end=Intermediate$Year[length(Intermediate$Year)])
# Saving the plot on annual rainfall in working directory.
png(file=paste0("Analysis/",Unique[m],"/Annual.png"),width=800,height=600)
plot(Annual,main=paste0("Annual Data Visualization - ",Unique[m]),
ylab="Rainfall in mm",xlab="Years")
dev.off(which=dev.cur())
}
#---------------------------------------------------------------------------------------------------------------------------------------------------------
# 30 year moving average estimation and deviation of actual rainfall from 30 year moving average for "Adilabad" district using Annual rainfall data.
#---------------------------------------------------------------------------------------------------------------------------------------------------------
library(pracma)
for(n in 1:length(Unique))
{
# Creating dataframes each having a seperate district data with the same name as the respective district.
Data<-eval(parse(text=paste0("`",Unique[n],"`")))
# Analysing Annual data.
Annual<-Data
Annual<-(as.vector(t(as.matrix(Annual[,13]))))
Annual<-ts(Annual,frequency=1,start=Intermediate$Year[1],
end=Intermediate$Year[length(Intermediate$Year)])
# Saving the plot on moving average for 30 years and deviation between original data and moving average.
png(file=paste0("Analysis/",Unique[n],"/Moving Average for 30 years.png"),width=800,height=600)
plot(movavg(Annual,n=30),type="l",ylab="Rainfall in mm",
main=paste0("Moving Average for 30 years - ",Unique[n]),xlab="Period (Number of years past 1901)")
dev.off(which=dev.cur())
#x-----------------------------------------------------------------------------------------------------------------------------------------------------x
png(file=paste0("Analysis/",Unique[n],"/Deviation of Actual Rainfall from 30 years Moving Average.png"),width=800,height=600)
plot(Annual-movavg(Annual,n=30),type="l",ylab="Deviation in mm",
main=paste0("Deviation of Actual Rainfall from 30 years Moving Average - ",Unique[n]),xlab="Years")
dev.off(which=dev.cur())
}
#---------------------------------------------------------------------------------------------------------------------------------------------------------
# Defining above and below Normal rainfall.
#---------------------------------------------------------------------------------------------------------------------------------------------------------
for(o in 1:length(Unique))
{
# Creating dataframes each having a seperate district data with the same name as the respective district.
Data<-eval(parse(text=paste0("`",Unique[o],"`")))[13]
# Finding normal rainfall value.
Normal<-sum(Data)/length(Data$WC13)
Data$Frequency<-c(0)
for(p in 1:length(rownames(Data)))
{
if(Data$WC13[p]>=(Normal*1.1))
{
Data$Frequency[p]=1
}
else if(Data$WC13[p]<=(Normal*0.9))
{
Data$Frequency[p]=-1
}
}
# Saving the plot on above & below normal rainfall and its frequency.
png(file=paste0("Analysis/",Unique[o],"/Above & Below Normal Rainfall and its Frequency.png"),width=800,height=600)
plot(Data$WC13,type="l",main=paste0("Above & Below Normal Rainfall and its Frequency - ",
Unique[o]),ylab="Rainfall in mm",xlab="Period (Number of years past 1901)")
points(Data$WC13,col=ifelse(Data$Frequency==1,"Red",ifelse(Data$Frequency==0,"Black","Blue")),pch=20)
abline(h=Normal*1.1,col="Red")
abline(h=Normal*0.9,col="Blue")
legend('bottomleft',horiz=TRUE,c(paste0("Above Normal Frequency - ",sum(Data$Frequency==1,na.rm=TRUE)," years"),
paste0("Below Normal Frequency - ",sum(Data$Frequency==-1,na.rm=TRUE)," years")),cex=.7,
fill=c("red","blue"),bty='n')
dev.off(which=dev.cur())
}
#---------------------------------------------------------------------------------------------------------------------------------------------------------
# Estimating percentage of districts having above and below normal rainfall for each year.
#---------------------------------------------------------------------------------------------------------------------------------------------------------
# Creating vector for storing all normal rainfall values for each district.
Normal_Rainfall<-vector(mode='numeric',length=length(Unique))
for(q in 1:length(Unique))
{
Temp<-eval(parse(text=paste0("`",Unique[q],"`")))[13]
Normal_Rainfall[q]=sum(Temp$WC13)/length(Temp$WC13)
}
# Converting into a data frame.
Normal_Rainfall<-as.data.frame(Normal_Rainfall)
rownames(Normal_Rainfall)<-Unique
# Adding frequency of districts having above and below normal rainfall for all years.
for(r in 1:length(rownames(Data)))
{
Frequency<-vector(mode='numeric',length=length(Unique))
for(s in 1:length(Unique))
{
if(((eval(parse(text=paste0("`",Unique[s],"`")))$WC13[r]))>=(Normal_Rainfall$Normal_Rainfall[s]*1.1))
{
Frequency[s]=1
}
else if(((eval(parse(text=paste0("`",Unique[s],"`")))$WC13[r]))<=(Normal_Rainfall$Normal_Rainfall[s]*0.9))
{
Frequency[s]=-1
}
}
Normal_Rainfall[paste0(rownames(Data)[r])]<-Frequency
}
Normal_Rainfall<-Normal_Rainfall[,-1]
Above_Normal<-vector(mode='numeric',length=length(rownames(Data)))
Below_Normal<-vector(mode='numeric',length=length(rownames(Data)))
Above_Normal_percent<-vector(mode='numeric',length=length(rownames(Data)))
Below_Normal_percent<-vector(mode='numeric',length=length(rownames(Data)))
for(t in 1:length(rownames(Data)))
{
Above_Normal[t]=(sum(Normal_Rainfall[,t]==1,na.rm=T))
Below_Normal[t]=(sum(Normal_Rainfall[,t]==-1,na.rm=T))
}
for(t in 1:length(rownames(Data)))
{
Above_Normal_percent[t]=(sum(Normal_Rainfall[,t]==1,na.rm=T)/length(Unique))*100
Below_Normal_percent[t]=(sum(Normal_Rainfall[,t]==-1,na.rm=T)/length(Unique))*100
}
Above_Normal<-as.data.frame(Above_Normal)
rownames(Above_Normal)<-rownames(Data)
Below_Normal<-as.data.frame(Below_Normal)
rownames(Below_Normal)<-rownames(Data)
Above_Normal_percent<-as.data.frame(Above_Normal_percent)
rownames(Above_Normal_percent)<-rownames(Data)
Below_Normal_percent<-as.data.frame(Below_Normal_percent)
rownames(Below_Normal_percent)<-rownames(Data)
# Saving plot on percentage of districts having above and below normal rainfall for each year.
png(file=paste0("Analysis/","Percentage of Districts having Above Normal rainfall.png"),width=800,height=600)
plot(ts(Above_Normal,start=as.numeric(rownames(Above_Normal)[1]),
end=as.numeric(rownames(Above_Normal)[length(rownames(Above_Normal))])),
main="Percentage of districts having above normal rainfall",ylab="Percentage of Districts",xlab="Years",col="Red")
dev.off(which=dev.cur())
png(file=paste0("Analysis/","Percentage of Districts having Below Normal rainfall.png"),width=800,height=600)
plot(ts(Below_Normal,start=as.numeric(rownames(Below_Normal)[1]),
end=as.numeric(rownames(Below_Normal)[length(rownames(Below_Normal))])),
main="Percentage of districts having below normal rainfall",ylab="Percentage of Districts",xlab="Years",col="Blue")
dev.off(which=dev.cur())