geom_density_2d(mapping = NULL, data = NULL, stat = "density2d", position = "identity", ..., lineend = "butt", linejoin = "round", linemitre = 1, 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)
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
mappingif there is no plot mapping.
NULL, the default, the data is inherited from the plot data as specified in the call to
data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See
fortifyfor which variables will be created. A
functionwill 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.
layer. These are often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"or
size = 3. They may also be parameters to the paired geom/stat.
FALSE(the default), removes missing values with a warning. If
TRUEsilently removes missing values.
NA, the default, includes if any aesthetics are mapped.
FALSEnever includes, and
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.
TRUE, contour the results of the 2d density estimation
NULL, estimated using
Perform a 2D kernel density estimation using kde2d and display the results with contours. This can be useful for dealing with overplotting.
geom_density_2d understands the following aesthetics (required aesthetics are in bold):
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 = ..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))
# If we turn contouring off, we can use use geoms like tiles: d + stat_density_2d(geom = "raster", aes(fill = ..density..), contour = FALSE)
# Or points: d + stat_density_2d(geom = "point", aes(size = ..density..), n = 20, contour = FALSE)