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.

ggwithinstats(
data,
x,
y,
type = "parametric",
pairwise.comparisons = FALSE,
pairwise.display = "significant",
effsize.type = "unbiased",
partial = TRUE,
bf.prior = 0.707,
bf.message = TRUE,
sphericity.correction = TRUE,
results.subtitle = TRUE,
xlab = NULL,
ylab = NULL,
caption = NULL,
title = NULL,
subtitle = NULL,
sample.size.label = TRUE,
k = 2,
conf.level = 0.95,
nboot = 100,
tr = 0.1,
mean.plotting = TRUE,
mean.ci = FALSE,
mean.point.args = list(size = 5, color = "darkred"),
mean.label.args = list(size = 3),
point.path = TRUE,
point.path.args = list(alpha = 0.5, linetype = "dashed"),
mean.path = TRUE,
mean.path.args = list(color = "red", size = 1, alpha = 0.5),
notch = FALSE,
notchwidth = 0.5,
outlier.tagging = FALSE,
outlier.label = NULL,
outlier.coef = 1.5,
outlier.label.args = list(),
outlier.point.args = list(),
violin.args = list(width = 0.5, alpha = 0.2),
ggtheme = ggplot2::theme_bw(),
ggstatsplot.layer = TRUE,
package = "RColorBrewer",
palette = "Dark2",
ggplot.component = NULL,
output = "plot",
...
)

## 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. 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. The pairwise comparison dataframes are prepared using the pairwiseComparisons::pairwise_comparisons function. For more details about pairwise comparisons, see the documentation for that function. 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)). If TRUE, return partial indices. 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). Logical that decides whether to apply correction to account for violation of sphericity in a repeated measures design ANOVA (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. 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. 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). 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. 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). A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved mean value plotting. A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved mean value plotting. Logical that decides whether individual data points and means, respectively, should be connected using 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. A list of additional aesthetic arguments passed on to geom_path connecting raw data points and mean points. 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). Decides whether outliers should be tagged (Default: FALSE). Label to put on the outliers that have been tagged. This can't be the same as x argument. 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). A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved outlier value plotting. A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved outlier value plotting. A list of additional aesthetic arguments to be passed to the geom_violin. 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. Name of palette as string or symbol. 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. 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. For functions ggpiestats and ggbarstats, setting output = "proptest" will return a dataframe containing results from proportion tests. Currently ignored.

## Details

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

For independent measures designs, use ggbetweenstats.

grouped_ggbetweenstats, ggbetweenstats, grouped_ggwithinstats

## Examples

# \donttest{
# setup
set.seed(123)
library(ggstatsplot)

# two groups (*t*-test)
ggstatsplot::ggwithinstats(
data = VR_dilemma,
x = modality,
y = score,
xlab = "Presentation modality",
ylab = "Proportion of utilitarian decisions"
)
# more than two groups (anova)
library(WRS2)

ggstatsplot::ggwithinstats(
data = as_tibble(WineTasting),
x = Wine,
y = Taste,
type = "np",
conf.level = 0.99,
pairwise.comparisons = TRUE,
outlier.tagging = TRUE,
outlier.label = Taster
)#> Warning: extreme order statistics used as endpoints# }