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ggcorrplot: Visualization of a correlation matrix using ggplot2

ggcorrplot draws a correlation matrix as a ggplot2 plot. Because the result is a plain ggplot object, you can restyle it, annotate it, and combine it with other layers using the usual + syntax.

It can:

• reorder the matrix by hierarchical clustering and outline the clusters,
• show only the lower or upper triangle, or a mixed layout with a different glyph per triangle,
• overlay the correlation coefficients and mark the statistically significant cells (including a standalone significance map), and
• compute the matrix of correlation p-values with cor_pmat().

Learn more in ggcorrplot: Correlation Matrix Heatmap in R with ggplot2.

Installation

Install the released version from CRAN:

install.packages("ggcorrplot")

Or the development version from GitHub:

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

library(ggcorrplot)

Getting started

The examples below use the mtcars data set. cor() builds the correlation matrix and cor_pmat() [in ggcorrplot] computes the matrix of correlation p-values.

data(mtcars)
corr <- round(cor(mtcars), 1)
corr[1:4, 1:4]
#>       mpg  cyl disp   hp
#> mpg   1.0 -0.9 -0.8 -0.8
#> cyl  -0.9  1.0  0.9  0.8
#> disp -0.8  0.9  1.0  0.8
#> hp   -0.8  0.8  0.8  1.0

# Matrix of correlation p-values
p.mat <- cor_pmat(mtcars)
p.mat[1:4, 1:4]
#>               mpg          cyl         disp           hp
#> mpg  0.000000e+00 6.112687e-10 9.380327e-10 1.787835e-07
#> cyl  6.112687e-10 0.000000e+00 1.802838e-12 3.477861e-09
#> disp 9.380327e-10 1.802838e-12 0.000000e+00 7.142679e-08
#> hp   1.787835e-07 3.477861e-09 7.142679e-08 0.000000e+00

Correlation matrix visualization

The default draws each correlation as a colored square; method = "circle" encodes the value with the circle area instead.

ggcorrplot(corr)
ggcorrplot(corr, method = "circle")

Sized glyphs in boxed cells

scale.square = TRUE sizes the squares by the absolute correlation, so strong correlations dominate; cell.grid = TRUE draws a light box around every cell so the glyphs sit inside a grid instead of floating on the axis lines.

ggcorrplot(corr, scale.square = TRUE, cell.grid = TRUE, outline.color = "white")
ggcorrplot(corr, method = "circle", cell.grid = TRUE)

Reorder by clustering, and outline the clusters

hc.order = TRUE reorders the variables by hierarchical clustering so that correlated variables sit together. hc.rect then draws rectangles around the clusters obtained by cutting the tree.

ggcorrplot(corr, hc.order = TRUE, outline.color = "white")
ggcorrplot(corr, hc.order = TRUE, hc.rect = 3, outline.color = "white")

Lower / upper triangle

For a symmetric matrix the two triangles are redundant, so you can keep just one.

ggcorrplot(corr, hc.order = TRUE, type = "lower", outline.color = "white")
ggcorrplot(corr, hc.order = TRUE, type = "upper", outline.color = "white")

Mixed layout

lower.method and upper.method draw a different glyph in each triangle — here the coefficients as numbers below the diagonal and circles above it, with the variable names on the diagonal. Adding cell.grid = TRUE boxes every cell for the tidy corrplot look.

ggcorrplot(corr,
  lower.method = "number", upper.method = "circle",
  cell.grid = TRUE, show.legend = FALSE
)

Add the coefficients

ggcorrplot(corr, hc.order = TRUE, type = "lower", lab = TRUE)

Highlighting significance

Passing p.mat marks the cells whose correlation is not significant at sig.level (default 0.05). By default a cross is drawn over them (insig = "pch"); insig = "blank" hides them instead.

# Cross out the non-significant coefficients
ggcorrplot(corr, hc.order = TRUE, type = "lower", p.mat = p.mat)
# Leave them blank
ggcorrplot(corr, hc.order = TRUE, type = "lower", p.mat = p.mat, insig = "blank")

Significance map

insig = "stars" flips the emphasis: instead of crossing out the non-significant cells, it marks the significant ones with significance stars (***, **, * for p <= 0.001, 0.01, 0.05 – fixed thresholds, not sig.level). With the default lab = FALSE this is a standalone significance map; with lab = TRUE the stars are appended to the coefficients (e.g. -0.85***).

ggcorrplot(corr, p.mat = p.mat, insig = "stars")

Colors and theme

ggcorrplot() returns a ggplot object, so any ggplot2 theme applies. colors sets the low / mid / high gradient. For the ggplot2 side of this, see ggplot2 Colours in R: Change Colours by Group and ggplot2 Themes in R: Customize the Look.

ggcorrplot(corr,
  hc.order = TRUE, type = "lower", outline.color = "white",
  ggtheme = ggplot2::theme_gray,
  colors = c("#6D9EC1", "white", "#E46726")
)

Related tutorials

• ggcorrplot: Correlation Matrix Heatmap in R with ggplot2
• Correlation Matrix in R: Compute, Visualize & P-values
• Correlogram in R: Visualize a Correlation Matrix with corrplot

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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