Skip to contents

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


  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,
  conf.level = 0.95,
  sampling.plan = "indepMulti",
  fixed.margin = "rows",
  prior.concentration = 1,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  legend.title = NULL,
  xlab = NULL,
  ylab = 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().


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


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.


Label for x axis variable. If NULL (default), variable name for x will be used.


Labels for y axis variable. If NULL (default), variable name for y will be used.


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
descriptive labelsggplot2::geom_label()label.args
sample size labelsggplot2::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

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

# creating a plot
p <- ggbarstats(mtcars, x = vs, y = cyl)

# looking at the plot

# extracting details from statistical tests
#> $subtitle_data
#> # A tibble: 1 × 13
#>   statistic    df   p.value method                     effectsize       
#>       <dbl> <int>     <dbl> <chr>                      <chr>            
#> 1      21.3     2 0.0000232 Pearson's Chi-squared test Cramer's V (adj.)
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.789       0.95    0.371         1 ncp         chisq                32
#>   expression
#>   <list>    
#> 1 <language>
#> $caption_data
#> # A tibble: 1 × 15
#>   term  conf.level effectsize estimate conf.low conf.high
#>   <chr>      <dbl> <chr>         <dbl>    <dbl>     <dbl>
#> 1 Ratio       0.95 Cramers_v     0.683    0.436     0.840
#>   prior.distribution      prior.location prior.scale   bf10
#>   <chr>                            <dbl>       <dbl>  <dbl>
#> 1 independent multinomial              0           1 30129.
#>   method                              conf.method log_e_bf10 n.obs expression
#>   <chr>                               <chr>            <dbl> <int> <list>    
#> 1 Bayesian contingency table analysis ETI               10.3    32 <language>
#> $pairwise_comparisons_data
#> $descriptive_data
#> # A tibble: 5 × 5
#>   cyl   vs    counts   perc .label
#>   <fct> <fct>  <int>  <dbl> <chr> 
#> 1 4     1         10  90.9  91%   
#> 2 6     1          4  57.1  57%   
#> 3 4     0          1   9.09 9%    
#> 4 6     0          3  42.9  43%   
#> 5 8     0         14 100    100%  
#> $one_sample_data
#> # A tibble: 3 × 10
#>   cyl   counts  perc N        statistic    df  p.value method    .label .p.label
#>   <fct>  <int> <dbl> <chr>        <dbl> <dbl>    <dbl> <chr>     <glue> <glue>  
#> 1 8         14  43.8 (n = 14)    14         1 0.000183 Chi-squa… list(… list(~i…
#> 2 6          7  21.9 (n = 7)      0.143     1 0.705    Chi-squa… list(… list(~i…
#> 3 4         11  34.4 (n = 11)     7.36      1 0.00666  Chi-squa… list(… list(~i…
#> $tidy_data
#> $glance_data