% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat_pvalue_manual.R
\name{stat_pvalue_manual}
\alias{stat_pvalue_manual}
\title{Add Manually P-values to a ggplot}
\usage{
stat_pvalue_manual(
  data,
  label = NULL,
  y.position = "y.position",
  xmin = "group1",
  xmax = "group2",
  x = NULL,
  size = 3.88,
  label.size = size,
  bracket.size = 0.3,
  bracket.nudge.y = 0,
  bracket.shorten = 0,
  color = "black",
  linetype = 1,
  tip.length = 0.03,
  remove.bracket = FALSE,
  step.increase = 0,
  step.group.by = NULL,
  hide.ns = FALSE,
  vjust = 0,
  coord.flip = FALSE,
  position = "identity",
  inherit.aes = FALSE,
  ...
)
}
\arguments{
\item{data}{a data frame containing statitistical test results. The expected
default format should contain the following columns: \code{group1 | group2 |
p | y.position | etc}. \code{group1} and \code{group2} are the groups that
have been compared. \code{p} is the resulting p-value. \code{y.position} is
the y coordinates of the p-values in the plot.}

\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.}

\item{y.position}{column containing the coordinates (in data units) to be used
for absolute positioning of the label. Default value is "y.position". Can be
also a numeric vector.}

\item{xmin}{column containing the position of the left sides of the brackets.
Default value is "group1".}

\item{xmax}{(optional) column containing the position of the right sides of
the brackets. Default value is "group2". If NULL, the p-values are plotted
as a simple text.}

\item{x}{x position of the p-value. Should be used only when you want plot the
p-value as text (without brackets).}

\item{size, label.size}{size of label text.}

\item{bracket.size}{Width of the lines of the bracket.}

\item{bracket.nudge.y}{Vertical adjustment to nudge brackets by. Useful to
move up or move down the bracket. If positive value, brackets will be moved
up; if negative value, brackets are moved down.}

\item{bracket.shorten}{a small numeric value in [0-1] for shortening the with
of bracket.}

\item{color}{text and line color. Can be variable name in the data for coloring by groups.}

\item{linetype}{linetype. Can be variable name in the data for changing linetype by groups.}

\item{tip.length}{numeric vector with the fraction of total height that the
bar goes down to indicate the precise column. Default is 0.03.}

\item{remove.bracket}{logical, if \code{TRUE}, brackets are removed from the
plot. Considered only in the situation, where comparisons are performed
against reference group or against "all".}

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

\item{step.group.by}{a variable name for grouping brackets before adding
step.increase. Useful to group bracket by facet panel.}

\item{hide.ns}{can be logical value or a character vector.
\itemize{
\item Case when logical value. If TRUE, hide ns symbol when displaying
 significance levels. Filter is done by checking the column
 \code{p.adj.signif}, \code{p.signif}, \code{p.adj} and \code{p}.
 \item Case when character value. Possible values are "p" or "p.adj", for filtering out non significant.
 }}

\item{vjust}{move the text up or down relative to the bracket. Can be also a
column name available in the data.}

\item{coord.flip}{logical. If \code{TRUE}, flip x and y coordinates so that
horizontal becomes vertical, and vertical, horizontal. When adding the
p-values to a horizontal ggplot (generated using
\code{\link[ggplot2]{coord_flip}()}), you need to specify the option
\code{coord.flip = TRUE}.}

\item{position}{position adjustment, either as a string, or the result of a
call to a position adjustment function.}

\item{inherit.aes}{If \code{FALSE} (the default), 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, e.g. \code{stat_pvalue_manual()}. Most layers should use 
\code{TRUE} (the \pkg{ggplot2} default), but \code{stat_pvalue_manual()} uses 
its own data with different column names than the parent plot data, so 
\code{FALSE} avoids conflicts between parent plot aesthetics (like \code{fill} or 
\code{color}) and the p-value annotation data.}

\item{...}{other arguments passed to the function \code{geom_bracket()} or
\code{geom_text()}}
}
\description{
Add manually p-values to a ggplot, such as box blots, dot plots
 and stripcharts. Frequently asked questions are available on \href{https://www.datanovia.com/en/blog/tag/ggpubr/}{Datanovia ggpubr FAQ page}, for example:
 \itemize{
 \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-basic-ggplots/}{How to Add P-Values onto Basic GGPLOTS}
 \item \href{https://www.datanovia.com/en/blog/ggpubr-how-to-add-adjusted-p-values-to-a-multi-panel-ggplot/}{How to Add Adjusted P-values to a Multi-Panel GGPlot}
 \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-to-ggplot-facets/}{How to Add P-values to GGPLOT Facets}
 \item \href{https://www.datanovia.com/en/blog/ggpubr-how-to-add-p-values-generated-elsewhere-to-a-ggplot/}{How to Add P-Values Generated Elsewhere to a GGPLOT}
 \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-a-grouped-ggplot-using-the-ggpubr-r-package/}{How to Add P-Values onto a Grouped GGPLOT using the GGPUBR R Package}
 \item \href{https://www.datanovia.com/en/blog/how-to-create-stacked-bar-plots-with-error-bars-and-p-values/}{How to Create Stacked Bar Plots with Error Bars and P-values}
 \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-horizontal-ggplots/}{How to Add P-Values onto Horizontal GGPLOTS}
 }
}
\examples{

# T-test
stat.test <- compare_means(
 len ~ dose, data = ToothGrowth,
 method = "t.test"
)
stat.test

# Create a simple box plot
p <- ggboxplot(ToothGrowth, x = "dose", y = "len")
p

# Perform a t-test between groups
stat.test <- compare_means(
 len ~ dose, data = ToothGrowth,
 method = "t.test"
)
stat.test

# Add manually p-values from stat.test data
# First specify the y.position of each comparison
stat.test <- stat.test \%>\%
 mutate(y.position = c(29, 35, 39))
p + stat_pvalue_manual(stat.test, label = "p.adj")

# Customize the label with glue expression
# (https://github.com/tidyverse/glue)
p + stat_pvalue_manual(stat.test, label = "p = {p.adj}")


# Grouped bar plots
#\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# Comparisons against reference
stat.test <- compare_means(
  len ~ dose, data = ToothGrowth, group.by = "supp",
  method = "t.test", ref.group = "0.5"
)
stat.test
# Plot
bp <- ggbarplot(ToothGrowth, x = "supp", y = "len",
                fill = "dose", palette = "jco",
                add = "mean_sd", add.params = list(group = "dose"),
                position = position_dodge(0.8))
bp + stat_pvalue_manual(
  stat.test, x = "supp", y.position = 33,
  label = "p.signif",
  position = position_dodge(0.8)
)

}
\seealso{
\code{\link{stat_compare_means}}
}
