Infovis vs. statgraphics: A clear example of their different goals

(by Andrew Gelman)

I recently came across a data visualization that perfectly demonstrates the difference between the “infovis” and “statgraphics” perspectives.

Here’s the image (link from Tyler Cowen):

That’s the infovis. The statgraphic version would simply be a dotplot, something like this:

(I purposely used the default settings in R with only minor modifications here to demonstrate what happens if you just want to plot the data with minimal effort.)

Let’s compare the two graphs:

From a statistical graphics perspective, the second graph dominates. The countries are directly comparable and the numbers are indicated by positions rather than area. The first graph is full of distracting color and gives the misleading visual impression that the total GDP of countries 5-10 is about equal to that of countries 1-4.

If the goal is to get attention, though, it’s another story. There’s nothing special about the top graph above except how it looks. It represents neither a data-gathering effort, nor a statistical analysis, nor even a clever juxtaposition (as in the famous graph of health costs and life expectancies). If someone had posted the second graph above (the lineplot), I doubt it would’ve been sent around the web, and I doubt that Cowen would’ve noticed it in the first place.

Thus, in this modern world of multichannel communications, chartjunk does have a purpose: it gets you noticed.

P.S. Here’s my R code:

png ("africagdp.png", height=350, width=400)
countries <- c ("South Africa", "Egypt", "Nigeria", "Algeria",
"Morocco", "Angola", "Libya", "Tunisia", "Kenya", "Ethiopia",
"Ghana", "Cameroon")
gdp <- c (285.4, 188.4, 173, 140.6, 91.4, 75.5, 62.3,
39.6, 29.4, 28.5, 26.2, 22.2)
dotchart (rev(gdp), rev(countries),
xlab="GDP in billions of US dollars",
main="African Countries by GDP",
xlim=max(gdp)*c(.038,1.02), pch=20) ()

1 Response to “Infovis vs. statgraphics: A clear example of their different goals”

  1. 1 Michael J. McFadden July 30, 2011 at 7:05 am

    It’s also interesting how the statgraphic analysis shows the clumping: the poorest five, then two groups of three, then the unitary S. Africa. Might be interesting to see how the figures look superimposed in different ways on a map of Africa itself.

    Of course more relevant to the people who actually LIVE there would probably be graphs representing something like GDP/person.

    – MJM

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