Smoothed density estimates
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 )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
... |
Other arguments passed on to |
na.rm |
If |
orientation |
The orientation of the layer. The default ( |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
outline.type |
Type of the outline of the area; |
geom, stat |
Use to override the default connection between
|
bw |
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
|
adjust |
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
For example, |
kernel |
Kernel. See list of available kernels in |
n |
number of equally spaced points at which the density is to be
estimated, should be a power of two, see |
trim |
If |
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.
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) # 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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