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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().
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)
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 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.
Line end style (round, butt, square).
Line join style (round, mitre, bevel).
Line mitre limit (number greater than 1).
If FALSE, the default, missing values are removed with
a warning. If TRUE, missing values are silently removed.
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.
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().
Use to override the default connection between
geom_density_2d and stat_density_2d.
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().
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").
Same as stat_contour()
With the addition of:
the density estimate
density estimate, scaled to maximum of 1
geom_contour() for information about how contours
are drawn; geom_bin2d() for another way of dealing with
overplotting.
# 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 = 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)
# }
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