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, 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
The data to be displayed in this layer. There are three options:
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Other arguments passed on to
logical. Should this layer be included in the legends?
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.
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
geom_density() understands the following aesthetics (required aesthetics are in bold):
Learn more about setting these aesthetics in
density * number of points - useful for stacked density plots
density estimate, scaled to maximum of 1
scaled, to mirror the syntax of
#> 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, stat(count), fill = cut)) + geom_density(position = "stack")# You can use position="fill" to produce a conditional density estimate ggplot(diamonds, aes(carat, stat(count), fill = cut)) + geom_density(position = "fill")