grouped_ggcorrmat: Visualization of a correlogram (or correlation matrix) for...

In ggstatsplot: 'ggplot2' Based Plots with Statistical Details

Visualization of a correlogram (or correlation matrix) for all levels of a grouping variable

Description

Helper function for ggstatsplot::ggcorrmat() to apply this function across multiple levels of a given factor and combining the resulting plots using ggstatsplot::combine_plots().

Usage

grouped_ggcorrmat(
  data,
  ...,
  grouping.var,
  plotgrid.args = list(),
  annotation.args = list()
)

Arguments

data	A data frame from which variables specified are to be taken.
...	Arguments passed on to ggcorrmat 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. 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. matrix.type Character, "upper" (default), "lower", or "full", to display the upper triangular, lower triangular, or full matrix, respectively. 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". 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). 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). 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. 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. 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. 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()). conf.level Scalar between 0 and 1 (default: ⁠95%⁠ confidence/credible intervals, 0.95). 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. 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". 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. 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.
grouping.var	A single grouping variable. A separate plot is created for each of its levels (in factor level order, or in order of appearance for character variables). Rows with a missing value in this variable are removed.
plotgrid.args	A list of additional arguments passed to patchwork::wrap_plots(), except for guides argument which is already separately specified here.
annotation.args	A list of additional arguments passed to patchwork::plot_annotation().

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 patchwork object combining one plot per level of grouping.var (see combine_plots()).

See Also

ggcorrmat, ggscatterstats, grouped_ggscatterstats

Examples

set.seed(123)

grouped_ggcorrmat(
  data = iris,
  grouping.var = Species,
  type = "robust",
  colors = c("#0072B2", "white", "#D55E00"),
  p.adjust.method = "holm",
  plotgrid.args = list(ncol = 1L),
  annotation.args = list(tag_levels = "i")
)

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