R: Grouped bar charts with statistical tests

grouped_ggbarstats {ggstatsplot}

R Documentation

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` function. `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 `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. `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 are `"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

Examples


# 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)
)