There are rare cases when a graphic is *not* better than a figure to help us understand our quantitative results. A simple yet common table we’re staring at ever so often are tables of correlation coefficients: how strongly do different variables correlate with one another. We’re scanning the tables for numbers close to +1 and close to -1, but there’s a better way: *visualize!*

The R package corrplot offers a ready-made solution:

library(corrplot)
dat=matrix(c(0.11128257, -0.38968561, 0.11765272, -0.07089879, -0.19715366, -0.48083950, 0.54760745, -0.49410370, -0.42443391), nrow=3)
corrplot(dat)

Here we call the corrplot package, create some data so that we can plot something, normally this would be a selection of variables. Then we simply call corrplot() and we’re done.

There are many ways to tweak the plots, but in all versions we get a quicker and better overview of the variables that correlate than staring at a large table.

Here are some variants of the above:

par(mfrow=c(2,2))
corrplot(dat, method = "shade")
corrplot(dat, diag=FALSE)
corrplot(dat, method = "square")
corrplot(dat, method = "number")

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