climclass: Tools for climate classification

#' Tools for climate classification.
#'
#' Tools for climate classification. The Walter-Lieth climate chart is a slightly
#' modified version from the climtol package.
#'
#' @name climclass-package
#' @docType package
NA




library(stringr)
library(climatol)
#setwd("D:\\apps\\Shiny example")
#months = c("Jan","Feb","Mar","Apr","May","Jun", "Jul","Aug","Sep", "Oct", "Nov", "Dec")
# 
# filterLocality <- function (data, country=NA, locality=NA) {
#   #print(country)
#   if(!is.na(country)) data = data[data$CNTRY == country,] 
#   #print(head(data))
#   if(length(locality)>0)  {
#     if(!is.na(locality)) data = data[data$ADMIN4 == locality,] 
#   }
#   
#   data
# }
# 
# 
# climclass <- function(data,type=c("Koeppen")){
#   
# }
# 


#' Plots a climate chart
#' 
#' Plots Walter-Lieth climate chart. Uses a base function from climatol package and
#' adds some more details. Work in progress.
#' 
#' @param adata a data frame of daily weather data. First columns include locality data
#' @return nothing
#' @export
chartWL <- function(adata){

latd = unique(adata$LATD)
lond = unique(adata$LOND)
elev = unique(adata$ELEV)
locl = unique(adata$ADMIN4)
cntr = unique(adata$CNTRY)
locl = paste(locl,", ", cntr,sep="")

adata = adata[,c(6:ncol(adata))]
mnth = str_sub(adata$Date,6,7)
year = str_sub(adata$Date,1,4)
perd = range(as.integer(year))
perd = paste(perd[1],perd[2],sep="-")
adata = cbind(mnth, adata)
adata = adata[,-c(2)]
adata[,1] = as.character(adata[,1])

dsum = aggregate(.~mnth, data = adata,FUN = "mean")
if(nrow(dsum)==12){
  

wl = t(dsum[,2:ncol(dsum)])
wl = wl[c(4,3,2),]

dsum = aggregate(TMIN~mnth, data = adata,FUN = "min")
wl = rbind(wl, t(dsum[,2]))

slat = 1
if(!is.na(latd)) slat = sign(latd)

#diagwl(wl, est = locl, alt = elev, mlab="en", per = perd, shem = slat)
diagwl(wl, est = locl, alt = elev, mlab="en", per = perd)
if(!is.na(latd)){
  latlon = paste("LAT",latd, "/ LON", lond)
  mtext(latlon,3,-1, cex = 0.8) 
}
} else {
  subt = locl
  plot(x=1:12, y=rep(NA,12), ylim=c(1,12), main = "Not enough data for climate chart",
       sub = subt, xlab = "month", ylab="")
}
}


# data = read.csv('climate/climate_data.csv', stringsAsFactors = FALSE, sep="\t")
# data = filterLocality(data, locality = "Gisozi")

#chartWL(data)