knitr::opts_chunk$set( collapse = TRUE, comment = "#>", # out.width = "100%", # dpi = 300, fig.path = "tools/README-", fig.cap = "ggcorrplot: visualize 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.
Find out more at http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2.
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)
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])
# 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.color = "white") # Types of correlogram layout # -------------------------------- # Get the lower triangle ggcorrplot(corr, hc.order = TRUE, type = "lower", outline.color = "white") # Get the upper triangle ggcorrplot(corr, hc.order = TRUE, type = "upper", outline.color = "white") # Change colors and theme # -------------------------------- # Argument colors ggcorrplot( corr, hc.order = TRUE, type = "lower", outline.color = "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" )
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