% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat_cor.R
\name{stat_cor}
\alias{stat_cor}
\title{Add Correlation Coefficients with P-values to a Scatter Plot}
\usage{
stat_cor(
  mapping = NULL,
  data = NULL,
  method = "pearson",
  alternative = "two.sided",
  cor.coef.name = c("R", "rho", "tau"),
  label.sep = ", ",
  label.x.npc = "left",
  label.y.npc = "top",
  label.x = NULL,
  label.y = NULL,
  output.type = "expression",
  digits = 2,
  r.digits = digits,
  p.digits = digits,
  r.accuracy = NULL,
  p.accuracy = NULL,
  geom = "text",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)
}
\arguments{
\item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}} or
\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{method}{a character string indicating which correlation coefficient (or
covariance) is to be computed. One of "pearson" (default), "kendall", or
"spearman".}

\item{alternative}{a character string specifying the alternative hypothesis,
must be one of "two.sided" (default), "greater" or "less". You can specify
just the initial letter.}

\item{cor.coef.name}{character. Can be one of \code{"R"} (pearson coef),
\code{"rho"} (spearman coef) and \code{"tau"} (kendall coef). Uppercase and
lowercase are allowed.}

\item{label.sep}{a character string to separate the terms. Default is ", ", to
separate the correlation coefficient and the p.value.}

\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.}

 If too short they will be recycled.}

\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{output.type}{character One of "expression", "latex", "tex" or "text".}

\item{digits, r.digits, p.digits}{integer indicating the number of decimal
places (round) or significant digits (signif) to be used for the correlation
coefficient and the p-value, respectively..}

\item{r.accuracy}{a real value specifying the number of decimal places of
precision for the correlation coefficient. Default is NULL. Use (e.g.) 0.01
to show 2 decimal places of precision. If specified, then \code{r.digits} is
ignored.}

\item{p.accuracy}{a real value specifying the number of decimal places of
precision for the p-value. Default is NULL. Use (e.g.) 0.0001 to show 4
decimal places of precision. If specified, then \code{p.digits} is ignored.}

\item{geom}{The geometric object to use display the data}

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

\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.}

\item{inherit.aes}{If \code{FALSE}, 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{\link[ggplot2:borders]{borders()}}.}

\item{...}{other arguments to pass to \code{\link[ggplot2]{geom_text}} or
\code{\link[ggplot2:geom_text]{geom_label}}.}
}
\description{
Add correlation coefficients with p-values to a scatter plot. Can
 be also used to add `R2`.
}
\section{Computed variables}{
 \describe{ \item{r}{correlation coefficient}
 \item{rr}{correlation coefficient squared} \item{r.label}{formatted label
 for the correlation coefficient} \item{rr.label}{formatted label for the
 squared correlation coefficient} \item{p.label}{label for the p-value}
 \item{label}{default labeldisplayed by \code{stat_cor()}} }
}

\examples{
# Load data
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)

# Scatter plot with correlation coefficient
#:::::::::::::::::::::::::::::::::::::::::::::::::
sp <- ggscatter(df, x = "wt", y = "mpg",
   add = "reg.line",  # Add regressin line
   add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
   conf.int = TRUE # Add confidence interval
   )
# Add correlation coefficient
sp + stat_cor(method = "pearson", label.x = 3, label.y = 30)

# Specify the number of decimal places of precision for p and r
# Using 3 decimal places for the p-value and
# 2 decimal places for the correlation coefficient (r)
sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01)

# Show only the r.label but not the p.label
sp + stat_cor(aes(label = ..r.label..), label.x = 3)

# Use R2 instead of R
ggscatter(df, x = "wt", y = "mpg", add = "reg.line") +
 stat_cor(
   aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")),
  label.x = 3
)

# Color by groups and facet
#::::::::::::::::::::::::::::::::::::::::::::::::::::
sp <- ggscatter(df, x = "wt", y = "mpg",
   color = "cyl", palette = "jco",
   add = "reg.line", conf.int = TRUE)
sp + stat_cor(aes(color = cyl), label.x = 3)

}
\seealso{
\code{\link{ggscatter}}
}
