More on Exploring Correlations in R

About a year ago I wrote a post about producing scatterplot matrices in R. These are handy for quickly getting a sense of the correlations that exist in your data. Recently someone asked me to pull out some relevant statistics (correlation coefficient and p-value) into tabular format to publish beside a scatterplot matrix. The built-in cor() function will produce a correlation matrix, but what if you want p-values for those correlation coefficients? Also, instead of a matrix, how might you get these statistics in tabular format (variable i, variable j, r, and p, for each i–j combination)? Here’s the code (you’ll need the PerformanceAnalytics package to produce the plot).


The cor() function will produce a basic correlation matrix.  12 years ago Bill Venables provided a function on the R help mailing list for replacing the upper triangle of the correlation matrix with the p-values for those correlations (based on the known relationship between t and r). The cor.prob() function will produce this matrix.

Finally, the flattenSquareMatrix() function will “flatten” this matrix to four columns: one column for variable i, one for variable j, one for their correlation, and another for their p-value (thanks to Chris Wallace on StackOverflow for helping out with this one).



Finally, the chart.Correlation() function from the PerformanceAnalytics package produces a very nice scatterplot matrix, with histograms, kernel density overlays, absolute correlations, and significance asterisks (0.05, 0.01, 0.001):