ggcorrmat: Visualization of a correlation matrix

In ggstatsplot: 'ggplot2' Based Plots with Statistical Details

Visualization of a correlation matrix

Description

Correlation matrix containing results from pairwise correlation tests. If you want a data frame of (grouped) correlation matrix, use correlation::correlation() instead. It can also do grouped analysis when used with output from dplyr::group_by().

Usage

ggcorrmat(
  data,
  cor.vars = NULL,
  cor.vars.names = NULL,
  matrix.type = "upper",
  type = "parametric",
  tr = 0.2,
  partial = FALSE,
  digits = 2L,
  sig.level = 0.05,
  conf.level = 0.95,
  bf.prior = 0.707,
  p.adjust.method = "holm",
  colors = c("#EA4335", "white", "#4285F4"),
  pch = "cross",
  ggcorrplot.args = list(method = "square", outline.color = "black", pch.cex = 14),
  ggtheme = ggstatsplot::theme_ggstatsplot(),
  ggplot.component = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  ...
)

Arguments

data	A data frame from which variables specified are to be taken.
cor.vars	Variables for which the correlation matrix is to be computed and visualized, specified using {tidyselect} syntax (e.g., c(var1, var2) or starts_with("Sepal")). If NULL (default), all numeric variables from data will be used.
cor.vars.names	Optional list of names to be used for cor.vars. The names should be entered in the same order.
matrix.type	Character, "upper" (default), "lower", or "full", to display the upper triangular, lower triangular, or full matrix, respectively.
type	A character specifying the type of statistical approach: "parametric" "nonparametric" "robust" "bayes" You can specify just the initial letter (e.g. "np" or "bf"). Matching is on the initial lowercase letter only, so any other value (including capitalized values such as "Bayes") falls back to "parametric" without a warning.
tr	Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.
partial	Can be TRUE for partial correlations. For Bayesian partial correlations, "full" instead of pseudo-Bayesian partial correlations (i.e., Bayesian correlation based on frequentist partialization) are returned.
digits	Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).
sig.level	Significance level (Default: 0.05). If the (adjusted; see p.adjust.method) p-value is bigger than sig.level, then the corresponding correlation coefficient is regarded as insignificant and flagged as such in the plot. Ignored for type = "bayes".
conf.level	Scalar between 0 and 1 (default: ⁠95%⁠ confidence/credible intervals, 0.95).
bf.prior	A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.
p.adjust.method	Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
colors	A character vector of exactly three colors for the gradient: low (negative correlations), mid (zero), and high (positive correlations). Must be a diverging palette so that the sign of the correlation is visually obvious. Default: c("#EA4335", "white", "#4285F4") (red–white–blue).
pch	Decides the point shape to be used for insignificant correlation coefficients. Default: pch = "cross". The caption explaining the insignificance marker is added only for pch = "cross" (or 4).
ggcorrplot.args	A list of additional (mostly aesthetic) arguments that will be passed to ggcorrplot::ggcorrplot() function. The list should avoid any of the following arguments since they are already internally being used: corr, p.mat, sig.level, ggtheme, colors, type, lab, pch, legend.title, digits.
ggtheme	A {ggplot2} theme. Default value is theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., ggplot2::theme_bw()), or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). But note that sometimes these themes will remove some of the details that {ggstatsplot} plots typically contain. For example, if relevant, ggbetweenstats() shows details about multiple comparison test as a label on the secondary Y-axis. Some themes (e.g. ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and thus the details as well.
ggplot.component	A ggplot component to be added to the plot prepared by {ggstatsplot}. This argument is primarily helpful for grouped_ variants of all primary functions. Default is NULL. The argument should be entered as a {ggplot2} function or a list of {ggplot2} functions.
title	The text for the plot title.
subtitle	The text for the plot subtitle.
caption	The text for the plot caption. If the insignificance marker caption is shown (see pch), this text is displayed above it.
...	Currently ignored.

Details

Missing values are handled using pairwise deletion (each correlation uses all complete pairs of observations), in which case the legend shows the minimum, mode, and maximum sample size across pairs. Partial correlations use only complete cases.

For details, see: https://www.indrapatil.com/ggstatsplot/articles/web_only/ggcorrmat.html

Value

A ggplot object, which can be further modified with {ggplot2} functions. Note that extract_stats() does not return statistical details for this plot; use correlation::correlation() instead.

Summary of graphics

graphical element	geom used	argument for further modification
correlation matrix	ggcorrplot::ggcorrplot()	ggcorrplot.args

Correlation analyses

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

Hypothesis testing and effect size estimation

Type	Test	CI available?	Function used
Parametric	Pearson's correlation coefficient	Yes	correlation::correlation()
Non-parametric	Spearman's rank correlation coefficient	Yes	correlation::correlation()
Robust	Winsorized Pearson's correlation coefficient	Yes	correlation::correlation()
Bayesian	Bayesian Pearson's correlation coefficient	Yes	correlation::correlation()

See Also

grouped_ggcorrmat ggscatterstats grouped_ggscatterstats

Examples

set.seed(123)
ggcorrmat(iris)

# with data containing NAs (uses pairwise complete observations)
ggcorrmat(airquality)

# selecting specific variables
ggcorrmat(iris, cor.vars = c(Sepal.Length, Petal.Length, Petal.Width))

ggstatsplot documentation built on Oct. 18, 2026, 1:07 a.m.