Introduction

R package corrplot provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.

corrplot is very easy to use and provides a rich array of plotting options in visualization method, graphic layout, color, legend, text labels, etc. It also provides p-values and confidence intervals to help users determine the statistical significance of the correlations.

corrplot() has about 50 parameters, however the mostly common ones are only a few. We can get a correlation matrix plot with only one line of code in most scenes.

The mostly using parameters include method, type, order, diag, and etc.

There are seven visualization methods (parameter method) in corrplot package, named 'circle', 'square', 'ellipse', 'number', 'shade', 'color', 'pie'. Color intensity of the glyph is proportional to the correlation coefficients by default color setting.

corrplot.mixed() is a wrapped function for mixed visualization style, which can set the visual methods of lower and upper triangular separately.

There are three layout types (parameter type): 'full', 'upper' and 'lower'.

The correlation matrix can be reordered according to the correlation matrix coefficients. This is important to identify the hidden structure and pattern in the matrix.

library(corrplot)
## corrplot 0.90 loaded
M = cor(mtcars)
corrplot(M, method = 'number') # colorful number

corrplot(M, method = 'color', order = 'alphabet') 

corrplot(M) # by default, method = 'circle'

corrplot(M, order = 'AOE') # after 'AOE' reorder

corrplot(M, method = 'shade', order = 'AOE', diag = FALSE) 

corrplot(M, method = 'square', order = 'FPC', type = 'lower', diag = FALSE)

corrplot(M, method = 'ellipse', order = 'AOE', type = 'upper')

corrplot.mixed(M, order = 'AOE')

corrplot.mixed(M, lower = 'shade', upper = 'pie', order = 'hclust')

Reorder a correlation matrix

The details of four order algorithms, named 'AOE', 'FPC', 'hclust', 'alphabet' are as following.

You can also reorder the matrix ‘manually’ via function corrMatOrder().

If using 'hclust', corrplot() can draw rectangles around the plot of correlation matrix based on the results of hierarchical clustering.

corrplot(M, order = 'hclust', addrect = 2)

corrplot(M, method = 'square', diag = FALSE, order = 'hclust', 
         addrect = 3, rect.col = 'blue', rect.lwd = 3, tl.pos = 'd')