Package {ggcorrplot}

Type: Package
Title: Visualization of a Correlation Matrix using 'ggplot2'
Version: 0.2.0
Date: 2026-07-08
Description: 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 plot. It also includes a function for computing a matrix of correlation p-values.
License: GPL-2
URL: https://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2
BugReports: https://github.com/kassambara/ggcorrplot/issues
Depends: R (≥ 3.3), ggplot2 (≥ 3.3.6)
Imports: reshape2, stats
Suggests: testthat (≥ 3.0.0), knitr, spelling, vdiffr (≥ 1.0.0)
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.1.0
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-07-08 15:18:07 UTC; kassambara
Author: Alboukadel Kassambara [aut, cre], Indrajeet Patil ORCID iD [ctb] (Twitter: @patilindrajeets)
Maintainer: Alboukadel Kassambara <alboukadel.kassambara@gmail.com>
Repository: CRAN
Date/Publication: 2026-07-08 16:00:02 UTC

Visualization of a correlation matrix using ggplot2

Description

Usage

ggcorrplot(
  corr,
  method = c("square", "circle"),
  type = c("full", "lower", "upper"),
  ggtheme = ggplot2::theme_minimal,
  title = "",
  show.legend = TRUE,
  legend.title = "Corr",
  show.diag = NULL,
  colors = c("blue", "white", "red"),
  outline.color = "gray",
  hc.order = FALSE,
  hc.method = "complete",
  lab = FALSE,
  lab_col = "black",
  lab_size = 4,
  lab_fontface = "plain",
  sig.stars = FALSE,
  p.mat = NULL,
  sig.level = 0.05,
  insig = c("pch", "blank"),
  pch = 4,
  pch.col = "black",
  pch.cex = 5,
  tl.cex = 12,
  tl.col = NULL,
  tl.srt = 45,
  tl.vjust = 1,
  tl.hjust = 1,
  digits = 2,
  as.is = FALSE,
  nsmall = 0L,
  leading.zero = TRUE,
  legend.limit = c(-1, 1),
  circle.scale = 1,
  coord.fixed = TRUE
)

cor_pmat(x, ..., use = c("pairwise.complete.obs", "everything"))

Arguments

corr

the correlation matrix to visualize

method

character, the visualization method of correlation matrix to be used. Allowed values are "square" (default), "circle".

type

character, "full" (default), "lower" or "upper" display.

ggtheme

ggplot2 function or theme object. Default value is 'theme_minimal'. Allowed values are the official ggplot2 themes including theme_gray, theme_bw, theme_minimal, theme_classic, theme_void, .... Theme objects are also allowed (e.g., 'theme_classic()').

title

character, title of the graph.

show.legend

logical, if TRUE the legend is displayed.

legend.title

a character string for the legend title. lower triangular, upper triangular or full matrix.

show.diag

NULL or logical, whether display the correlation coefficients on the principal diagonal. If NULL, the default is to show diagonal correlation for type = "full" and to remove it when type is one of "upper" or "lower".

colors

a vector of colors for the fill gradient. The default is a length-3 vector for the low, mid and high correlation values (mapped with scale_fill_gradient2). A vector of any other length (>= 2) is spread evenly across the scale with scale_fill_gradientn, so an n-color palette (e.g. RColorBrewer::brewer.pal(11, "RdBu")) can be passed directly.

outline.color

the outline color of square or circle. Default value is "gray".

hc.order

logical value. If TRUE, correlation matrix will be hc.ordered using hclust function.

hc.method

the agglomeration method to be used in hclust (see ?hclust).

lab

logical value. If TRUE, add correlation coefficient on the plot.

lab_col, lab_size

size and color to be used for the correlation coefficient labels. used when lab = TRUE.

lab_fontface

the font face ("plain", "bold", "italic", "bold.italic") for the correlation coefficient labels. Default is "plain". Used when lab = TRUE.

sig.stars

logical value. If TRUE and a p.mat is supplied, significance stars are appended to the coefficient labels (*** for p < 0.001, ** for p < 0.01, * for p < 0.05), e.g. "-0.85**". Only used when lab = TRUE. Default is FALSE. When TRUE, significance is shown by the stars and the insig = "pch" markers are not drawn.

p.mat

matrix of p-value. If NULL, arguments sig.level, insig, pch, pch.col, pch.cex is invalid.

sig.level

significant level, if the p-value in p-mat is bigger than sig.level, then the corresponding correlation coefficient is regarded as insignificant.

insig

character, specialized insignificant correlation coefficients, "pch" (default), "blank". If "blank", wipe away the corresponding glyphs; if "pch", add characters (see pch for details) on corresponding glyphs.

pch

add character on the glyphs of insignificant correlation coefficients (only valid when insig is "pch"). Default value is 4.

pch.col, pch.cex

the color and the cex (size) of pch (only valid when insig is "pch").

tl.cex, tl.col, tl.srt

the size, the color and the string rotation of text label (variable names). tl.col defaults to NULL, which inherits the color from the theme.

tl.vjust, tl.hjust

the vertical and horizontal justification of the x-axis text labels, passed to element_text. Both default to 1; adjust them to reposition the variable-name labels.

digits

Decides the number of decimal digits to be displayed (Default: '2').

as.is

A logical passed to melt.array. If TRUE, dimnames will be left as strings instead of being converted using type.convert.

nsmall

the minimum number of digits to the right of the decimal point in the coefficient labels, passed to format. Default is 0 (no minimum, current behavior). Set e.g. nsmall = 2 to keep trailing zeros (such as 0.70). Only used when lab = TRUE.

leading.zero

logical. If TRUE (default), coefficient labels keep the leading zero (e.g. 0.23, -0.67). Set to FALSE to drop it (.23, -.67), which is common for correlation tables. Only used when lab = TRUE.

legend.limit

a length-2 numeric vector giving the limits of the fill color scale. Default c(-1, 1) (suitable for a correlation matrix); set to NULL to use the data range instead, e.g. for a covariance matrix.

circle.scale

a scaling factor for the circle sizes when method = "circle". Default is 1; increase it (e.g. circle.scale = 2) for larger circles or decrease it for smaller ones, which is useful when the output device size makes the default circles too small or too large. Has no effect when method = "square".

coord.fixed

logical value. If TRUE (default), the plot uses coord_fixed so the cells are square. Set to FALSE to let the cells fill the plotting area (a non 1:1 aspect ratio), which can look better with many long variable names.

x

numeric matrix or data frame

...

other arguments to be passed to the function cor.test.

use

character, how to treat pairs involving missing values when deciding which cells are NA. Either "pairwise.complete.obs" (default; test every pair that has enough overlapping observations) or "everything" (set a pair to NA as soon as either variable has a missing value, matching cor's default). Mirrors the corresponding values of cor's use argument.

Details

cor_pmat() tests each pair of columns with cor.test. A pair with fewer than three overlapping non-missing observations (which cor.test cannot test, e.g. two variables that never co-occur) yields NA for that cell rather than aborting the whole computation. Pairs that can be tested are computed as before, and errors they raise are passed through.

The use argument controls which pairs are returned as NA so the p-value matrix can be aligned with a correlation matrix built the same way. With the default "pairwise.complete.obs" every pair that has enough overlapping observations is tested (the previous behavior). With "everything" a pair is set to NA whenever either variable has any missing value, so the NA pattern matches cor(x) with its default use = "everything".

Value

Examples

# Compute a correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
corr

# Compute a matrix of correlation p-values
p.mat <- cor_pmat(mtcars)
p.mat

# Visualize the correlation matrix
# --------------------------------
# method = "square" or "circle"
ggcorrplot(corr)
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 upeper 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,
  ggtheme = ggplot2::theme_dark(),
)

# 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"
)

# Changing number of digits for correlation coeffcient
# --------------------------------
ggcorrplot(cor(mtcars),
  type = "lower",
  insig = "blank",
  lab = TRUE,
  digits = 3
)