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.

grouped_ggbarstats(
data,
x,
y,
counts = NULL,
grouping.var,
output = "plot",
plotgrid.args = list(),
annotation.args = list(),
...
)

## Arguments

data A dataframe (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. 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. A string naming a variable in data containing counts, or NULL if each row represents a single observation. A single grouping variable (can be entered either as a bare name x or as a string "x"). 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. Arguments passed on to ggbarstats xlabCustom text for the x axis label (Default: NULL, which will cause the x axis label to be the x variable). ylabCustom text for the y axis label (Default: NULL). typeA character specifying the type of statistical approach: "parametric" "nonparametric" "robust" "bayes" You can specify just the initial letter. pairedLogical 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. results.subtitleDecides 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. labelCharacter decides what information needs to be displayed on the label in each pie slice. Possible options are "percentage" (default), "counts", "both". label.argsAdditional aesthetic arguments that will be passed to geom_label. kNumber of digits after decimal point (should be an integer) (Default: k = 2L). proportion.testDecides whether proportion test for x variable is to be carried out for each level of y (Default: TRUE). In ggbarstats, only p-values from this test will be displayed. perc.kNumeric that decides number of decimal places for percentage labels (Default: 0). bf.messageLogical that decides whether to display Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (Default: TRUE). ratioA 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. conf.levelScalar between 0 and 1. If unspecified, the defaults return 95% confidence/credible intervals (0.95). sampling.planCharacter describing the sampling plan. Possible options are "indepMulti" (independent multinomial; default), "poisson", "jointMulti" (joint multinomial), "hypergeom" (hypergeometric). For more, see ?BayesFactor::contingencyTableBF(). fixed.marginFor the independent multinomial sampling plan, which margin is fixed ("rows" or "cols"). Defaults to "rows". prior.concentrationSpecifies 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. subtitleThe text for the plot subtitle. Will work only if results.subtitle = FALSE. captionThe text for the plot caption. legend.titleTitle text for the legend. ggthemeA ggplot2 theme. Default value is ggstatsplot::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.). packageName 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). paletteName 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). ggplot.componentA 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.

## Details

For more details, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html

ggbarstats, ggpiestats, grouped_ggpiestats

## Examples

# \donttest{
# for reproducibility
set.seed(123)
library(ggstatsplot)

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

# plot
# let's skip statistical analysis
grouped_ggbarstats(
data = diamonds_short,
x = color,
y = clarity,
grouping.var = cut,
plotgrid.args = list(nrow = 2)
)
#> Warning: Chi-squared approximation may be incorrect

# }