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na_kalman_cli.R
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na_kalman_cli.R
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# 21.3.17 IVA
# http://stackoverflow.com/questions/18695335/replacing-all-nas-with-smoothing-spline
# http://stackoverflow.com/questions/18695335/replacing-all-nas-with-smoothing-spline
require(zoo)
setZeroNegativeNumber <- function(n){
if(n < 0) return(0)
else return(n)
}
createDF.script <- function(fileName){
cli_dataset <-read.table(fileName, header=FALSE, sep="")
names(cli_dataset) <- c("day", "month", "year", "prec", "temp");
cli_dataset[cli_dataset$prec == -9999, 4] <- NA ; prec_vector <- cli_dataset[, 4]
cli_dataset[cli_dataset$temp == -9999, 5] <- NA ; temp_vector <- cli_dataset[, 5]
days <- c(1:dim(cli_dataset)[1])
return(data.frame(days=days, prec=prec_vector, temp=temp_vector))
}
# Replacement numbers-9999 to missing value NA
replacementNumbersMinus9999.NA <- function(cli_dataset){
for(i in 1:nrow(cli_dataset)){
if(!is.na(cli_dataset[i,2])){
if(cli_dataset[i,2]==-9999) cli_dataset[i,2]<-NA
}
if(!is.na(cli_dataset[i,3])) {
if(cli_dataset[i,3]==-9999) cli_dataset[i,3]<-NA
}
}
return(cli_dataset)
}
na.spline.cli <- function(cli_dataset){
#cli_dataset<-setNameCli(cli_dataset)
cli_dataset<-replacementNumbersMinus9999.NA(cli_dataset)
prec_vector <- cli_dataset[, 2]
temp_vector <- cli_dataset[, 3]
#days <- length(cli_dataset[,1])
days <- c(1:dim(cli_dataset)[1])
prec_zoo <- zoo(prec_vector, days)
temp_zoo <- zoo(temp_vector, days)
prec_filled <- sapply(as.integer(na.spline(prec_zoo)), setZeroNegativeNumber)
temp_filled <- as.integer(na.spline(temp_zoo))
new_cli <- data.frame(day=cli_dataset[,1], prec=prec_filled, temp=temp_filled)
return(new_cli)
}
plotTempFilled <- function(naDF, filledDF, fileName){
plot(filledDF[,3], main=fileName, ylab="Temp(c)*10", xlab="Days", col="red", pch=19);
points(naDF[,3], col="blue", pch=19)
grid (10,10, lty = 6, col = "cornsilk2")
points(filledDF[,3], col="red", pch=19);
points(naDF[,3], col="blue", pch=19)
# Add a legend
legend("topleft", legend=c("Real CLI", "Guess CLI"), col=c("red", "blue"), pch=c(19,19), cex=0.8)
}
plotPrecFilled <- function(naDF, filledDF, fileName){
plot(filledDF[,2], main=fileName, ylab="Prec(mm)*10", xlab="Days", col="red", pch=19);
points(naDF[,2], col="blue", pch=19)
grid (10,10, lty = 6, col = "cornsilk2")
points(filledDF[,2], col="red", pch=19);
points(naDF[,2], col="blue", pch=19)
# Add a legend
legend("topleft", legend=c("Real CLI", "Guess CLI"), col=c("red", "blue"), pch=c(19,19), cex=0.8)
}
if(FALSE){
filled_path <- "C:/Users/IVA/Dropbox/Apps/na_grnn"
#filled_path <- "/home/larisa/Dropbox/Apps/na_grnn"
setwd(filled_path); getwd(); file_name <- "1969.cli"
cli_dataset <- createDF.script(file_name)
summary(cli_dataset)
cli_dataset<-replacementNumbersMinus9999.NA(cli_dataset)
summary(cli_dataset)
cli_dataset_filled <- na.spline.cli(cli_dataset)
cli_dataset<-replacementNumbersMinus9999.NA(cli_dataset)
summary(cli_dataset_filled)
plotTempFilled(cli_dataset, cli_dataset_filled, file_name)
plotPrecFilled(cli_dataset, cli_dataset_filled, file_name)
}
# https://www.analyticsvidhya.com/blog/2016/12/practical-guide-to-implement-machine-learning-with-caret-package-in-r-with-practice-problem/
# https://gist.github.com/anonymous/6c738ab40823ce8466a942897850d262
# install.packages('shiny', dependencies=TRUE, repos='http://cran.rstudio.com/')
# install.packages('imputeTS', dependencies=TRUE, repos='http://cran.rstudio.com/')