grouped_ggbarstats: Grouped bar charts with statistical tests

grouped_ggbarstatsR Documentation

Grouped bar charts with statistical tests


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


  output = "plot",
  plotgrid.args = list(),
  annotation.args = list()



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.


Arguments passed on to ggbarstats


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.


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).


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 geom_label.


Title text for the legend.


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


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.


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.


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.


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 {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.


A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter.


Number of digits after decimal point (should be an integer) (Default: k = 2L).


Scalar between 0 and 1. If unspecified, the defaults return 95% confidence/credible intervals (0.95).


Logical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE). If TRUE, McNemar's test expression will be returned. If FALSE, Pearson's chi-square test will be returned.


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


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. This means if there are two levels this will be ratio = c(0.5,0.5) or if there are four levels this will be ratio = c(0.25,0.25,0.25,0.25), etc.


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.


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 single grouping variable.


Character that describes what is to be returned: can be "plot" (default) or "subtitle" or "caption". Setting this to "subtitle" will return the expression containing statistical results. If you have set results.subtitle = FALSE, then this will return a NULL. Setting this to "caption" will return the expression containing details about Bayes Factor analysis, but valid only when type = "parametric" and bf.message = TRUE, otherwise this will return a NULL.


A list of additional arguments passed to patchwork::wrap_plots, except for guides argument which is already separately specified here.


A list of additional arguments passed to patchwork::plot_annotation.


For details, see:

See Also

ggbarstats, ggpiestats, grouped_ggpiestats


# for reproducibility
library(dplyr, warn.conflicts = FALSE)

# let's create a smaller dataframe
diamonds_short <- ggplot2::diamonds %>%
  filter(cut %in% c("Very Good", "Ideal")) %>%
  filter(clarity %in% c("SI1", "SI2", "VS1", "VS2")) %>%
  sample_frac(size = 0.05)

# plot
  data          = diamonds_short,
  x             = color,
  y             = clarity,
  grouping.var  = cut,
  plotgrid.args = list(nrow = 2)

ggstatsplot documentation built on May 21, 2022, 5:05 p.m.