# Violin plots for group or condition comparisons in within-subjects designs repeated across all levels of a grouping variable.

Source:`R/ggwithinstats.R`

`grouped_ggwithinstats.Rd`

A combined plot of comparison plot created for levels of a grouping variable.

## 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

`ggwithinstats`

`point.path,centrality.path`

Logical that decides whether individual data points and means, respectively, should be connected using

`ggplot2::geom_path()`

. Both default to`TRUE`

. Note that`point.path`

argument is relevant only when there are two groups (i.e., in case of a*t*-test). In case of large number of data points, it is advisable to set`point.path = FALSE`

as these lines can overwhelm the plot.`centrality.path.args,point.path.args`

A list of additional aesthetic arguments passed on to

`ggplot2::geom_path()`

connecting raw data points and mean points.`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.`pairwise.comparisons`

Logical 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`pairwise_comparisons`

function. For more details about pairwise comparisons, see the documentation for that function.`p.adjust.method`

Adjustment method for

*p*-values for multiple comparisons. Possible methods are:`"holm"`

(default),`"hochberg"`

,`"hommel"`

,`"bonferroni"`

,`"BH"`

,`"BY"`

,`"fdr"`

,`"none"`

.`pairwise.display`

Decides

*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.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.`point.args`

A list of additional aesthetic arguments to be passed to the

`ggplot2::geom_point()`

displaying the raw data.`boxplot.args`

A list of additional aesthetic arguments passed on to

`ggplot2::geom_boxplot()`

.`violin.args`

A list of additional aesthetic arguments to be passed to the

`ggplot2::geom_violin()`

.`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)`

.`centrality.point.args,centrality.label.args`

A 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.args`

A list of additional aesthetic arguments to be passed to

`ggsignif::geom_signif`

.`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.`x`

The grouping (or independent) variable from

`data`

. In case of a repeated measures or within-subjects design, if`subject.id`

argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is**not**sorted, the results*can*be inaccurate when there are more than two levels in`x`

and there are`NA`

s present. The data is expected to be sorted by user in subject-1,subject-2, ..., pattern.`y`

The response (or outcome or dependent) variable from

`data`

.`type`

A character specifying the type of statistical approach:

`"parametric"`

`"nonparametric"`

`"robust"`

`"bayes"`

You can specify just the initial letter.

`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.`effsize.type`

Type of effect size needed for

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

(partial eta-squared) or`"omega"`

(partial omega-squared).`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.`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.`nboot`

Number of bootstrap samples for computing confidence interval for the effect size (Default:

`100L`

).

- 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()`

.

## Examples

```
# for reproducibility
set.seed(123)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
# the most basic function call
grouped_ggwithinstats(
data = filter(bugs_long, condition %in% c("HDHF", "HDLF")),
x = condition,
y = desire,
grouping.var = gender,
type = "np",
# additional modifications for **each** plot using `{ggplot2}` functions
ggplot.component = scale_y_continuous(breaks = seq(0, 10, 1), limits = c(0, 10))
)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
```