geom_tile.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom-raster.r, R/geom-rect.r, R/geom-tile.r
\name{geom_raster}
\alias{geom_raster}
\alias{geom_rect}
\alias{geom_tile}
\title{Rectangles}
\usage{
geom_raster(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", ..., hjust = 0.5, vjust = 0.5,
  interpolate = FALSE, na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE)

geom_rect(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", ..., na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE)

geom_tile(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", ..., na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE)
}
\arguments{
\item{mapping}{Set of aesthetic mappings created by \code{\link[=aes]{aes()}} or
\code{\link[=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[=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[=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.}

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

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

\item{...}{Other arguments passed on to \code{\link[=layer]{layer()}}. These are
often aesthetics, used to set an aesthetic to a fixed value, like
\code{colour = "red"} or \code{size = 3}. They may also be parameters
to the paired geom/stat.}

\item{hjust, vjust}{horizontal and vertical justification of the grob.  Each
justification value should be a number between 0 and 1.  Defaults to 0.5
for both, centering each pixel over its data location.}

\item{interpolate}{If \code{TRUE} interpolate linearly, if \code{FALSE}
(the default) don't interpolate.}

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

\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[=borders]{borders()}}.}
}
\description{
\code{geom_rect} and \code{geom_tile} do the same thing, but are
parameterised differently: \code{geom_rect} uses the locations of the four
corners (\code{xmin}, \code{xmax}, \code{ymin} and \code{ymax}), while
\code{geom_tile} uses the center of the tile and its size (\code{x},
\code{y}, \code{width}, \code{height}). \code{geom_raster} is a high
performance special case for when all the tiles are the same size.
}
\section{Aesthetics}{

\code{geom_tile()} understands the following aesthetics (required aesthetics are in bold):
\itemize{
\item \strong{\code{x}}
\item \strong{\code{y}}
\item \code{alpha}
\item \code{colour}
\item \code{fill}
\item \code{group}
\item \code{height}
\item \code{linetype}
\item \code{size}
\item \code{width}
}
Learn more about setting these aesthetics in \code{vignette("ggplot2-specs")}.
}

\examples{
# The most common use for rectangles is to draw a surface. You always want
# to use geom_raster here because it's so much faster, and produces
# smaller output when saving to PDF
ggplot(faithfuld, aes(waiting, eruptions)) +
 geom_raster(aes(fill = density))

# Interpolation smooths the surface & is most helpful when rendering images.
ggplot(faithfuld, aes(waiting, eruptions)) +
 geom_raster(aes(fill = density), interpolate = TRUE)

# If you want to draw arbitrary rectangles, use geom_tile() or geom_rect()
df <- data.frame(
  x = rep(c(2, 5, 7, 9, 12), 2),
  y = rep(c(1, 2), each = 5),
  z = factor(rep(1:5, each = 2)),
  w = rep(diff(c(0, 4, 6, 8, 10, 14)), 2)
)
ggplot(df, aes(x, y)) +
  geom_tile(aes(fill = z), colour = "grey50")
ggplot(df, aes(x, y, width = w)) +
  geom_tile(aes(fill = z), colour = "grey50")
ggplot(df, aes(xmin = x - w / 2, xmax = x + w / 2, ymin = y, ymax = y + 1)) +
  geom_rect(aes(fill = z), colour = "grey50")

\donttest{
# Justification controls where the cells are anchored
df <- expand.grid(x = 0:5, y = 0:5)
df$z <- runif(nrow(df))
# default is compatible with geom_tile()
ggplot(df, aes(x, y, fill = z)) + geom_raster()
# zero padding
ggplot(df, aes(x, y, fill = z)) + geom_raster(hjust = 0, vjust = 0)

# Inspired by the image-density plots of Ken Knoblauch
cars <- ggplot(mtcars, aes(mpg, factor(cyl)))
cars + geom_point()
cars + stat_bin2d(aes(fill = stat(count)), binwidth = c(3,1))
cars + stat_bin2d(aes(fill = stat(density)), binwidth = c(3,1))

cars + stat_density(aes(fill = stat(density)), geom = "raster", position = "identity")
cars + stat_density(aes(fill = stat(count)), geom = "raster", position = "identity")
}
}