# Grouped histograms for distribution of a numeric variable

Source:`R/gghistostats.R`

`grouped_gghistostats.Rd`

Helper function for `ggstatsplot::gghistostats`

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

.

## 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.

- binwidth
The width of the histogram bins. Can be specified as a numeric value, or a function that calculates width from

`x`

. The default is to use the`max(x) - min(x) / sqrt(N)`

. You should always check this value and explore multiple widths to find the best to illustrate the stories in your data.- 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()`

.- ...
Arguments passed on to

`gghistostats`

`normal.curve`

A logical value that decides whether to super-impose a normal curve using

`stats::dnorm(mean(x), sd(x))`

. Default is`FALSE`

.`normal.curve.args`

A list of additional aesthetic arguments to be passed to the normal curve.

`bin.args`

A list of additional aesthetic arguments to be passed to the

`stat_bin`

used to display the bins. Do not specify`binwidth`

argument in this list since it has already been specified using the dedicated argument.`centrality.line.args`

A list of additional aesthetic arguments to be passed to the

`geom_line`

used to display the lines corresponding to the centrality parameter.`type`

A character specifying the type of statistical approach:

`"parametric"`

`"nonparametric"`

`"robust"`

`"bayes"`

You can specify just the initial letter.

`test.value`

A number indicating the true value of the mean (Default:

`0`

).`k`

Number of digits after decimal point (should be an integer) (Default:

`k = 2L`

).`conf.level`

Scalar between

`0`

and`1`

(default:`95%`

confidence/credible intervals,`0.95`

). If`NULL`

, no confidence intervals will be computed.`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.`effsize.type`

Type of effect size needed for

*parametric*tests. The argument can be`"d"`

(for Cohen's*d*) or`"g"`

(for Hedge's*g*).`xlab`

Label for

`x`

axis variable. If`NULL`

(default), variable name for`x`

will be used.`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`

.`centrality.plotting`

Logical 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.type`

Decides 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.`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.`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.

## Details

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