timeComplementary.R

  ##################################################################
  ## Source code for the book: "Displaying time series, spatial and
  ## space-time data with R"
  
  ## Copyright (C) 2013-2012 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.
  ####################################################################

    ##################################################################
    ## Initial configuration
    ##################################################################
    ## Clone or download the repository and set the working directory
    ## with setwd to the folder where the repository is located.
  
    library(lattice)
    library(ggplot2)
    library(latticeExtra)
    library(zoo)
    
    myTheme <- custom.theme.2(pch=19, cex=0.7,
                              region=rev(brewer.pal(9, 'YlOrRd')),
                              symbol = brewer.pal(n=8, name = "Dark2"))
    myTheme$strip.background$col='transparent'
    myTheme$strip.shingle$col='transparent'
    myTheme$strip.border$col='transparent'
    
    xscale.components.custom <- function(...){
        ans <- xscale.components.default(...)
        ans$top=FALSE
        ans}
    yscale.components.custom <- function(...){
        ans <- yscale.components.default(...)
        ans$right=FALSE
        ans}
    myArgs <- list(as.table=TRUE,
                   between=list(x=0.5, y=0.2),
                   xscale.components = xscale.components.custom,
                   yscale.components = yscale.components.custom)
    defaultArgs <- lattice.options()$default.args
    
    lattice.options(default.theme = myTheme,
                    default.args = modifyList(defaultArgs, myArgs))
  ##################################################################

##################################################################
## Polylines
##################################################################

  load('data/CO2.RData')

  library(googleVis)
  pgvis <- gvisMotionChart(CO2data, idvar='Country.Name', timevar='Year')

print(pgvis, 'html', file='figs/googleVis.html')

pdf(file="figs/CO2_GNI.pdf")
  ## lattice version
  xyplot(GNI.capita  ~ CO2.capita, data=CO2data,
         xlab="Carbon dioxide emissions (metric tons per capita)",
         ylab="GNI per capita, PPP (current international $)",
         groups=Country.Name, type='b')
dev.off()

  ## ggplot2 version
  ggplot(data=CO2data, aes(x=CO2.capita, y=GNI.capita,
             color=Country.Name)) +
      xlab("Carbon dioxide emissions (metric tons per capita)") +
      ylab("GNI per capita, PPP (current international $)") +
      geom_point() + geom_path() + theme_bw()

  ##################################################################
  ## Choosing colors
  ##################################################################

  library(RColorBrewer)
  
  nCountries <- nlevels(CO2data$Country.Name)
  pal <- brewer.pal(n=5, 'Set1')
  pal <- rep(pal, length = nCountries)

  ## Rank of average values of CO2 per capita
  CO2mean <- aggregate(CO2.capita ~ Country.Name, data=CO2data, FUN=mean)
  palOrdered <- pal[rank(CO2mean$CO2.capita)]  

pdf(file="figs/hclust.pdf")
  CO2capita <- CO2data[, c('Country.Name', 'Year', 'CO2.capita')]
  CO2capita <- reshape(CO2capita, idvar='Country.Name', timevar='Year', direction='wide')
  hCO2 <- hclust(dist(CO2capita[, -1]))
  
  oldpar <- par(mar=c(0, 2, 0, 0) + .1)
  plot(hCO2, labels=CO2capita$Country.Name,
       xlab='', ylab='', sub='', main='')
  par(oldpar)
dev.off()

  idx <- match(levels(CO2data$Country.Name), 
               CO2capita$Country.Name[hCO2$order])
  palOrdered <- pal[idx]  

  ## simpleTheme encapsulates the palette in a new theme for xyplot
  myTheme <- simpleTheme(pch=19, cex=0.6, col=palOrdered)
  
  pCO2.capita <- xyplot(GNI.capita  ~ CO2.capita,
                        xlab="Carbon dioxide emissions (metric tons per capita)",
                        ylab="GNI per capita, PPP (current international $)",
                        groups=Country.Name, data=CO2data,
                        par.settings=myTheme,
                        type='b')

  gCO2.capita <- ggplot(data=CO2data, aes(x=CO2.capita, y=GNI.capita,
                        color=Country.Name)) +
      geom_point() + geom_path() +
      scale_color_manual(values=palOrdered, guide=FALSE) +
      xlab('CO2 emissions (metric tons per capita)') +
      ylab('GNI per capita, PPP (current international $)') +
      theme_bw()

##################################################################
## Labels to show time information
##################################################################

  xyplot(GNI.capita  ~ CO2.capita,
         xlab="Carbon dioxide emissions (metric tons per capita)",
         ylab="GNI per capita, PPP (current international $)",
         groups=Country.Name, data=CO2data,
         par.settings=myTheme,
         type='b', 
         panel=function(x, y, ..., subscripts, groups){
           panel.text(x, y, ...,
                      labels=CO2data$Year[subscripts],
                      pos=2, cex=0.5, col='gray')
           panel.superpose(x, y, subscripts, groups,...)
         }
         )

  pCO2.capita <- pCO2.capita +
      glayer_(panel.text(..., labels=CO2data$Year[subscripts],
                         pos=2, cex=0.5, col='gray'))

  gCO2.capita <- gCO2.capita + geom_text(aes(label=Year),
                                         colour='gray',
                                         size=2.5,
                                         hjust=0, vjust=0)
  

  ##################################################################
  ## Country names: positioning labels
  ##################################################################

pdf(file="figs/CO2_capita.pdf")
  library(maptools)  
  ## group.value provides the country name; group.number is the
  ## index of each country to choose the color from the palette.
  pCO2.capita +
      glayer(panel.pointLabel(mean(x), mean(y),
                              labels= group.value,
                              col=palOrdered[group.number],
                              cex=.8,
                              fontface=2, fontfamily='Palatino'))
dev.off()

pdf(file="figs/CO2_capitaDL.pdf")
  library(directlabels)
  direct.label(pCO2.capita, method='extreme.grid')
dev.off()

  direct.label(gCO2.capita, method='extreme.grid')

  ##################################################################
  ## A panel for each year
  ##################################################################

pdf(file="figs/CO2_capita_panel.pdf")
  xyplot(GNI.capita  ~ CO2.capita | factor(Year), data=CO2data,
         xlab="Carbon dioxide emissions (metric tons per capita)",
         ylab="GNI per capita, PPP (current international $)",
         groups=Country.Name, type='b',
         auto.key=list(space='right'))
dev.off()

  ggplot(data=CO2data, aes(x=CO2.capita, y=GNI.capita, colour=Country.Name)) +
      facet_wrap(~ Year) + geom_point(pch=19) + 
      xlab('CO2 emissions (metric tons per capita)') +
      ylab('GNI per capita, PPP (current international $)') +
      theme_bw()

pdf(file="figs/CO2_capita_panel_labels.pdf")
  xyplot(GNI.capita  ~ CO2.capita | factor(Year), data=CO2data,
         xlab="Carbon dioxide emissions (metric tons per capita)",
         ylab="GNI per capita, PPP (current international $)",
         groups=Country.Name, type='b',
         par.settings=myTheme) + 
      glayer(panel.pointLabel(x, y, labels=group.value,
                              col=palOrdered[group.number], cex=0.7))
dev.off()

##################################################################
## Using variable size to encode an additional variable
##################################################################

  library(classInt)
  z <- CO2data$CO2.PPP
  intervals <- classIntervals(z, n=4, style='fisher')

  nInt <- length(intervals$brks) - 1
  cex.key <- seq(0.5, 1.8, length=nInt)
  
  idx <- findCols(intervals)
  CO2data$cexPoints <- cex.key[idx]

pdf(file="figs/CO2pointsGG.pdf")
  ggplot(data=CO2data, aes(x=CO2.capita, y=GNI.capita, colour=Country.Name)) +
      facet_wrap(~ Year) + geom_point(aes(size=cexPoints), pch=19) +
      xlab('Carbon dioxide emissions (metric tons per capita)') +
      ylab('GNI per capita, PPP (current international $)') +
      theme_bw()
dev.off()

pdf(file="figs/CO2points.pdf")
op <- options(digits=2)
tab <- print(intervals)
options(op)
  
key <- list(space='right',
            title=expression(CO[2]/GNI.PPP),
            cex.title=1,
            ## Labels of the key are the intervals strings
            text=list(labels=names(tab), cex=0.85),
            ## Points sizes are defined with cex.key
            points=list(col='black', pch=19,
                cex=cex.key, alpha=0.7))

  
xyplot(GNI.capita ~ CO2.capita|factor(Year), data=CO2data,
       xlab="Carbon dioxide emissions (metric tons per capita)",
       ylab="GNI per capita, PPP (current international $)",
       groups=Country.Name, key=key, alpha=0.7,
       panel = panel.superpose,
       panel.groups = function(x, y,
           subscripts, group.number, group.value, ...){
           panel.xyplot(x, y,
                        col = palOrdered[group.number],
                        cex = CO2data$cexPoints[subscripts])
           panel.pointLabel(x, y, labels=group.value,
                            col=palOrdered[group.number],
                            cex=0.7)
       }
       ) 
dev.off()

  ##################################################################
  ## Travelling bubbles
  ##################################################################

  library(gridSVG)
  
  xyplot(GNI.capita ~ CO2.capita, data=CO2data,
         xlab="Carbon dioxide emissions (metric tons per capita)",
         ylab="GNI per capita, PPP (current international $)",
         subset=Year==2000, groups=Country.Name,
         ## The limits of the graphic are defined
         ## with the entire dataset
         xlim=extendrange(CO2data$CO2.capita),
         ylim=extendrange(CO2data$GNI.capita),
         panel=function(x, y, ..., subscripts, groups) {
           color <- palOrdered[groups[subscripts]]
           radius <- CO2data$CO2.PPP[subscripts]
           ## Size of labels
           cex <- 1.1*sqrt(radius)
           ## Bubbles
           grid.circle(x, y, default.units="native",
                       r=radius*unit(.25, "inch"),
                       name=trellis.grobname("points", type="panel"),
                       gp=gpar(col=color,
                         ## Fill color ligther than border
                         fill=adjustcolor(color, alpha=.5),
                         lwd=2))
           ## Country labels
           grid.text(label=groups[subscripts],
                     x=unit(x, 'native'),
                     ## Labels above each bubble
                     y=unit(y, 'native') + 1.5 * radius *unit(.25, 'inch'),
                     name=trellis.grobname('labels', type='panel'),
                     gp=gpar(col=color, cex=cex))
         })

  ## Duration in seconds of the animation
  duration <- 20
  
  nCountries <- nlevels(CO2data$Country.Name)
  years <- unique(CO2data$Year)
  nYears <- length(years)
  
  ## Intermediate positions of the bubbles
  x_points <- animUnit(unit(CO2data$CO2.capita, 'native'),
                       id=rep(seq_len(nCountries), each=nYears))
  y_points <- animUnit(unit(CO2data$GNI.capita, 'native'),
                       id=rep(seq_len(nCountries), each=nYears))
  ## Intermediate positions of the labels
  y_labels <- animUnit(unit(CO2data$GNI.capita, 'native') +
                       1.5 * CO2data$CO2.PPP * unit(.25, 'inch'),
                       id=rep(seq_len(nCountries), each=nYears))
  ## Intermediate sizes of the bubbles
  size <- animUnit(CO2data$CO2.PPP * unit(.25, 'inch'),
                       id=rep(seq_len(nCountries), each=nYears))
  
  grid.animate(trellis.grobname("points", type="panel", row=1, col=1),
               duration=duration,
               x=x_points,
               y=y_points,
               r=size,
               rep=TRUE)
  
  grid.animate(trellis.grobname("labels", type="panel", row=1, col=1),
               duration=duration,
               x=x_points,
               y=y_labels,
               rep=TRUE)
  

  countries <- unique(CO2data$Country.Name)
  URL <- paste('http://en.wikipedia.org/wiki/', countries, sep='')
  grid.hyperlink(trellis.grobname('points', type='panel', row=1, col=1),
                 URL, group=FALSE)
  

  visibility <- matrix("hidden", nrow=nYears, ncol=nYears)
  diag(visibility) <- "visible"
  yearText <- animateGrob(garnishGrob(textGrob(years, .9, .15,
                                               name="year",
                                               gp=gpar(cex=2, col="grey")),
                                      visibility="hidden"),
                          duration=20,
                          visibility=visibility,
                          rep=TRUE)
  grid.draw(yearText)

  grid.export("figs/bubbles.svg")