geom_point.Rd

% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/geom-point-.r
\name{geom_point}
\alias{geom_point}
\title{Points, as for a scatterplot}
\usage{
geom_point(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", na.rm = FALSE, show_guide = NA, ...)
}
\arguments{
\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 data frame. If specified, overrides the default data frame
defined at the top level of the plot.}

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

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

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

\item{show_guide}{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.}

\item{...}{other arguments passed on to \code{\link{layer}}. There are
  three types of arguments you can use here:

  \itemize{
  \item Aesthetics: to set an aesthetic to a fixed value, like
     \code{color = "red"} or \code{size = 3}.
  \item Other arguments to the layer, for example you override the
    default \code{stat} associated with the layer.
  \item Other arguments passed on to the stat.
  }}
}
\description{
The point geom is used to create scatterplots.
}
\details{
The scatterplot is useful for displaying the relationship between two
continuous variables, although it can also be used with one continuous
and one categorical variable, or two categorical variables.  See
\code{\link{geom_jitter}} for possibilities.

The \emph{bubblechart} is a scatterplot with a third variable mapped to
the size of points.  There are no special names for scatterplots where
another variable is mapped to point shape or colour, however.

The biggest potential problem with a scatterplot is overplotting: whenever
you have more than a few points, points may be plotted on top of one
another. This can severely distort the visual appearance of the plot.
There is no one solution to this problem, but there are some techniques
that can help.  You can add additional information with
\code{\link{stat_smooth}}, \code{\link{stat_quantile}} or
\code{\link{stat_density2d}}.  If you have few unique x values,
\code{\link{geom_boxplot}} may also be useful.  Alternatively, you can
summarise the number of points at each location and display that in some
way, using \code{\link{stat_sum}}. Another technique is to use transparent
points, \code{geom_point(alpha = 0.05)}.
}
\section{Aesthetics}{

\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("geom", "point")}
}
\examples{
\donttest{
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point()

# Add aesthetic mappings
p + geom_point(aes(colour = qsec))
p + geom_point(aes(alpha = qsec))
p + geom_point(aes(colour = factor(cyl)))
p + geom_point(aes(shape = factor(cyl)))
p + geom_point(aes(size = qsec))

# Change scales
p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = "blue")
p + geom_point(aes(size = qsec)) + scale_size_area()
p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE)

# Set aesthetics to fixed value
p + geom_point(colour = "red", size = 3)
ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)

# Varying alpha is useful for large datasets
d <- ggplot(diamonds, aes(carat, price))
d + geom_point(alpha = 1/10)
d + geom_point(alpha = 1/20)
d + geom_point(alpha = 1/100)

# For shapes that have a border (like 21), you can colour the inside and
# outside separately. Use the stroke aesthetic to modify the width of the
# border
ggplot(mtcars, aes(wt, mpg)) +
  geom_point(shape = 21, size = 5, colour = "black", fill = "white", stroke = 5)

# You can create interesting shapes by layering multiple points of
# different sizes
p <- ggplot(mtcars, aes(mpg, wt))
p + geom_point(colour="grey50", size = 4) + geom_point(aes(colour = cyl))
p + aes(shape = factor(cyl)) +
  geom_point(aes(colour = factor(cyl)), size = 4) +
  geom_point(colour="grey90", size = 1.5)
p + geom_point(colour="black", size = 4.5) +
  geom_point(colour="pink", size = 4) +
  geom_point(aes(shape = factor(cyl)))

# These extra layers don't usually appear in the legend, but we can
# force their inclusion
p + geom_point(colour="black", size = 4.5, show_guide = TRUE) +
  geom_point(colour="pink", size = 4, show_guide = TRUE) +
  geom_point(aes(shape = factor(cyl)))

# Transparent points:
ggplot(mtcars, aes(mpg, wt)) + geom_point(size = 5, alpha = 1/5)

# geom_point warns when missing values have been dropped from the data set
# and not plotted, you can turn this off by setting na.rm = TRUE
mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))
ggplot(mtcars2, aes(wt, mpg)) + geom_point()
ggplot(mtcars2, aes(wt, mpg)) + geom_point(na.rm = TRUE)
}
}
\seealso{
\code{\link{scale_size}} to see scale area of points, instead of
  radius, \code{\link{geom_jitter}} to jitter points to reduce (mild)
  overplotting
}