Working with Climate Data

Climate data

## FAO
## http://geonetwork3.fao.org/climpag/agroclimdb_en.php

temp <- read.csv("./data/fao_sl_temp.csv", header = TRUE)
temp$DATE <- as.Date(temp$DATE)
#str(temp)
#head(temp)
temp <- temp[complete.cases(temp)==TRUE,]
#str(temp)

library(chron)
temp$YEAR <- years(as.chron(temp$DATE))
temp$MONTH <- months(as.chron(temp$DATE))
temp.y <- split(temp, temp$YEAR)
temp.m <- split(temp, temp$MONTH)
#str(temp.y)
#str(temp.m)

library(plyr)
temp.mean <- lapply(temp.y, FUN = function(x){ddply(x, c("NAME", "LATITUDE", "LONGITUDE"),
                                              summarise,
                                              N = length(VALUE),
                                              MEAN = mean(VALUE))}
                 )

#str(temp.mean)
#head(temp.mean)

temp.mean <- lapply(temp.mean, FUN = function(x){x[x$N==12,]})
#temp.mean

temp.mean.m <- lapply(temp.m, FUN = function(x){ddply(x, c("NAME", "LATITUDE", "LONGITUDE","YEAR"),
                                                summarise,
                                                N = length(VALUE),
                                                MEAN = mean(VALUE))}
                 )

#str(temp.mean.m)
#head(temp.mean.m)

temp.mean.m <- lapply(temp.mean.m, FUN = function(x) {ddply(x, c("NAME", "LATITUDE", "LONGITUDE"),
                                                      summarise, mean_temp = mean(MEAN))})

#str(temp.mean.m)
#head(temp.mean.m)

sl.mean.temp <- ldply(temp.mean.m, data.frame)
#str(sl.mean.temp)
#head(sl.mean.temp)
sl.mean.temp$.id <- as.factor(sl.mean.temp$.id)

ggplot(sl.mean.temp, aes(x=.id, y=mean_temp, group=NAME, col = NAME)) +
        geom_line() +
            geom_point()

prc <- read.csv("./data/fao_sl_prc.csv", header = TRUE)
prc$DATE <- as.Date(prc$DATE)
#head(prc)
prc <- prc[complete.cases(prc)==TRUE,]
#str(prc)

library(chron)
prc$YEAR <- years(as.chron(prc$DATE))
prc$MONTH <- months(as.chron(prc$DATE))
prc.y <- split(prc, prc$YEAR)
prc.m <- split(prc, prc$MONTH)

#str(prc.y)
#str(prc.m)

library(plyr)
prc.sum <- lapply(prc.y, FUN = function(x){ddply(x, c("NAME", "LATITUDE", "LONGITUDE"), summarise,
                 N = length(VALUE),
                 sum = sum(VALUE))}
                 )

#str(prc.sum)
#head(prc.sum)

prc.sum <- lapply(prc.sum, FUN = function(x){x[x$N==12,]})
#prc.sum

prc.sum.m <- lapply(prc.m, FUN = function(x){ddply(x, c("NAME", "LATITUDE", "LONGITUDE","YEAR"),
                                             summarise,
                                             N = length(VALUE),
                                             sum = sum(VALUE))}
                                             )

#str(prc.sum.m)
#head(prc.sum.m)

prc.sum.m <- lapply(prc.sum.m, FUN = function(x) {ddply(x, c("NAME", "LATITUDE", "LONGITUDE"),
                                                  summarise, mean_prc = mean(sum))})

#str(prc.sum.m)
#head(prc.sum.m)

sl.mean.prc <- ldply(prc.sum.m, data.frame)
#str(sl.mean.prc)
#head(sl.mean.prc)
sl.mean.prc$.id <- as.factor(sl.mean.prc$.id)

ggplot(sl.mean.prc, aes(x=.id, y=mean_prc, group=NAME, col = NAME)) +
    geom_line() +
    geom_point()

.

## function (..., .env = parent.frame()) 
## {
##     structure(as.list(match.call()[-1]), env = .env, class = "quoted")
## }
## <environment: namespace:plyr>

## test wether the places from the two data sets are the same
sl.mean.temp$NAME %in% sl.mean.prc$NAME

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

cl.stat <- SpatialPointsDataFrame(coords = cbind(sl.mean.temp$LONGITUDE, sl.mean.temp$LATITUDE),
                                  data = data.frame(NAME = sl.mean.temp$NAME),
                                  proj4string = CRS("+init=epsg:4326"))
cl.stat <- remove.duplicates(cl.stat)

library(mapdata)

map("worldHires", regions = "Sri Lanka")
points(cl.stat, pch = 19)
map.axes()
title("Cities with climate data available at FAO ")