Description Usage Arguments Details Computed variables See Also Examples
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
1 2 3 4 5 6 7 8 9  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, ...)

mapping 
Set of aesthetic mappings created by 
data 
The data to be displayed in this layer. There are three options: If A A 
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

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 
lineend 
Line end style (round, butt, square) 
contour 
If 
linejoin 
Line join style (round, mitre, bevel) 
linemitre 
Line mitre limit (number greater than 1) 
na.rm 
If 
show.legend 
logical. Should this layer be included in the legends?

inherit.aes 
If 
... 
other arguments passed on to 
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

truncate 
logical flag: if TRUE, data with x values outside the range
specified by range.x are ignored. see 
A sample of the output from geom_bkde2d()
:
Same as stat_contour
geom_contour
for contour drawing geom,
stat_sum
for another way of dealing with overplotting
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  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)

Loading required package: ggplot2
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