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

grouped_ggbetweenstats(
data,
x,
y,
grouping.var,
outlier.label = NULL,
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 grouping (or independent) variable from the dataframe data. The response (or outcome or dependent) variable from the dataframe data. A single grouping variable (can be entered either as a bare name x or as a string "x"). Label to put on the outliers that have been tagged. This can't be the same as x argument. 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 ggbetweenstats plot.typeCharacter describing the type of plot. Currently supported plots are "box" (for only boxplots), "violin" (for only violin plots), and "boxviolin" (for a combination of box and violin plots; default). xlabLabels for x and y axis variables. If NULL (default), variable names for x and y will be used. ylabLabels for x and y axis variables. If NULL (default), variable names for x and y will be used. pairwise.comparisonsLogical that decides whether pairwise comparisons are to be displayed (default: TRUE). Please note that only significant comparisons will be shown by default. To change this behavior, select appropriate option with pairwise.display argument. The pairwise comparison dataframes are prepared using the pairwiseComparisons::pairwise_comparisons function. For more details about pairwise comparisons, see the documentation for that function. p.adjust.methodAdjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". pairwise.displayDecides which pairwise comparisons to display. Available options are: "significant" (abbreviation accepted: "s") "non-significant" (abbreviation accepted: "ns") "all" You can use this argument to make sure that your plot is not uber-cluttered when you have multiple groups being compared and scores of pairwise comparisons being displayed. bf.priorA number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors. 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). 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. subtitleThe text for the plot subtitle. Will work only if results.subtitle = FALSE. captionThe text for the plot caption. outlier.colorDefault aesthetics for outliers (Default: "black"). outlier.taggingDecides whether outliers should be tagged (Default: FALSE). outlier.shapeHiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden. outlier.label.argsA list of additional aesthetic arguments to be passed to ggrepel::geom_label_repel for outlier label plotting. outlier.coefCoefficient for outlier detection using Tukey's method. With Tukey's method, outliers are below (1st Quartile) or above (3rd Quartile) outlier.coef times the Inter-Quartile Range (IQR) (Default: 1.5). centrality.plottingLogical that decides whether centrality tendency measure is to be displayed as a point with a label (Default: TRUE). Function decides which central tendency measure to show depending on the type argument. mean for parametric statistics median for non-parametric statistics trimmed mean for robust statistics MAP estimator for Bayesian statistics If you want default centrality parameter, you can specify this using centrality.type argument. centrality.typeDecides which centrality parameter is to be displayed. The default is to choose the same as type argument. You can specify this to be: "parameteric" (for mean) "nonparametric" (for median) robust (for trimmed mean) bayes (for MAP estimator) Just as type argument, abbreviations are also accepted. point.argsA list of additional aesthetic arguments to be passed to the geom_point displaying the raw data. violin.argsA list of additional aesthetic arguments to be passed to the geom_violin. 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. 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). centrality.point.argsA list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms, which are involved in mean plotting. centrality.label.argsA list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms, which are involved in mean plotting. ggsignif.argsA list of additional aesthetic arguments to be passed to ggsignif::geom_signif. 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. typeA character specifying the type of statistical approach. Four possible options: "parametric" "nonparametric" "robust" "bayes" Corresponding abbreviations are also accepted: "p" (for parametric), "np" (for nonparametric), "r" (for robust), or "bf" (for Bayesian). effsize.typeType of effect size needed for parametric tests. The argument can be "eta" (partial eta-squared) or "omega" (partial omega-squared). kNumber of digits after decimal point (should be an integer) (Default: k = 2L). var.equala logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used. conf.levelScalar between 0 and 1. If unspecified, the defaults return 95% confidence/credible intervals (0.95). nbootNumber of bootstrap samples for computing confidence interval for the effect size (Default: 100). trTrim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.

## References

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

ggbetweenstats, ggwithinstats, grouped_ggwithinstats

## Examples

# \donttest{
# to get reproducible results from bootstrapping
set.seed(123)
library(ggstatsplot)

# the most basic function call
grouped_ggbetweenstats(
data = dplyr::filter(ggplot2::mpg, drv != "4"),
x = year,
y = hwy,
grouping.var = drv
)

# modifying individual plots using ggplot.component argument
grouped_ggbetweenstats(
data = dplyr::filter(
movies_long,
genre %in% c("Action", "Comedy"),
mpaa %in% c("R", "PG")
),
x = genre,
y = rating,
grouping.var = mpaa,
results.subtitle = FALSE,
ggplot.component = ggplot2::scale_y_continuous(
breaks = seq(1, 9, 1),
limits = (c(1, 9))
)
)
# }