Computes and draws kernel density estimate, which is a smoothed version of the histogram. This is a useful alternative to the histogram if for continuous data that comes from an underlying smooth distribution.
geom_density(mapping = NULL, data = NULL, stat = "density", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) stat_density(mapping = NULL, data = NULL, geom = "area", position = "stack", ..., bw = "nrd0", adjust = 1, kernel = "gaussian", n = 512, trim = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
Set of aesthetic mappings created by
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.
The data to be displayed in this layer. There are three options:
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
fortify for which variables will be created.
function will be called with a single argument,
the plot data. The return value must be a
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
color = "red" or
size = 3. They may also be parameters
to the paired geom/stat.
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.
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.
Use to override the default connection between
The smoothing bandwidth to be used.
If numeric, the standard deviation of the smoothing kernel.
If character, a rule to choose the bandwidth, as listed in
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
adjust = 1/2 means use half of the default bandwidth.
Kernel. See list of available kernels in
number of equally spaced points at which the density is to be
estimated, should be a power of two, see
This parameter only matters if you are displaying multiple
densities in one plot. If
FALSE, the default, each density is
computed on the full range of the data. If
TRUE, each density
is computed over the range of that group: this typically means the
estimated x values will not line-up, and hence you won't be able to
stack density values.
geom_density understands the following aesthetics (required aesthetics are in bold):
density * number of points - useful for stacked density plots
density estimate, scaled to maximum of 1
#> Warning: Removed 45 rows containing non-finite values (stat_density).#> Warning: Removed 45 rows containing non-finite values (stat_density).# Stacked density plots: if you want to create a stacked density plot, you # probably want to 'count' (density * n) variable instead of the default # density # Loses marginal densities ggplot(diamonds, aes(carat, fill = cut)) + geom_density(position = "stack")# Preserves marginal densities ggplot(diamonds, aes(carat, ..count.., fill = cut)) + geom_density(position = "stack")# You can use position="fill" to produce a conditional density estimate ggplot(diamonds, aes(carat, ..count.., fill = cut)) + geom_density(position = "fill")