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Grouped histograms for distribution of a numeric variable

Source: R/gghistostats.R
grouped_gghistostats.Rd

Applies gghistostats() across all levels of grouping.var and combines the resulting plots using combine_plots().

Usage

grouped_gghistostats(
  data,
  x,
  grouping.var,
  binwidth = NULL,
  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.

x

A numeric variable from the data frame data.

grouping.var

A single grouping variable. A separate plot is created for each of its levels (in factor level order, or in order of appearance for character variables). Rows with a missing value in this variable are removed.

binwidth

The width of the histogram bins (see gghistostats()). If NULL (default), it is computed once from the non-missing values of x in the full data (not per group), so that all plots share the same bin width.

plotgrid.args

A list of additional arguments passed to patchwork::wrap_plots(), except for guides, which has its own argument.

annotation.args

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

...

Arguments passed on to gghistostats

bin.args

A list of additional aesthetic arguments passed to ggplot2::stat_bin(), which displays the bins. Use the dedicated binwidth argument (not this list) to set the bin width.

centrality.line.args

A list of additional aesthetic arguments passed to ggplot2::geom_vline(), which displays the line for the centrality measure.

type

A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter (e.g. "np" or "bf"). Matching is on the initial lowercase letter only, so any other value (including capitalized values such as "Bayes") falls back to "parametric" without a warning.

test.value

A number indicating the true value of the mean (Default: 0).

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". 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).

tr

Trim 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.

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.

xlab

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

bf.message

Logical that decides whether to display the Bayes Factor in favor of the null hypothesis as a caption. Relevant only for parametric tests (Default: TRUE).

results.subtitle

Logical that decides whether the results of statistical tests are displayed as a subtitle (Default: TRUE). Setting this to FALSE also skips the automatic Bayes Factor caption. Other plot details have their own arguments.

subtitle

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

caption

The text for the plot caption. It will be replaced by the Bayes Factor caption whenever that is displayed (see bf.message).

centrality.plotting

Logical that decides whether central 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

Use centrality.type to choose a different centrality measure.

centrality.type

Decides which centrality measure is displayed. Defaults to the same value as type. Can be one of:

  • "parametric" (for mean)

  • "nonparametric" (for median)

  • "robust" (for trimmed mean)

  • "bayes" (for MAP estimator)

As with type, abbreviations are accepted.

ggplot.component

A {ggplot2} component, or a list of components, to add to the plot (Default: NULL). This is mostly helpful for the grouped_ variants, where it is applied to each individual plot.

ggtheme

A {ggplot2} theme (Default: theme_ggstatsplot()). Any {ggplot2} theme (e.g., ggplot2::theme_bw()) or theme from an extension package (e.g., ggthemes::theme_fivethirtyeight()) can be used. Note that some themes remove plot details. For example, ggthemes::theme_fivethirtyeight() removes the secondary Y-axis, which ggbetweenstats() uses to show details about pairwise comparisons.

Value

A patchwork object combining one plot per level of grouping.var (see combine_plots()). Applying extract_stats() to it returns a list with the statistical details for each of these plots.

Details

Missing values in x are removed before plotting and analysis.

For details, see: https://www.indrapatil.com/ggstatsplot/articles/web_only/gghistostats.html

See also

gghistostats(), ggdotplotstats(), grouped_ggdotplotstats()

Examples

# for reproducibility
set.seed(123)

grouped_gghistostats(
  data            = iris,
  x               = Sepal.Length,
  test.value      = 5,
  grouping.var    = Species,
  plotgrid.args   = list(nrow = 1),
  annotation.args = list(tag_levels = "i")
)