A combination of box and violin plots along with raw (unjittered) data points for within-subjects designs with statistical details included in the plot as a subtitle.

## Usage

```
ggwithinstats(
data,
x,
y,
type = "parametric",
pairwise.display = "significant",
p.adjust.method = "holm",
effsize.type = "unbiased",
bf.prior = 0.707,
bf.message = TRUE,
results.subtitle = TRUE,
xlab = NULL,
ylab = NULL,
caption = NULL,
title = NULL,
subtitle = NULL,
digits = 2L,
conf.level = 0.95,
nboot = 100L,
tr = 0.2,
centrality.plotting = TRUE,
centrality.type = type,
centrality.point.args = list(size = 5, color = "darkred"),
centrality.label.args = list(size = 3, nudge_x = 0.4, segment.linetype = 4),
centrality.path = TRUE,
centrality.path.args = list(linewidth = 1, color = "red", alpha = 0.5),
point.args = list(size = 3, alpha = 0.5, na.rm = TRUE),
point.path = TRUE,
point.path.args = list(alpha = 0.5, linetype = "dashed"),
boxplot.args = list(width = 0.2, alpha = 0.5, na.rm = TRUE),
violin.args = list(width = 0.5, alpha = 0.2, na.rm = TRUE),
ggsignif.args = list(textsize = 3, tip_length = 0.01, na.rm = TRUE),
ggtheme = ggstatsplot::theme_ggstatsplot(),
package = "RColorBrewer",
palette = "Dark2",
ggplot.component = NULL,
...
)
```

## 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
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.

- 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. If set to

`"none"`

, no pairwise comparisons will be displayed.- p.adjust.method
Adjustment method for

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

(default),`"hochberg"`

,`"hommel"`

,`"bonferroni"`

,`"BH"`

,`"BY"`

,`"fdr"`

,`"none"`

.- 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.- 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.- 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.The text for the plot caption. This argument is relevant only if

`bf.message = FALSE`

.- title
The text for the plot title.

- subtitle
The text for the plot subtitle. Will work only if

`results.subtitle = FALSE`

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

`100L`

).- 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.- 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.- 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.- 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.- point.args
A list of additional aesthetic arguments to be passed to the

`ggplot2::geom_point()`

displaying the raw data.- 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.- 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()`

.- 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.- 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)`

.- 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.- ...
Currently ignored.

## Details

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

## Summary of graphics

graphical element | `geom` used | argument for further modification |

raw data | `ggplot2::geom_point()` | `point.args` |

point path | `ggplot2::geom_path()` | `point.path.args` |

box plot | `ggplot2::geom_boxplot()` | `boxplot.args` |

density plot | `ggplot2::geom_violin()` | `violin.args` |

centrality measure point | `ggplot2::geom_point()` | `centrality.point.args` |

centrality measure point path | `ggplot2::geom_path()` | `centrality.path.args` |

centrality measure label | `ggrepel::geom_label_repel()` | `centrality.label.args` |

pairwise comparisons | `ggsignif::geom_signif()` | `ggsignif.args` |

## Centrality measures

The table below provides summary about:

statistical test carried out for inferential statistics

type of effect size estimate and a measure of uncertainty for this estimate

functions used internally to compute these details

Type | Measure | Function used |

Parametric | mean | `datawizard::describe_distribution()` |

Non-parametric | median | `datawizard::describe_distribution()` |

Robust | trimmed mean | `datawizard::describe_distribution()` |

Bayesian | MAP | `datawizard::describe_distribution()` |

## Two-sample tests

The table below provides summary about:

statistical test carried out for inferential statistics

type of effect size estimate and a measure of uncertainty for this estimate

functions used internally to compute these details

### between-subjects

**Hypothesis testing**

Type | No. of groups | Test | Function used |

Parametric | 2 | Student's or Welch's t-test | `stats::t.test()` |

Non-parametric | 2 | Mann-Whitney U test | `stats::wilcox.test()` |

Robust | 2 | Yuen's test for trimmed means | `WRS2::yuen()` |

Bayesian | 2 | Student's t-test | `BayesFactor::ttestBF()` |

**Effect size estimation**

Type | No. of groups | Effect size | CI available? | Function used |

Parametric | 2 | Cohen's d, Hedge's g | Yes | `effectsize::cohens_d()` , `effectsize::hedges_g()` |

Non-parametric | 2 | r (rank-biserial correlation) | Yes | `effectsize::rank_biserial()` |

Robust | 2 | Algina-Keselman-Penfield robust standardized difference | Yes | `WRS2::akp.effect()` |

Bayesian | 2 | difference | Yes | `bayestestR::describe_posterior()` |

### within-subjects

**Hypothesis testing**

Type | No. of groups | Test | Function used |

Parametric | 2 | Student's t-test | `stats::t.test()` |

Non-parametric | 2 | Wilcoxon signed-rank test | `stats::wilcox.test()` |

Robust | 2 | Yuen's test on trimmed means for dependent samples | `WRS2::yuend()` |

Bayesian | 2 | Student's t-test | `BayesFactor::ttestBF()` |

**Effect size estimation**

Type | No. of groups | Effect size | CI available? | Function used |

Parametric | 2 | Cohen's d, Hedge's g | Yes | `effectsize::cohens_d()` , `effectsize::hedges_g()` |

Non-parametric | 2 | r (rank-biserial correlation) | Yes | `effectsize::rank_biserial()` |

Robust | 2 | Algina-Keselman-Penfield robust standardized difference | Yes | `WRS2::wmcpAKP()` |

Bayesian | 2 | difference | Yes | `bayestestR::describe_posterior()` |

## One-way ANOVA

The table below provides summary about:

statistical test carried out for inferential statistics

type of effect size estimate and a measure of uncertainty for this estimate

functions used internally to compute these details

### between-subjects

**Hypothesis testing**

Type | No. of groups | Test | Function used |

Parametric | > 2 | Fisher's or Welch's one-way ANOVA | `stats::oneway.test()` |

Non-parametric | > 2 | Kruskal-Wallis one-way ANOVA | `stats::kruskal.test()` |

Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means | `WRS2::t1way()` |

Bayes Factor | > 2 | Fisher's ANOVA | `BayesFactor::anovaBF()` |

**Effect size estimation**

Type | No. of groups | Effect size | CI available? | Function used |

Parametric | > 2 | partial eta-squared, partial omega-squared | Yes | `effectsize::omega_squared()` , `effectsize::eta_squared()` |

Non-parametric | > 2 | rank epsilon squared | Yes | `effectsize::rank_epsilon_squared()` |

Robust | > 2 | Explanatory measure of effect size | Yes | `WRS2::t1way()` |

Bayes Factor | > 2 | Bayesian R-squared | Yes | `performance::r2_bayes()` |

### within-subjects

**Hypothesis testing**

Type | No. of groups | Test | Function used |

Parametric | > 2 | One-way repeated measures ANOVA | `afex::aov_ez()` |

Non-parametric | > 2 | Friedman rank sum test | `stats::friedman.test()` |

Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means | `WRS2::rmanova()` |

Bayes Factor | > 2 | One-way repeated measures ANOVA | `BayesFactor::anovaBF()` |

**Effect size estimation**

Type | No. of groups | Effect size | CI available? | Function used |

Parametric | > 2 | partial eta-squared, partial omega-squared | Yes | `effectsize::omega_squared()` , `effectsize::eta_squared()` |

Non-parametric | > 2 | Kendall's coefficient of concordance | Yes | `effectsize::kendalls_w()` |

Robust | > 2 | Algina-Keselman-Penfield robust standardized difference average | Yes | `WRS2::wmcpAKP()` |

Bayes Factor | > 2 | Bayesian R-squared | Yes | `performance::r2_bayes()` |

## Pairwise comparison tests

The table below provides summary about:

statistical test carried out for inferential statistics

type of effect size estimate and a measure of uncertainty for this estimate

functions used internally to compute these details

### between-subjects

**Hypothesis testing**

Type | Equal variance? | Test | p-value adjustment? | Function used |

Parametric | No | Games-Howell test | Yes | `PMCMRplus::gamesHowellTest()` |

Parametric | Yes | Student's t-test | Yes | `stats::pairwise.t.test()` |

Non-parametric | No | Dunn test | Yes | `PMCMRplus::kwAllPairsDunnTest()` |

Robust | No | Yuen's trimmed means test | Yes | `WRS2::lincon()` |

Bayesian | `NA` | Student's t-test | `NA` | `BayesFactor::ttestBF()` |

**Effect size estimation**

Not supported.

### within-subjects

**Hypothesis testing**

Type | Test | p-value adjustment? | Function used |

Parametric | Student's t-test | Yes | `stats::pairwise.t.test()` |

Non-parametric | Durbin-Conover test | Yes | `PMCMRplus::durbinAllPairsTest()` |

Robust | Yuen's trimmed means test | Yes | `WRS2::rmmcp()` |

Bayesian | Student's t-test | `NA` | `BayesFactor::ttestBF()` |

**Effect size estimation**

Not supported.

## Examples

```
# for reproducibility
set.seed(123)
library(dplyr, warn.conflicts = FALSE)
# create a plot
p <- ggwithinstats(
data = filter(bugs_long, condition %in% c("HDHF", "HDLF")),
x = condition,
y = desire,
type = "np"
)
# looking at the plot
p
# extracting details from statistical tests
extract_stats(p)
#> $subtitle_data
#> # A tibble: 1 × 14
#> parameter1 parameter2 statistic p.value method alternative
#> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 desire condition 1796 0.000430 Wilcoxon signed rank test two.sided
#> effectsize estimate conf.level conf.low conf.high conf.method n.obs
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <int>
#> 1 r (rank biserial) 0.487 0.95 0.285 0.648 normal 90
#> expression
#> <list>
#> 1 <language>
#>
#> $caption_data
#> NULL
#>
#> $pairwise_comparisons_data
#> NULL
#>
#> $descriptive_data
#> NULL
#>
#> $one_sample_data
#> NULL
#>
#> $tidy_data
#> NULL
#>
#> $glance_data
#> NULL
#>
# modifying defaults
ggwithinstats(
data = bugs_long,
x = condition,
y = desire,
type = "robust"
)
# you can remove a specific geom by setting `width` to `0` for that geom
ggbetweenstats(
data = bugs_long,
x = condition,
y = desire,
# to remove violin plot
violin.args = list(width = 0, linewidth = 0),
# to remove boxplot
boxplot.args = list(width = 0),
# to remove points
point.args = list(alpha = 0)
)
```