% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat_kruskal_test.R
\name{stat_kruskal_test}
\alias{stat_kruskal_test}
\title{Add Kruskal-Wallis Test P-values to a GGPlot}
\usage{
stat_kruskal_test(
  mapping = NULL,
  data = NULL,
  group.by = NULL,
  label = "{method}, p = {p.format}",
  label.x.npc = "left",
  label.y.npc = "top",
  label.x = NULL,
  label.y = NULL,
  step.increase = 0.1,
  p.adjust.method = "holm",
  significance = list(),
  p.format.style = "default",
  p.digits = NULL,
  p.leading.zero = NULL,
  p.min.threshold = NULL,
  p.decimal.mark = NULL,
  geom = "text",
  position = "identity",
  na.rm = FALSE,
  show.legend = FALSE,
  inherit.aes = TRUE,
  parse = FALSE,
  ...
)
}
\arguments{
\item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and
\code{inherit.aes = TRUE} (the default), it is combined with the default mapping
at the top level of the plot. You must supply \code{mapping} if there is no plot
mapping.}

\item{data}{The data to be displayed in this layer. There are three
options:

If \code{NULL}, the default, the data is inherited from the plot
data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}.

A \code{data.frame}, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
\code{\link[ggplot2:fortify]{fortify()}} for which variables will be created.

A \code{function} will be called with a single argument,
the plot data. The return value must be a \code{data.frame}, and
will be used as the layer data. A \code{function} can be created
from a \code{formula} (e.g. \code{~ head(.x, 10)}).}

\item{group.by}{(optional) character vector specifying the grouping variable;
it should be used only for grouped plots. Possible values are : \itemize{
\item \code{"x.var"}: Group by the x-axis variable and perform the test
between legend groups. In other words, the p-value is compute between legend
groups at each x position \item \code{"legend.var"}: Group by the legend
variable and perform the test between x-axis groups. In other words, the
test is performed between the x-groups for each legend level. }}

\item{label}{the column containing the label (e.g.: label = "p" or label =
"p.adj"), where \code{p} is the p-value. Can be also an expression that can
be formatted by the \code{\link[glue]{glue}()} package. For example, when
specifying label = "t-test, p = \{p\}", the expression \{p\} will be
replaced by its value. Values inserted through a glue expression are taken
from the raw data column and are \strong{not} rounded by \code{p.digits}; to
round within a glue expression, wrap the value, e.g.
\code{label = "p = \{signif(p, 3)\}"} (or \code{\{format_p_value(p)\}} for the
p-value house style).}

\item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character}
vector of the same length as the number of groups and/or panels. If too
short they will be recycled. \itemize{ \item If \code{numeric}, value should
be between 0 and 1. Coordinates to be used for positioning the label,
expressed in "normalized parent coordinates". \item If \code{character},
allowed values include: i) one of c('right', 'left', 'center', 'centre',
'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre',
'middle') for y-axis.}}

\item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used
for absolute positioning of the label. If too short they will be recycled.}

\item{step.increase}{numeric vector with the increase in fraction of total
height for every additional comparison to minimize overlap.}

\item{p.adjust.method}{method for adjusting p values (see
\code{\link[stats]{p.adjust}}).  Has impact only in a situation, where
multiple pairwise tests are performed; or when there are multiple grouping
variables. Allowed values include "holm", "hochberg", "hommel",
"bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p
value (not recommended), use p.adjust.method = "none".}

\item{significance}{a list of arguments specifying the significance cutpoints
 and symbols. For example, \code{significance <- list(cutpoints = c(0,
 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*",
 "ns"))}.

 In other words, we use the following convention for symbols indicating
 statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item
 \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001
 \item \code{****}:  p <= 0.0001 }

 Note: \code{significance} is kept for backward compatibility. For functions
 that also expose \code{signif.cutoffs} and related parameters,
 \code{significance} takes precedence when provided.}

\item{p.format.style}{character string specifying the p-value formatting style
applied to recognized numeric p-value label columns (see \code{p.digits}).
One of: \code{"default"} (backward compatible, uses scientific notation),
\code{"apa"} (APA style, no leading zero), \code{"nejm"} (NEJM style),
\code{"lancet"} (Lancet style), \code{"ama"} (AMA style), \code{"graphpad"}
(GraphPad style), or \code{"scientific"} (scientific notation for GWAS). See
\code{\link{list_p_format_styles}} for details. Default is \code{"default"},
which leaves the rendered p-value labels unchanged.}

\item{p.digits}{integer indicating the number of digits used to
format recognized \strong{numeric} p-value label columns (\code{label} is one
of \code{"p"}, \code{"p.adj"}, \code{"p.value"}, \code{"p.val"},
\code{"pval"}, \code{"padj"}). Default is 3. Set to \code{NULL} to print the
raw value without rounding (this also disables \code{p.format.style} and the
related formatting arguments). All other labels are left unchanged: other
numeric columns (e.g. \code{"statistic"}, \code{"n"}, effect sizes),
significance symbols (\code{"p.signif"}, \code{"p.adj.signif"}),
already-formatted strings, and \code{\link[glue]{glue}} expressions. A
p-named column whose values fall outside \code{[0, 1]} is also left as-is.
Uses the same formatting engine (\code{\link{format_p_value}()}) as
\code{\link{stat_anova_test}()} for consistency across layers. The style of
this formatting is further controlled by \code{p.format.style},
\code{p.leading.zero}, \code{p.min.threshold} and \code{p.decimal.mark}
(same arguments as \code{\link{stat_compare_means}()}).}

\item{p.leading.zero}{logical indicating whether to include the leading zero
before the decimal point (e.g., "0.05" vs ".05"). If provided, overrides the
style default.}

\item{p.min.threshold}{numeric specifying the minimum p-value to display
exactly. Values below this threshold are shown as "< threshold" (e.g.
\code{p.min.threshold = 0.001} renders very small p-values as "< 0.001"). If
provided, overrides the style default.}

\item{p.decimal.mark}{character string to use as the decimal mark. If
\code{NULL}, uses \code{getOption("OutDec")}.}

\item{geom}{The geometric object to use to display the data for this layer.
When using a \verb{stat_*()} function to construct a layer, the \code{geom} argument
can be used to override the default coupling between stats and geoms. The
\code{geom} argument accepts the following:
\itemize{
\item A \code{Geom} ggproto subclass, for example \code{GeomPoint}.
\item A string naming the geom. To give the geom as a string, strip the
function name of the \code{geom_} prefix. For example, to use \code{geom_point()},
give the geom as \code{"point"}.
\item For more information and other ways to specify the geom, see the
\link[ggplot2:layer_geoms]{layer geom} documentation.
}}

\item{position}{A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The \code{position} argument accepts the following:
\itemize{
\item The result of calling a position function, such as \code{position_jitter()}.
This method allows for passing extra arguments to the position.
\item A string naming the position adjustment. To give the position as a
string, strip the function name of the \code{position_} prefix. For example,
to use \code{position_jitter()}, give the position as \code{"jitter"}.
\item For more information and other ways to specify the position, see the
\link[ggplot2:layer_positions]{layer position} documentation.
}}

\item{na.rm}{If FALSE (the default), removes missing values with a warning. If
TRUE silently removes missing values.}

\item{show.legend}{logical. Should this layer be included in the legends?
\code{NA}, the default, includes if any aesthetics are mapped.
\code{FALSE} never includes, and \code{TRUE} always includes.
It can also be a named logical vector to finely select the aesthetics to
display. To include legend keys for all levels, even
when no data exists, use \code{TRUE}.  If \code{NA}, all levels are shown in legend,
but unobserved levels are omitted.}

\item{inherit.aes}{If \code{FALSE} (the default for most ggpubr functions),
overrides the default aesthetics, rather than combining with them. This is
most useful for helper functions that define both data and aesthetics and
shouldn't inherit behaviour from the default plot specification. Set to
\code{TRUE} to inherit aesthetics from the parent ggplot layer.}

\item{parse}{If TRUE, the labels will be parsed into expressions and displayed
as described in \code{?plotmath}.}

\item{...}{other arguments passed to the function \code{geom_bracket()} or
\code{geom_text()}}
}
\description{
Add Kruskal-Wallis test p-values to a ggplot, such as
 box plots, dot plots and stripcharts.
}
\section{Computed variables}{
 \itemize{
 \item{statistic}:	the Kruskal-Wallis rank sum chi-squared statistic used to compute the p-value.
 \item{p}:	p-value.
 \item{p.adj}: Adjusted p-values.
 \item{p.signif}: P-value significance.
 \item{p.adj.signif}: Adjusted p-value significance.
 \item{p.format}: Formatted p-value.
 \item{p.format.signif}: Formatted p-value with significance symbols.
 \item{p.adj.format}: Formatted adjusted p-value.
 \item{n}: number of samples.
  }
}

\examples{
# Data preparation
# \%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%
# Transform `dose` into factor variable
df <- ToothGrowth
df$dose <- as.factor(df$dose)
# Add a random grouping variable
set.seed(123)
df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20)))
df$len <- ifelse(df$group == "grp2", df$len + 2, df$len)
df$len <- ifelse(df$group == "grp3", df$len + 7, df$len)
head(df, 3)


# Basic boxplot
# \%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%
# Create a basic boxplot
# Add 5\% and 10\% space to the plot bottom and the top, respectively
bxp <- ggboxplot(df, x = "dose", y = "len") +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))

# Add the p-value to the boxplot
bxp + stat_kruskal_test()

# Change the label position
# Using coordinates in data units
bxp + stat_kruskal_test(label.x = "1", label.y = 10, hjust = 0)

# Format the p-value differently
custom_p_format <- function(p) {
  rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE)
}
bxp + stat_kruskal_test(
  label = "Kruskal-Wallis, italic(p) = {custom_p_format(p)}{p.signif}"
)

# Show a detailed label in italic
bxp + stat_kruskal_test(label = "as_detailed_italic")


# Faceted plots
# \%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%
# Create a ggplot facet
bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))
# Add p-values
bxp + stat_kruskal_test()


# Grouped plots
# \%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%
bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg")

# For each x-position, computes tests between legend groups
bxp2 + stat_kruskal_test(aes(group = dose), label = "p = {p.format}{p.signif}")

#  For each legend group, computes tests between x variable groups
bxp2 + stat_kruskal_test(aes(group = dose, color = dose), group.by = "legend.var")

}
