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")