ggbarstats: Stacked bar charts with statistical tests

In ggstatsplot: 'ggplot2' Based Plots with Statistical Details

Stacked bar charts with statistical tests

Description

Bar charts for categorical data with statistical details included in the plot as a subtitle.

Usage

ggbarstats(
  data,
  x,
  y = NULL,
  counts = NULL,
  type = "parametric",
  paired = FALSE,
  results.subtitle = TRUE,
  label = "percentage",
  label.args = list(alpha = 1, fill = "white"),
  sample.size.label.args = list(size = 4),
  digits = 2L,
  proportion.test = results.subtitle,
  digits.perc = 0L,
  bf.message = TRUE,
  ratio = NULL,
  alternative = "two.sided",
  conf.level = 0.95,
  p.adjust.method = "holm",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  legend.title = NULL,
  xlab = NULL,
  ylab = NULL,
  ggtheme = ggstatsplot::theme_ggstatsplot(),
  palette = "ggthemes::gdoc",
  ggplot.component = NULL,
  ...
)

Arguments

data	A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix, table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.
x	The variable to use as the rows in the contingency table. Note that empty factor levels are not dropped: they are shown in the legend and counted (with zero observations) in the statistical test. Use droplevels() beforehand if this is not intended.
y	The variable to use as the columns in the contingency table. Default is NULL. If NULL, one-sample proportion test (a goodness of fit test) will be run for the x variable. Otherwise an appropriate association test will be run.
counts	The variable in data containing counts, or NULL if each row represents a single observation.
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.
paired	Logical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE). Only relevant for two-way tables (i.e., when y is supplied), and paired designs are only supported for frequentist tests (McNemar's test). For a two-way table with type = "bayes", paired is ignored and the Bayesian test of independence for unpaired data is run instead. For one-way tables (y = NULL), paired has no effect.
results.subtitle	Decides whether the results of statistical tests are to be displayed as a subtitle (Default: TRUE). Setting this to FALSE also skips the automatic Bayes Factor caption. See the individual annotation arguments to control other plot details.
label	Character that decides what information needs to be displayed on the label in each pie slice. Possible options are "percentage" (default), "counts", "both".
label.args	Additional aesthetic arguments that will be passed to ggplot2::geom_label().
sample.size.label.args	Additional aesthetic arguments that will be passed to ggplot2::geom_text().
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()).
proportion.test	Decides whether proportion test for x variable is to be carried out for each level of y. Defaults to results.subtitle. In ggbarstats(), only p-values from this test will be displayed. Always FALSE when type = "bayes" or when y is NULL.
digits.perc	Numeric that decides number of decimal places for percentage labels (Default: 0L).
bf.message	Logical that decides whether to display Bayes Factor in favor of the null hypothesis as a caption. This argument is relevant only for non-Bayesian types and unpaired designs (paired = FALSE) (Default: TRUE).
ratio	A vector of proportions: the expected proportions for the proportion test (should sum to 1). Default is NULL, which means the null is equal theoretical proportions across the levels of the nominal variable. E.g., ratio = c(0.5, 0.5) for two levels, ratio = c(0.25, 0.25, 0.25, 0.25) for four levels, etc.
alternative	a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.
conf.level	Scalar between 0 and 1 (default: ⁠95%⁠ confidence/credible intervals, 0.95).
p.adjust.method	Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
title	The text for the plot title.
subtitle	The text for the plot subtitle. Will work only if results.subtitle = FALSE.
caption	The text for the plot caption. It will be replaced by the Bayes Factor caption whenever that is displayed (see bf.message).
legend.title	Title text for the legend. If NULL (default), the name of the x variable is used.
xlab	Label for x axis. If NULL (default), the name of the y variable is used (no label if y is NULL), since bars are drawn for each level of y.
ylab	Label for y axis. If NULL (default), no label is shown.
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.
palette	Name of the discrete palette in "package::palette" format to be used for coloring (via {paletteer}). Run View(paletteer::palettes_d_names) to see all available options.
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.
...	Currently ignored.

Details

Rows with a missing value in x, y, or counts are removed before plotting and analysis.

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

Value

A ggplot object, which can be further modified with {ggplot2} functions. Use extract_stats() to get the statistical details shown in the plot as data frames.

Summary of graphics

graphical element	geom used	argument for further modification
bars	ggplot2::geom_bar()	NA
descriptive labels	ggplot2::geom_label()	label.args
sample size labels	ggplot2::geom_text()	sample.size.label.args

Contingency table 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

There are no dedicated non-parametric or robust contingency table analyses. type = "nonparametric" and type = "robust" are accepted, but run the same frequentist tests as type = "parametric" (the "Parametric/Non-parametric" rows below).

Two-way table

Hypothesis testing

Type	Design	Test	Function used
Parametric/Non-parametric	Unpaired	Pearson's chi-squared test	stats::chisq.test()
Bayesian	Unpaired	Bayesian Pearson's chi-squared test	BayesFactor::contingencyTableBF()
Parametric/Non-parametric	Paired	McNemar's chi-squared test	stats::mcnemar.test()
Bayesian	Paired	No	No

Effect size estimation

Type	Design	Effect size	CI available?	Function used
Parametric/Non-parametric	Unpaired	Cramer's V	Yes	effectsize::cramers_v()
Bayesian	Unpaired	Cramer's V	Yes	effectsize::cramers_v()
Parametric/Non-parametric	Paired	Cohen's g	Yes	effectsize::cohens_g()
Bayesian	Paired	No	No	No

Paired Bayesian analysis is not supported: for a two-way table with type = "bayes", the paired argument is ignored and the unpaired Bayesian test is run instead.

One-way table

Hypothesis testing

Type	Test	Function used
Parametric/Non-parametric	Goodness of fit chi-squared test	stats::chisq.test()
Bayesian	Bayesian Goodness of fit chi-squared test	(custom)

Effect size estimation

Type	Effect size	CI available?	Function used
Parametric/Non-parametric	Pearson's C	Yes	effectsize::pearsons_c()
Bayesian	No	No	No

Pairwise comparisons

When there is a two-way table, x has more than two levels, and paired = FALSE, pairwise contingency table analyses (Fisher's exact tests) are computed using statsExpressions::pairwise_contingency_table(). These pairwise results are not displayed in the plot because bar and pie charts lack a natural visual representation for pairwise significance annotations (unlike box/violin plots, which use bracket annotations). Additionally, there is no established convention for overlaying pairwise comparisons on pie charts, and both ggpiestats() and ggbarstats() are designed to remain visually congruent. The pairwise results are available as a data frame via extract_stats(plot)$pairwise_comparisons_data.

See Also

grouped_ggbarstats, ggpiestats, grouped_ggpiestats

Examples

# for reproducibility
set.seed(123)

# one sample goodness of fit proportion test
p <- ggbarstats(mtcars, vs)

# looking at the plot
p

# extracting details from statistical tests
extract_stats(p)

# association test (or contingency table analysis)
ggbarstats(mtcars, vs, cyl)

# with 3+ x levels, pairwise comparisons are available
ggbarstats(mtcars, cyl, am)

# Bayesian test
ggbarstats(mtcars, vs, cyl, type = "bayes")

# using pre-aggregated data with counts
ggbarstats(as.data.frame(Titanic), x = Survived, y = Sex, counts = Freq)

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