# ggbetweenstats

##### violin plots for group or condition comparisons

Violin plots for between-subjects designs with statistical details included in the plot as a subtitle.

##### Arguments

- data
Dataframe from which variables specified are preferentially to be taken.

- x
The grouping variable.

- y
The response - a vector of length the number of rows of

`x`

.- xlab
Label for

`x`

axis variable.- ylab
Label for

`y`

axis variable.- type
Type of statistic expected (

`"parametric"`

or`"nonparametric"`

or`"robust"`

).Corresponding abbreviations are also accepted:`"p"`

(for parametric),`"np"`

(nonparametric),`"r"`

(robust), resp.- effsize.type
Type of effect size needed for

*parametric*tests (`"biased"`

(Cohen's*d*for**t-test**; partial eta-squared for**anova**) or`"unbiased"`

(Hedge's*g*for**t-test**; partial omega-squared for**anova**)).- title
The text for the plot title.

- caption
The text for the plot caption.

- k
Number of decimal places expected for results.

- var.equal
A logical variable indicating whether to treat the two variances as being equal (Default:

`FALSE`

).- nboot
Number of bootstrap samples for computing effect size (Default:

`100`

).- outlier.color
Default aesthetics for outliers.

- outlier.tagging
Decides whether outliers should be tagged (Default:

`FALSE`

).- outlier.label
Label to put on the outliers that have been tagged.

- outlier.coef
Coefficient for outlier detection using Tukey's method. With Tukey<U+2019>s method, outliers are below (1st Quartile) or above (3rd Quartile)

`outlier.coef`

times the Inter-Quartile Range (IQR) (Default:`1.5`

).- mean.plotting
Decides whether mean is to be highlighted and its value to be displayed (Default:

`TRUE`

).- mean.color
Color for the data point corresponding to mean.

- messages
Decides whether messages references, notes, and warnings are to be displayed (Default:

`TRUE`

).

##### Examples

```
# NOT RUN {
# to get reproducible results from bootstrapping
set.seed(123)
# simple function call with the defaults
ggstatsplot::ggbetweenstats(
data = datasets::iris,
x = Species,
y = Sepal.Length
)
# more detailed function call
ggstatsplot::ggbetweenstats(
data = datasets::ToothGrowth,
x = supp,
y = len,
xlab = "Supplement type",
ylab = "Tooth length",
outlier.tagging = TRUE)
# }
```

*Documentation reproduced from package ggstatsplot, version 0.0.2, License: GPL-3 | file LICENSE*