geom_density_2d: Contours of a 2d density estimate

In TomeTiger/ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics

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,
  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.
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
n	number of grid points in each direction
h	Bandwidth (vector of length two). If NULL, estimated using MASS::bandwidth.nrd().

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

See Also

geom_contour() for information about how contours are drawn; geom_bin2d() for another way of dealing with overplotting.

Examples

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
 geom_point() +
 xlim(0.5, 6) +
 ylim(40, 110)
m + geom_density_2d()

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)

TomeTiger/ggplot2 documentation built on May 29, 2019, 7:53 a.m.