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


  y = NULL,
  counts = NULL,
  type = "parametric",
  paired = FALSE,
  results.subtitle = TRUE,
  label = "percentage",
  label.args = list(direction = "both"),
  label.repel = FALSE,
  digits = 2L,
  proportion.test = results.subtitle,
  digits.perc = 0L,
  bf.message = TRUE,
  ratio = NULL,
  conf.level = 0.95,
  sampling.plan = "indepMulti",
  fixed.margin = "rows",
  prior.concentration = 1,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  legend.title = NULL,
  ggtheme = ggstatsplot::theme_ggstatsplot(),
  package = "RColorBrewer",
  palette = "Dark2",
  ggplot.component = NULL,



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.


The variable to use as the rows in the contingency table. Please note that if there are empty factor levels in your variable, they will be dropped.


The variable to use as the columns in the contingency table. Please note that if there are empty factor levels in your variable, they will be dropped. 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. This argument can not be NULL for ggbarstats function.


The variable in data containing counts, or NULL if each row represents a single observation.


A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter.


Logical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE).


Decides whether the results of statistical tests are to be displayed as a subtitle (Default: TRUE). If set to FALSE, only the plot will be returned.


Character decides what information needs to be displayed on the label in each pie slice. Possible options are "percentage" (default), "counts", "both".


Additional aesthetic arguments that will be passed to ggplot2::geom_label().


Whether labels should be repelled using {ggrepel} package. This can be helpful in case the labels are overlapping.


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()).


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.


Numeric that decides number of decimal places for percentage labels (Default: 0L).


Logical that decides whether to display Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (Default: TRUE).


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.


Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.


Character describing the sampling plan. Possible options are "indepMulti" (independent multinomial; default), "poisson", "jointMulti" (joint multinomial), "hypergeom" (hypergeometric). For more, see ?BayesFactor::contingencyTableBF().


For the independent multinomial sampling plan, which margin is fixed ("rows" or "cols"). Defaults to "rows".


Specifies the prior concentration parameter, set to 1 by default. It indexes the expected deviation from the null hypothesis under the alternative, and corresponds to Gunel and Dickey's (1974) "a" parameter.


The text for the plot title.


The text for the plot subtitle. Will work only if results.subtitle = FALSE.


The text for the plot caption. This argument is relevant only if bf.message = FALSE.


Title text for the legend.


A {ggplot2} theme. Default value is ggstatsplot::theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., 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 contains. 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.

package, palette

Name of the package from which the given palette is to be extracted. The available palettes and packages can be checked by running View(paletteer::palettes_d_names).


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.

Summary of graphics

graphical elementgeom usedargument for further modification
pie slicesggplot2::geom_col()NA

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

two-way table

Hypothesis testing

TypeDesignTestFunction used
Parametric/Non-parametricUnpairedPearson's chi-squared teststats::chisq.test()
BayesianUnpairedBayesian Pearson's chi-squared testBayesFactor::contingencyTableBF()
Parametric/Non-parametricPairedMcNemar's chi-squared teststats::mcnemar.test()

Effect size estimation

TypeDesignEffect sizeCI available?Function used
Parametric/Non-parametricUnpairedCramer's VYeseffectsize::cramers_v()
BayesianUnpairedCramer's VYeseffectsize::cramers_v()
Parametric/Non-parametricPairedCohen's gYeseffectsize::cohens_g()

one-way table

Hypothesis testing

TypeTestFunction used
Parametric/Non-parametricGoodness of fit chi-squared teststats::chisq.test()
BayesianBayesian Goodness of fit chi-squared test(custom)

Effect size estimation

TypeEffect sizeCI available?Function used
Parametric/Non-parametricPearson's CYeseffectsize::pearsons_c()


# for reproducibility

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

# looking at the plot

# extracting details from statistical tests
#> $subtitle_data
#> # A tibble: 1 × 13
#>   statistic    df p.value method                                   effectsize 
#>       <dbl> <dbl>   <dbl> <chr>                                    <chr>      
#> 1       0.5     1   0.480 Chi-squared test for given probabilities Pearson's C
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.124       0.95        0     0.426 ncp         chisq                32
#>   expression
#>   <list>    
#> 1 <language>
#> $caption_data
#> # A tibble: 1 × 4
#>    bf10 prior.scale method                                      expression
#>   <dbl>       <dbl> <chr>                                       <list>    
#> 1 0.180           1 Bayesian one-way contingency table analysis <language>
#> $pairwise_comparisons_data
#> $descriptive_data
#> # A tibble: 2 × 4
#>   vs    counts  perc .label
#>   <fct>  <int> <dbl> <chr> 
#> 1 1         14  43.8 44%   
#> 2 0         18  56.2 56%   
#> $one_sample_data
#> $tidy_data
#> $glance_data

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