Contours from a 2d density estimate.

Perform a 2D kernel density estimation using bkde2D and display the results with contours. This can be useful for dealing with overplotting

geom_bkde2d(mapping = NULL, data = NULL, stat = "bkde2d",
  position = "identity", bandwidth = NULL, range.x = NULL,
  lineend = "butt", contour = TRUE, linejoin = "round",
  linemitre = 1, na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, ...)

stat_bkde2d(mapping = NULL, data = NULL, geom = "density2d",
  position = "identity", contour = TRUE, bandwidth = NULL,
  grid_size = c(51, 51), range.x = NULL, truncate = TRUE,
  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.
stat	The statistical transformation to use on the data for this layer, as a string.
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
bandwidth	the kernel bandwidth smoothing parameter. see bkde2D for details. If NULL, it will be computed for you but will most likely not yield optimal results. see bkde2D for details
range.x	a list containing two vectors, where each vector contains the minimum and maximum values of x at which to compute the estimate for each direction. see bkde2D for details
lineend	Line end style (round, butt, square).
contour	If TRUE, contour the results of the 2d density estimation
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().
...	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.
geom	default geom to use with this stat
grid_size	vector containing the number of equally spaced points in each direction over which the density is to be estimated. see bkde2D for details
truncate	logical flag: if TRUE, data with x values outside the range specified by range.x are ignored. see bkde2D for details

Details

A sample of the output from geom_bkde2d():

Computed variables

Same as stat_contour

See also

geom_contour for contour drawing geom, stat_sum for another way of dealing with overplotting

Examples

# NOT RUN {
m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
       geom_point() +
       xlim(0.5, 6) +
       ylim(40, 110)

m + geom_bkde2d(bandwidth=c(0.5, 4))

m + stat_bkde2d(bandwidth=c(0.5, 4), aes(fill = ..level..), geom = "polygon")

# If you map an aesthetic to a categorical variable, you will get a
# set of contours for each value of that variable
set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
d <- ggplot(dsmall, aes(x, y)) +
       geom_bkde2d(bandwidth=c(0.5, 0.5), aes(colour = cut))
d

# If we turn contouring off, we can use use geoms like tiles:
d + stat_bkde2d(bandwidth=c(0.5, 0.5), geom = "raster",
                aes(fill = ..density..), contour = FALSE)

# Or points:
d + stat_bkde2d(bandwidth=c(0.5, 0.5), geom = "point",
                aes(size = ..density..),  contour = FALSE)
# }