A combination of box and violin plots along with jittered data points for between-subjects designs with statistical details included in the plot as a subtitle.

ggbetweenstats(
data,
x,
y,
plot.type = "boxviolin",
type = "parametric",
pairwise.comparisons = FALSE,
pairwise.annotation = "p.value",
pairwise.display = "significant",
effsize.type = "unbiased",
partial = TRUE,
effsize.noncentral = TRUE,
bf.prior = 0.707,
bf.message = TRUE,
results.subtitle = TRUE,
xlab = NULL,
ylab = NULL,
caption = NULL,
title = NULL,
subtitle = NULL,
stat.title = NULL,
sample.size.label = TRUE,
k = 2,
var.equal = FALSE,
conf.level = 0.95,
nboot = 100,
tr = 0.1,
sort = "none",
sort.fun = mean,
axes.range.restrict = FALSE,
mean.label.size = 3,
mean.label.fontface = "bold",
mean.label.color = "black",
notch = FALSE,
notchwidth = 0.5,
linetype = "solid",
outlier.tagging = FALSE,
outlier.shape = 19,
outlier.label = NULL,
outlier.label.color = "black",
outlier.color = "black",
outlier.coef = 1.5,
mean.plotting = TRUE,
mean.ci = FALSE,
mean.size = 5,
mean.color = "darkred",
point.jitter.width = NULL,
point.jitter.height = 0,
point.dodge.width = 0.6,
ggtheme = ggplot2::theme_bw(),
ggstatsplot.layer = TRUE,
package = "RColorBrewer",
palette = "Dark2",
direction = 1,
ggplot.component = NULL,
return = "plot",
messages = TRUE
)

## Arguments

data A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted. The grouping variable from the dataframe data. The response (a.k.a. outcome or dependent) variable from the dataframe data. Character describing the type of plot. Currently supported plots are "box" (for pure boxplots), "violin" (for pure violin plots), and "boxviolin" (for a combination of box and violin plots; default). Type of statistic expected ("parametric" or "nonparametric" or "robust" or "bayes").Corresponding abbreviations are also accepted: "p" (for parametric), "np" (nonparametric), "r" (robust), or "bf"resp. Logical that decides whether pairwise comparisons are to be displayed (default: FALSE). Please note that only significant comparisons will be shown by default. To change this behavior, select appropriate option with pairwise.display argument. Character that decides the annotations to use for pairwise comparisons. Either "p.value" (default) or "asterisk". Decides which pairwise comparisons to display. Available options are "significant" (abbreviation accepted: "s") or "non-significant" (abbreviation accepted: "ns") or "everything"/"all". The default is "significant". 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. Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". Type of effect size needed for parametric tests. The argument can be "biased" (equivalent to "d" for Cohen's d for t-test; "partial_eta" for partial eta-squared for anova) or "unbiased" (equivalent to "g" Hedge's g for t-test; "partial_omega" for partial omega-squared for anova)). Logical that decides if partial eta-squared or omega-squared are returned (Default: TRUE). If FALSE, eta-squared or omega-squared will be returned. Valid only for objects of class lm, aov, anova, or aovlist. Logical indicating whether to use non-central t-distributions for computing the confidence interval for Cohen's d or Hedge's g (Default: TRUE). A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors. Logical that decides whether to display Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (Default: TRUE). 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. Labels for x and y axis variables. If NULL (default), variable names for x and y will be used. The text for the plot caption. The text for the plot title. The text for the plot subtitle. Will work only if results.subtitle = FALSE. A character describing the test being run, which will be added as a prefix in the subtitle. The default is NULL. An example of a stat.title argument will be something like "Student's t-test: ". Logical that decides whether sample size information should be displayed for each level of the grouping variable x (Default: TRUE). Number of digits after decimal point (should be an integer) (Default: k = 2). a logical variable indicating whether to treat the variances in the samples as equal. If TRUE, then a simple F test for the equality of means in a one-way analysis of variance is performed. If FALSE, an approximate method of Welch (1951) is used, which generalizes the commonly known 2-sample Welch test to the case of arbitrarily many samples. Scalar between 0 and 1. If unspecified, the defaults return 95% lower and upper confidence intervals (0.95). Number of bootstrap samples for computing confidence interval for the effect size (Default: 100). Trim level for the mean when carrying out robust tests. If you get error stating "Standard error cannot be computed because of Winsorized variance of 0 (e.g., due to ties). Try to decrease the trimming level.", try to play around with the value of tr, which is by default set to 0.1. Lowering the value might help. If "ascending" (default), x-axis variable factor levels will be sorted based on increasing values of y-axis variable. If "descending", the opposite. If "none", no sorting will happen. The function used to sort (default: mean). Logical that decides whether to restrict the axes values ranges to min and max values of the axes variables (Default: FALSE), only relevant for functions where axes variables are of numeric type. Aesthetics for the label displaying mean. Defaults: 3, "bold","black", respectively. A logical. If FALSE (default), a standard box plot will be displayed. If TRUE, a notched box plot will be used. Notches are used to compare groups; if the notches of two boxes do not overlap, this suggests that the medians are significantly different. In a notched box plot, the notches extend 1.58 * IQR / sqrt(n). This gives a roughly 95% confidence interval for comparing medians. IQR: Inter-Quartile Range. For a notched box plot, width of the notch relative to the body (default 0.5). Character strings ("blank", "solid", "dashed", "dotted", "dotdash", "longdash", and "twodash") specifying the type of line to draw box plots (Default: "solid"). Alternatively, the numbers 0 to 6 can be used (0 for "blank", 1 for "solid", etc.). Decides whether outliers should be tagged (Default: FALSE). Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers hidden. Label to put on the outliers that have been tagged. This can't be the same as x argument. Color for the label to to put on the outliers that have been tagged (Default: "black"). Default aesthetics for outliers (Default: "black"). Coefficient for outlier detection using Tukey's method. With Tukey's method, outliers are below (1st Quartile) or above (3rd Quartile) outlier.coef times the Inter-Quartile Range (IQR) (Default: 1.5). Logical that decides whether mean is to be highlighted and its value to be displayed (Default: TRUE). Logical that decides whether 95% confidence interval for mean is to be displayed (Default: FALSE). Point size for the data point corresponding to mean (Default: 5). Color for the data point corresponding to mean (Default: "darkred"). Numeric specifying the degree of jitter in x direction. Defaults to 40% of the resolution of the data. Numeric specifying the degree of jitter in y direction. Defaults to 0.1. Numeric specifying the amount to dodge in the x direction. Defaults to 0.60. A function, ggplot2 theme name. Default value is ggplot2::theme_bw(). Any of the ggplot2 themes, or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). Logical that decides whether theme_ggstatsplot theme elements are to be displayed along with the selected ggtheme (Default: TRUE). theme_ggstatsplot is an opinionated theme layer that override some aspects of the selected ggtheme. Name of package from which the palette is desired as string or symbol. If a character string (e.g., "Set1"), will use that named palette. If a number, will index into the list of palettes of appropriate type. Default palette is "Dark2". Either 1 or -1. If -1 the palette will be reversed. A ggplot component to be added to the plot prepared by ggstatsplot. This argument is primarily helpful for grouped_ variant of the current function. Default is NULL. The argument should be entered as a function. If the given function has an argument axes.range.restrict and if it has been set to TRUE, the added ggplot component might not work as expected. Character that describes what is to be returned: can be "plot" (default) or "subtitle" or "caption". Setting this to "subtitle" will return the expression containing statistical results. If you have set results.subtitle = FALSE, then this will return a NULL. Setting this to "caption" will return the expression containing details about Bayes Factor analysis, but valid only when type = "parametric" and bf.message = TRUE, otherwise this will return a NULL. Decides whether messages references, notes, and warnings are to be displayed (Default: TRUE).

## Details

For parametric tests, Welch's ANOVA/t-test are used as a default (i.e., var.equal = FALSE). References:

• ANOVA: Delacre, Leys, Mora, & Lakens, PsyArXiv, 2018

• t-test: Delacre, Lakens, & Leys, International Review of Social Psychology, 2017

If robust tests are selected, following tests are used is .

• ANOVA: one-way ANOVA on trimmed means (see ?WRS2::t1way)

• t-test: Yuen's test for trimmed means (see ?WRS2::yuen)

For more about how the effect size measures (for nonparametric tests) and their confidence intervals are computed, see ?rcompanion::wilcoxonR.

For repeated measures designs, use ggwithinstats.

## References

https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html

grouped_ggbetweenstats, ggwithinstats, grouped_ggwithinstats

## Examples

# \donttest{
# to get reproducible results from bootstrapping
set.seed(123)
library(ggstatsplot)

# simple function call with the defaults
ggstatsplot::ggbetweenstats(
data = mtcars,
x = am,
y = mpg,
title = "Fuel efficiency by type of car transmission",
caption = "Transmission (0 = automatic, 1 = manual)"
)#> Note: Shapiro-Wilk Normality Test for mpg: p-value = 0.123#> #> Note: Bartlett's test for homogeneity of variances for factor am: p-value = 0.072#>
# more detailed function call
ggstatsplot::ggbetweenstats(
data = datasets::morley,
x = Expt,
y = Speed,
type = "np",
plot.type = "box",
conf.level = 0.99,
xlab = "The experiment number",
ylab = "Speed-of-light measurement",
pairwise.comparisons = TRUE,
pairwise.annotation = "p.value",
outlier.tagging = TRUE,
outlier.label = Run,
nboot = 10,
ggtheme = ggplot2::theme_grey(),
ggstatsplot.layer = FALSE
)#> Warning: extreme order statistics used as endpoints#> Note: 99% CI for effect size estimate was computed with 10 bootstrap samples.
#> #> #> # A tibble: 10 x 5
#>    group1 group2      W p.value significance
#>    <chr>  <chr>   <dbl>   <dbl> <chr>
#>  1 1      2      -3.24    0.369 ns
#>  2 1      3      -3.55    0.295 ns
#>  3 1      4      -4.37    0.145 ns
#>  4 1      5      -4.13    0.145 ns
#>  5 2      3       0.289   1.000 ns
#>  6 2      4      -2.15    0.918 ns
#>  7 2      5      -1.65    0.962 ns
#>  8 3      4      -1.88    0.961 ns
#>  9 3      5      -2.25    0.918 ns
#> 10 4      5       0.673   1.000 ns
#> Note: Shapiro-Wilk Normality Test for Speed-of-light measurement: p-value = 0.514#> #> Note: Bartlett's test for homogeneity of variances for factor The experiment number: p-value = 0.021#> # }