dataTime.R

##################################################################
## Source code for the book: "Displaying time series, spatial and
## space-time data with R"

## Copyright (C) 2012 Oscar Perpiñán Lamigueiro

## This program is free software you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published
## by the Free Software Foundation; either version 2 of the License,
## or (at your option) any later version.
 
## This program is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
## General Public License for more details.
 
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
## 02111-1307, USA.
####################################################################

##################################################################
## SIAR
##################################################################

##################################################################
## Daily data of different meteorological variables 
##################################################################

library(zoo)

aranjuez <- read.zoo("data/aranjuez.gz",
                     index.column = 3, format = "%d/%m/%Y",
                     fileEncoding = 'UTF-16LE',
                     header = TRUE, fill = TRUE,
                     sep = ';', dec = ",", as.is = TRUE)
aranjuez <- aranjuez[, -c(1:4)]

names(aranjuez) <- c('TempAvg', 'TempMax', 'TempMin',
                     'HumidAvg', 'HumidMax',
                     'WindAvg', 'WindMax',
                     'Radiation', 'Rain', 'ET')


summary(aranjuez)

aranjuezClean <- within(as.data.frame(aranjuez),{
  TempMin[TempMin>40] <- NA
  HumidMax[HumidMax>100] <- NA
  WindAvg[WindAvg>10] <- NA
  WindMax[WindMax>10] <- NA
})

aranjuez <- zoo(aranjuezClean, index(aranjuez))

save(aranjuez, file='data/aranjuez.RData')

##################################################################
## Solar radiation measurements from different locations
##################################################################

##################################################################
## Unemployment in the United States
##################################################################

unemployUSA <- read.csv('data/unemployUSA.csv')
nms <- unemployUSA$Series.ID
##columns of annual summaries
annualCols <- 14 + 13*(0:12)
## Transpose. Remove annual summaries
unemployUSA <- as.data.frame(t(unemployUSA[,-c(1, annualCols)]))
## First 7 characters can be suppressed
names(unemployUSA) <- substring(nms, 7)
head(unemployUSA)

library(zoo)

Sys.setlocale("LC_TIME", 'C')
idx <- as.yearmon(row.names(unemployUSA), format='%b.%Y')
unemployUSA <- zoo(unemployUSA, idx)

isNA <- apply(is.na(unemployUSA), 1, any)
unemployUSA <- unemployUSA[!isNA,]

save(unemployUSA, file='data/unemployUSA.RData')

##################################################################
## Gross National Income and $CO_2$ emissions
##################################################################

library(WDI)
  
CO2data <- WDI(indicator=c('EN.ATM.CO2E.PC', 'EN.ATM.CO2E.PP.GD',
              'NY.GNP.MKTP.PP.CD', 'NY.GNP.PCAP.PP.CD'),
          start=2000, end=2011,
          country=c('BR', 'CN', 'DE', 'ES',
              'FI', 'FR', 'GR', 'IN', 'NO', 'US'))

names(CO2data) <- c('iso2c', 'Country.Name', 'Year',
                    'CO2.capita', 'CO2.PPP',
                    'GNI.PPP', 'GNI.capita')

isNA <- apply(is.na(CO2data), 1, any)
CO2data <- CO2data[!isNA, ]

CO2data$Country.Name <- factor(CO2data$Country.Name)

save(CO2data, file='data/CO2.RData')