geom_abline.Rd

% Generated by roxygen2 (4.0.0): do not edit by hand
\name{geom_abline}
\alias{geom_abline}
\title{Line specified by slope and intercept.}
\usage{
geom_abline(mapping = NULL, data = NULL, stat = "abline",
  position = "identity", show_guide = FALSE, ...)
}
\arguments{
\item{show_guide}{should a legend be drawn? (defaults to \code{FALSE})}

\item{mapping}{The aesthetic mapping, usually constructed with
\code{\link{aes}} or \code{\link{aes_string}}. Only needs to be set
at the layer level if you are overriding the plot defaults.}

\item{data}{A layer specific dataset - only needed if you want to override
the plot defaults.}

\item{stat}{The statistical transformation to use on the data for this
layer.}

\item{position}{The position adjustment to use for overlapping points
on this layer}

\item{...}{other arguments passed on to \code{\link{layer}}. This can
include aesthetics whose values you want to set, not map. See
\code{\link{layer}} for more details.}
}
\description{
The abline geom adds a line with specified slope and intercept to the
plot.
}
\details{
With its siblings \code{geom_hline} and \code{geom_vline}, it's useful for
annotating plots.  You can supply the parameters for geom_abline,
intercept and slope, in two ways: either explicitly as fixed values, or
in a data frame.  If you specify the fixed values
(\code{geom_abline(intercept=0, slope=1)}) then the line will be the same
in all panels.  If the intercept and slope are stored in the data, then
they can vary from panel to panel.  See the examples for more ideas.
}
\section{Aesthetics}{

\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("geom", "abline")}
}
\examples{
p <- qplot(wt, mpg, data = mtcars)

# Fixed slopes and intercepts
p + geom_abline() # Can't see it - outside the range of the data
p + geom_abline(intercept = 20)

# Calculate slope and intercept of line of best fit
coef(lm(mpg ~ wt, data = mtcars))
p + geom_abline(intercept = 37, slope = -5)
p + geom_abline(intercept = 10, colour = "red", size = 2)

# See ?stat_smooth for fitting smooth models to data
p + stat_smooth(method="lm", se=FALSE)

# Slopes and intercepts as data
p <- ggplot(mtcars, aes(x = wt, y=mpg), . ~ cyl) + geom_point()
df <- data.frame(a=rnorm(10, 25), b=rnorm(10, 0))
p + geom_abline(aes(intercept=a, slope=b), data=df)

# Slopes and intercepts from linear model
library(plyr)
coefs <- ddply(mtcars, .(cyl), function(df) {
  m <- lm(mpg ~ wt, data=df)
  data.frame(a = coef(m)[1], b = coef(m)[2])
})
str(coefs)
p + geom_abline(data=coefs, aes(intercept=a, slope=b))

# It's actually a bit easier to do this with stat_smooth
p + geom_smooth(aes(group=cyl), method="lm")
p + geom_smooth(aes(group=cyl), method="lm", fullrange=TRUE)

# With coordinate transforms
p + geom_abline(intercept = 37, slope = -5) + coord_flip()
p + geom_abline(intercept = 37, slope = -5) + coord_polar()
}
\seealso{
\code{\link{stat_smooth}} to add lines derived from the data,
 \code{\link{geom_hline}} for horizontal lines,
 \code{\link{geom_vline}} for vertical lines
 \code{\link{geom_segment}}
}