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

grouped_ggpiestats(
data,
main,
condition = NULL,
counts = NULL,
grouping.var,
title.prefix = NULL,
output = "plot",
x = NULL,
y = NULL,
...,
plotgrid.args = list(),
title.text = NULL,
title.args = list(size = 16, fontface = "bold"),
caption.text = NULL,
caption.args = list(size = 10),
sub.text = NULL,
sub.args = list(size = 12)
)

## Arguments

data A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted. The variable to use as the rows in the contingency table. 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. 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 (Default). A single grouping variable (can be entered either as a bare name x or as a string "x"). Character string specifying the prefix text for the fixed plot title (name of each factor level) (Default: NULL). If NULL, the variable name entered for grouping.var will be used. 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. For functions ggpiestats and ggbarstats, setting output = "proptest" will return a dataframe containing results from proportion tests. The variable to use as the rows in the contingency table. 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. This argument can not be NULL for ggbarstats function. Arguments passed on to ggpiestats proportion.testDecides whether proportion test for main variable is to be carried out for each level of condition (Default: TRUE). perc.kNumeric that decides number of decimal places for percentage labels (Default: 0). 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. label.repelWhether labels should be repelled using ggrepel package. This can be helpful in case the labels are overlapping. legend.titleTitle text for the legend. 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. conf.levelScalar between 0 and 1. If unspecified, the defaults return 95% lower and upper confidence intervals (0.95). nbootNumber of bootstrap samples for computing confidence interval for the effect size (Default: 100). kNumber of digits after decimal point (should be an integer) (Default: k = 2). 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). subtitleThe text for the plot subtitle. Will work only if results.subtitle = FALSE. captionThe text for the plot caption. ggthemeA function, ggplot2 theme name. Default value is ggplot2::theme_bw(). Any of the ggplot2 themes, or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). ggstatsplot.layerLogical that decides whether theme_ggstatsplot theme elements are to be displayed along with the selected ggtheme (Default: TRUE). theme_ggstatsplot is an opinionated theme layer that override some aspects of the selected ggtheme. packageName of package from which the palette is desired as string or symbol. paletteName of palette as string or symbol. ggplot.componentA ggplot component to be added to the plot prepared by ggstatsplot. This argument is primarily helpful for grouped_ variant of the current function. Default is NULL. The argument should be entered as a function. 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. 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. pairedLogical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE). If TRUE, McNemar's test subtitle will be returned. If FALSE, Pearson's chi-square test will be returned. A list of additional arguments to cowplot::plot_grid. String or plotmath expression to be drawn as title for the combined plot. A list of additional arguments provided to title, caption and sub, resp. String or plotmath expression to be drawn as the caption for the combined plot. A list of additional arguments provided to title, caption and sub, resp. The label with which the combined plot should be annotated. Can be a plotmath expression. A list of additional arguments provided to title, caption and sub, resp.

## Value

Unlike a number of statistical softwares, ggstatsplot doesn't provide the option for Yates' correction for the Pearson's chi-squared statistic. This is due to compelling amount of Monte-Carlo simulation research which suggests that the Yates' correction is overly conservative, even in small sample sizes. As such it is recommended that it should not ever be applied in practice (Camilli & Hopkins, 1978, 1979; Feinberg, 1980; Larntz, 1978; Thompson, 1988).

For more about how the effect size measures and their confidence intervals are computed, see ?rcompanion::cohenG, ?rcompanion::cramerV, and ?rcompanion::cramerVFit.

## References

https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html

ggbarstats, ggpiestats, grouped_ggbarstats

## Examples

# \donttest{
# grouped one-sample proportion tests
ggstatsplot::grouped_ggpiestats(
data = mtcars,
grouping.var = am,
x = cyl
)#> Warning: Chi-squared approximation may be incorrect
# the following will take slightly more amount of time
# for reproducibility
set.seed(123)

# let's create a smaller dataframe
diamonds_short <- ggplot2::diamonds %>%
dplyr::filter(.data = ., cut %in% c("Fair", "Very Good", "Ideal")) %>%
dplyr::sample_frac(tbl = ., size = 0.10)

# plot
ggstatsplot::grouped_ggpiestats(
data = diamonds_short,
x = color,
y = clarity,
grouping.var = cut,
nboot = 20,
sampling.plan = "poisson",
title.prefix = "Quality",
slice.label = "both",
messages = FALSE,
perc.k = 1,
plotgrid.args = list(nrow = 3)
)#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect# }