A package was recently released to generate plots in the style of xkcd using R. Being a big fan of the cartoon, I could not resist trying it out. So I set out to produce something like one of Hans Rosling’s bubble plots.

First I needed some data. Spoilt for choice. I scraped some population data broken down by country and retained only the country and population fields.

population.url = "http://en.wikipedia.org/wiki/List_of_countries_by_population"
download.file(population.url, "data/wiki-population.html")

library(XML)

population = readHTMLTable("data/wiki-population.html", which = 2, trim = TRUE)

After a bit of tidying up, this was ready to use.

> head(population)
         region population
1         China 1354040000
2         India 1210569573
3 United States  315901000
4     Indonesia  237641326
5        Brazil  193946886
6      Pakistan  183122000

Next I got my hands on some Gross Domestic Product (GDP) data from the World Bank. These data came as a spreadsheet which could be sucked into R with little effort.

library(xlsx)

GDP = read.xlsx("data/NY.GDP.MKTP.CD_Indicator_MetaData_en_EXCEL.xls", 1, stringsAsFactors = FALSE)

I simply retained the entries for 2011, which had few missing values.

Education spending data are also available from the World Bank. These data are a little more patchy, so I kept the most recent value for each country. This required a little fancy footwork.

XPD = read.xlsx("data/SE.XPD.TOTL.GD.ZS_Indicator_MetaData_en_EXCEL.xls", 1,
                stringsAsFactors = FALSE)

# Returns the last element in x which is not an NA
#
last.not.na After the requisite tidying, these two sets of data were also ready.
1 > head(GDP)
                                     region code          GDP
1                                Arab World  ARB 2.410300e+12
2                    Caribbean small states  CSS 6.178652e+10
3   East Asia & Pacific (all income levels)  EAS 1.880026e+13
4     East Asia & Pacific (developing only)  EAP 9.313033e+12
5                                 Euro area  EMU 1.307986e+13
6 Europe & Central Asia (all income levels)  ECS 2.215649e+13
> head(XPD)
                                     region education
1                                Arab World  4.337300
2                    Caribbean small states  6.354870
3   East Asia & Pacific (all income levels)  3.766995
4     East Asia & Pacific (developing only)  4.442010
5                                 Euro area  5.910550
6 Europe & Central Asia (all income levels)  5.478525

Finally I aggregated the three sets of data and removed any rows which were missing either GDP or education statistics.

data data #
data #
data Since there was a range of many orders of magnitude in both the population and GDP data, I took logarithms of these columns.
1 > data[,4]  data[,3]  head(data)
               region code population       GDP education
1         Afghanistan  AFG   7.406542 10.282776   1.72998
2             Albania  ALB   6.450553 10.112590   3.26756
3             Algeria  DZA   7.578639 11.275728   4.33730
6              Angola  AGO   7.314063 11.018416   3.47644
7 Antigua and Barbuda  ATG   4.935986  9.048565   2.53790
8           Argentina  ARG   7.603329 11.649378   5.78195

Then came the fun bit: putting the plot together. There is a great document “An introduction to the xkcd package” by Emilio Torres Manzanera which got me up to speed.

library(xkcd)
library(ggplot2)

xrange yrange p     geom_point(aes(education, GDP, size = population), alpha = 0.35, colour = I("red"), data = data) +
    scale_size_continuous(name = "log(population)", range = c(5, 20)) +
    geom_text(aes(education, GDP, label=code), size=5, family="xkcd", data = data) +
    xkcdaxis(xrange,yrange) +
    xlab("education (% of GDP)") + ylab("log(GDP) in $")
print(p)

And here is the result. Click on the image below to see it at higher resolution. Interesting that small countries like our neighbour, Lesotho, are spending a large fraction of their GDP on education. Also I must confess to having been previously completely unaware of the existence of Tuvalu (TUV), which is the fourth smallest country in the world (and the smallest country in my data).fff