geom_density_2d: Contours of a 2d density estimate

Description

Perform a 2D kernel density estimation using MASS::kde2d() and display the results with contours. This can be useful for dealing with overplotting. This is a 2d version of geom_density().

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,
  adjust = c(1, 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

position

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

...

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

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

linemitre

Line mitre limit (number greater than 1).

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If 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. borders().

geom, stat

Use to override the default connection between geom_density_2d and stat_density_2d.

contour

If TRUE, contour the results of the 2d density estimation

number of grid points in each direction

Bandwidth (vector of length two). If NULL, estimated using MASS::bandwidth.nrd().

adjust

A multiplicative bandwidth adjustment to be used if 'h' is 'NULL'. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator. For example, adjust = 1/2 means use half of the default bandwidth.

Aesthetics

geom_density_2d() understands the following aesthetics (required aesthetics are in bold):

x
y
alpha
colour
group
linetype
size

Learn more about setting these aesthetics in vignette("ggplot2-specs").

Computed variables

Same as stat_contour()

With the addition of:

density: the density estimate
ndensity: density estimate, scaled to maximum of 1

Examples

Run this code

# NOT RUN {
m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
 geom_point() +
 xlim(0.5, 6) +
 ylim(40, 110)
m + geom_density_2d()
# }
# NOT RUN {
m + stat_density_2d(aes(fill = after_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 = after_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 after_stat(nlevel)
d + stat_density_2d(aes(fill = after_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 = after_stat(density)), contour = FALSE)
# Or points:
d + stat_density_2d(geom = "point", aes(size = after_stat(density)), n = 20, contour = FALSE)
# }