Gapminder has posted an interesting chart using world development indicators from the World Bank. I thought it would be a good exercise to recreate this chart using R and ggplot2. While playing with the data, not log transforming GDP provides some interesting, and perhaps different, interpretation. The R script and graphics are below.

library(ggplot2)
library(reshape)

countries = read.csv('Data/WDI_GDF_Country.csv', strip.white=TRUE)
worldData = read.csv('Data/WDI_GDF_Data.csv', strip.white=TRUE)

worldData2 = worldData[which(worldData$Series.Code %in% c('NY.GDP.MKTP.KD', 'SE.XPD.TOTL.GD.ZS', 'SP.DYN.LE00.IN', 'SP.POP.TOTL')), c('Series.Code', 'Series.Name', 'Country.Name', 'Country.Code', 'X2008')]
worldData2 = merge(worldData2, countries[,c('Country.Code', 'Region')], by='Country.Code')
worldData2 = worldData2[which(worldData2$Region != 'Aggregates'),]
worldData2$Series.Name = as.factor(as.character(worldData2$Series.Name))
worldData2$Region = as.factor(as.character(worldData2$Region))
worldData3 = cast(worldData2, Country.Name + Region ~ Series.Name, mean, value='X2008')
names(worldData3) = c('Country', 'Region', 'GDP', 'Life.Expectancy', 'Population', 'Education')

worldData3$GDP.log = log(worldData3$GDP)
worldData3$GDP = worldData3$GDP / 1000000000 #Billions
worldData3$Population = worldData3$Population / 1000000 #Millions

p = ggplot(worldData3, aes(x=GDP, y=Life.Expectancy, label=Country))
p + geom_point(aes(size=Population, colour=Region), stat='identity', alpha=.6) + geom_text(hjust=-.2, vjust=.5, size=2) + scale_size_continuous('Population (Millions)', to=c(1,20)) + xlab('Gross Domestic Product (billions)') + ylab('Life Expectancy at birth (years)')

p = ggplot(worldData3, aes(x=GDP.log, y=Life.Expectancy, label=Country))
p + geom_point(aes(size=Population, colour=Region), stat='identity', alpha=.6) + geom_text(hjust=-.2, vjust=.5, size=2) + scale_size_continuous('Population (Millions)', to=c(1,20)) + xlab('Gross Domestic Product (log scale)') + ylab('Life Expectancy at birth (years)')

worldData4 = worldData3[which(worldData3$GDP < 2000),]
p = ggplot(worldData4, aes(x=GDP, y=Life.Expectancy, label=Country))
p + geom_point(aes(size=Population, colour=Region), stat='identity', alpha=.6) + geom_text(hjust=-.2, vjust=.5, size=2) + scale_size_continuous('Population (Millions)', to=c(1,20)) + xlab('Gross Domestic Product (billions)') + ylab('Life Expectancy at birth (years)')

gworldData = worldData[which(worldData$Series.Code %in% c('NY.GDP.MKTP.KD', 'SP.DYN.LE00.IN', 'SP.POP.TOTL')), c('Series.Code', 'Series.Name', 'Country.Name', 'Country.Code', 'X2000', 'X2001', 'X2002', 'X2003', 'X2004', 'X2005', 'X2006', 'X2007', 'X2008')]
gworldData = merge(gworldData, countries[,c('Country.Code', 'Region')], by='Country.Code')
gworldData = gworldData[which(gworldData$Region != 'Aggregates'),]
gworldData$Series.Name = as.factor(as.character(gworldData$Series.Name))
gworldData$Region = as.factor(as.character(gworldData$Region))
gworldData = melt(gworldData, id=c('Country.Code', 'Series.Code', 'Series.Name', 'Country.Name', 'Region'))
gworldData = cast(gworldData, Country.Name + Region + variable ~ Series.Name, mean, value='value')
names(gworldData) = c('Country', 'Region', 'Year', 'GDP', 'Life.Expectancy', 'Population')
gworldData$GDP = gworldData$GDP / 1000000000 #Billions
gworldData$Population = gworldData$Population / 1000000 #Millions
gworldData$Year = as.integer(substr(gworldData$Year, 2,5))
head(gworldData)
m = gvisMotionChart(gworldData, idvar='Country', timevar='Year')
cat(m$html$chart)
plot(m)