Computes and draws kernel density estimate, which is a smoothed version of the histogram. This is a useful alternative to the histogram for continuous data that comes from an underlying smooth distribution.
geom_density(
mapping = NULL,
data = NULL,
stat = "density",
position = "identity",
...,
na.rm = FALSE,
orientation = NA,
show.legend = NA,
inherit.aes = TRUE,
outline.type = "upper"
)stat_density(
mapping = NULL,
data = NULL,
geom = "area",
position = "stack",
...,
bw = "nrd0",
adjust = 1,
kernel = "gaussian",
n = 512,
trim = FALSE,
na.rm = FALSE,
orientation = NA,
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. A function
can be created
from a formula
(e.g. ~ head(.x, 10)
).
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.
If FALSE
, the default, missing values are removed with
a warning. If TRUE
, missing values are silently removed.
The orientation of the layer. The default (NA
)
automatically determines the orientation from the aesthetic mapping. In the
rare event that this fails it can be given explicitly by setting orientation
to either "x"
or "y"
. See the Orientation section for more detail.
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()
.
Type of the outline of the area; "both"
draws both the
upper and lower lines, "upper"
/"lower"
draws the either lines only.
"full"
draws a closed polygon around the area.
Use to override the default connection between
geom_density
and stat_density
.
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
stats::bw.nrd()
.
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
For example, adjust = 1/2
means use half of the default bandwidth.
Kernel. See list of available kernels in density()
.
number of equally spaced points at which the density is to be
estimated, should be a power of two, see density()
for
details
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.
This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the orientation
parameter, which can be either "x"
or "y"
. The value gives the axis that the geom should run along, "x"
being the default orientation you would expect for the geom.
geom_density()
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
group
linetype
size
weight
Learn more about setting these aesthetics in vignette("ggplot2-specs")
.
density estimate
density * number of points - useful for stacked density plots
density estimate, scaled to maximum of 1
alias for scaled
, to mirror the syntax of
stat_bin()
See geom_histogram()
, geom_freqpoly()
for
other methods of displaying continuous distribution.
See geom_violin()
for a compact density display.
# NOT RUN {
ggplot(diamonds, aes(carat)) +
geom_density()
# Map the values to y to flip the orientation
ggplot(diamonds, aes(y = carat)) +
geom_density()
ggplot(diamonds, aes(carat)) +
geom_density(adjust = 1/5)
ggplot(diamonds, aes(carat)) +
geom_density(adjust = 5)
ggplot(diamonds, aes(depth, colour = cut)) +
geom_density() +
xlim(55, 70)
ggplot(diamonds, aes(depth, fill = cut, colour = cut)) +
geom_density(alpha = 0.1) +
xlim(55, 70)
# }
# NOT RUN {
# 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, after_stat(count), fill = cut)) +
geom_density(position = "stack")
# You can use position="fill" to produce a conditional density estimate
ggplot(diamonds, aes(carat, after_stat(count), fill = cut)) +
geom_density(position = "fill")
# }
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