In YTLogos/ggcorrplot: Visualization of a Correlation Matrix using 'ggplot2'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-",
  fig.cap="ggcorrplot: visualize correlation matrix using ggplot2"
)

ggcorrplot: Visualization of a correlation matrix using ggplot2

The ggcorrplot package can be used to visualize easily a correlation matrix using ggplot2. It provides a solution for reordering the correlation matrix and displays the significance level on the correlogram. It includes also a function for computing a matrix of correlation p-values. It's inspired from the package corrplot.

Find out more at http://www.sthda.com/english/wiki/ggcorrplot.

Installation and loading

ggcorrplot can be installed from CRAN as follow:

install.packages("ggcorrplot")

Or, install the latest version from GitHub:

# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

# Loading
library(ggcorrplot)

Getting started

Compute a correlation matrix

The mtcars data set will be used in the following R code. The function cor_pmat() [in ggcorrplot] computes a matrix of correlation p-values.

# Compute a correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
head(corr[, 1:6])

# Compute a matrix of correlation p-values
p.mat <- cor_pmat(mtcars)
head(p.mat[, 1:4])

Correlation matrix visualization

# Visualize the correlation matrix
# --------------------------------
# method = "square" (default)
ggcorrplot(corr)
# method = "circle"
ggcorrplot(corr, method = "circle")

# Reordering the correlation matrix
# --------------------------------
# using hierarchical clustering
ggcorrplot(corr, hc.order = TRUE, outline.col = "white")

# Types of correlogram layout
# --------------------------------
# Get the lower triangle
ggcorrplot(corr, hc.order = TRUE, type = "lower",
     outline.col = "white")
# Get the upeper triangle
ggcorrplot(corr, hc.order = TRUE, type = "upper",
     outline.col = "white")

# Change colors and theme
# --------------------------------
# Argument colors
ggcorrplot(corr, hc.order = TRUE, type = "lower",
   outline.col = "white",
   ggtheme = ggplot2::theme_gray,
   colors = c("#6D9EC1", "white", "#E46726"))

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr, hc.order = TRUE, type = "lower",
   lab = TRUE)

# Add correlation significance level
# --------------------------------
# Argument p.mat
# Barring the no significant coefficient
ggcorrplot(corr, hc.order = TRUE,
    type = "lower", p.mat = p.mat)
# Leave blank on no significant coefficient
ggcorrplot(corr, p.mat = p.mat, hc.order = TRUE,
    type = "lower", insig = "blank")