# More on Exploring Correlations in R

**Getting Genetics Done**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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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):

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**Getting Genetics Done**.

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