ggstatsplot (version 0.13.0)

grouped_ggbarstats: Grouped bar charts with statistical tests

Description

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().

Usage

grouped_ggbarstats(
  data,
  ...,
  grouping.var,
  plotgrid.args = list(),
  annotation.args = list()
)

Arguments

data

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

sample.size.label.args

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

x

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.

y

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().

proportion.test

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.

digits.perc

Numeric that decides number of decimal places for percentage labels (Default: 0L).

label

Character decides what information needs to be displayed on the label in each pie slice. Possible options are "percentage" (default), "counts", "both".

label.args

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

legend.title

Title text for the legend.

bf.message

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

results.subtitle

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.

subtitle

The text for the plot subtitle. Will work only if results.subtitle = FALSE.

caption

The text for the plot caption. This argument is relevant only if bf.message = FALSE.

ggplot.component

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.

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).

ggtheme

A {ggplot2} theme. Default value is 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.). 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.

type

A character specifying the type of statistical approach:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

You can specify just the initial letter.

digits

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()).

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.

paired

Logical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE).

counts

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

ratio

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.

sampling.plan

Character describing the sampling plan. Possible options:

• "indepMulti" (independent multinomial; default)

• "poisson"

• "jointMulti" (joint multinomial)

• "hypergeom" (hypergeometric). For more, see BayesFactor::contingencyTableBF().

fixed.margin

For the independent multinomial sampling plan, which margin is fixed ("rows" or "cols"). Defaults to "rows".

prior.concentration

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.

xlab

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

ylab

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

grouping.var

A single grouping variable.

plotgrid.args

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

annotation.args

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

Details

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

See Also

ggbarstats, ggpiestats, grouped_ggpiestats

Examples

if (FALSE) { # identical(Sys.getenv("NOT_CRAN"), "true")
# for reproducibility
set.seed(123)
library(dplyr, warn.conflicts = FALSE)

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

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