% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom-density2d.r, R/stat-density-2d.r
\name{geom_density_2d}
\alias{geom_density_2d}
\alias{geom_density2d}
\alias{stat_density_2d}
\alias{stat_density2d}
\title{Contours of a 2d density estimate}
\usage{
geom_density_2d(mapping = NULL, data = NULL, stat = "density2d",
  position = "identity", ..., lineend = "butt", linejoin = "round",
  linemitre = 10, na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE)

stat_density_2d(mapping = NULL, data = NULL, geom = "density_2d",
  position = "identity", ..., contour = TRUE, n = 100, h = NULL,
  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. A \code{function} can be created
from a \code{formula} (e.g. \code{~ head(.x, 10)}).}

\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{lineend}{Line end style (round, butt, square).}

\item{linejoin}{Line join style (round, mitre, bevel).}

\item{linemitre}{Line mitre limit (number greater than 1).}

\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()}}.}

\item{geom, stat}{Use to override the default connection between
\code{geom_density_2d} and \code{stat_density_2d}.}

\item{contour}{If \code{TRUE}, contour the results of the 2d density
estimation}

\item{n}{number of grid points in each direction}

\item{h}{Bandwidth (vector of length two). If \code{NULL}, estimated
using \code{\link[MASS:bandwidth.nrd]{MASS::bandwidth.nrd()}}.}
}
\description{
Perform a 2D kernel density estimation using \code{\link[MASS:kde2d]{MASS::kde2d()}} and
display the results with contours. This can be useful for dealing with
overplotting. This is a 2d version of \code{\link[=geom_density]{geom_density()}}.
}
\section{Aesthetics}{

\code{geom_density_2d()} 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{group}
\item \code{linetype}
\item \code{size}
}
Learn more about setting these aesthetics in \code{vignette("ggplot2-specs")}.
}

\section{Computed variables}{

Same as \code{\link[=stat_contour]{stat_contour()}}

With the addition of:
\describe{
\item{density}{the density estimate}
\item{ndensity}{density estimate, scaled to maximum of 1}
}
}

\examples{
m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
 geom_point() +
 xlim(0.5, 6) +
 ylim(40, 110)
m + geom_density_2d()
\donttest{
m + stat_density_2d(aes(fill = stat(level)), geom = "polygon")

set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
d <- ggplot(dsmall, aes(x, y))
# If you map an aesthetic to a categorical variable, you will get a
# set of contours for each value of that variable
d + geom_density_2d(aes(colour = cut))

# Similarly, if you apply faceting to the plot, contours will be
# drawn for each facet, but the levels will calculated across all facets
d + stat_density_2d(aes(fill = stat(level)), geom = "polygon") +
  facet_grid(. ~ cut) + scale_fill_viridis_c()
# To override this behavior (for instace, to better visualize the density
# within each facet), use stat(nlevel)
d + stat_density_2d(aes(fill = stat(nlevel)), geom = "polygon") +
  facet_grid(. ~ cut) + scale_fill_viridis_c()

# If we turn contouring off, we can use use geoms like tiles:
d + stat_density_2d(geom = "raster", aes(fill = stat(density)), contour = FALSE)
# Or points:
d + stat_density_2d(geom = "point", aes(size = stat(density)), n = 20, contour = FALSE)
}
}
\seealso{
\code{\link[=geom_contour]{geom_contour()}} for information about how contours
are drawn; \code{\link[=geom_bin2d]{geom_bin2d()}} for another way of dealing with
overplotting.
}
