Display a smooth density estimate.


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", adjust = 1, kernel = "gaussian", trim = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)


Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), is combined with the default mapping at the top level of the plot. You only need to supply mapping if there isn't a mapping defined for the plot.
A data frame. If specified, overrides the default data frame defined at the top level of the plot.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
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.
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.
other arguments passed on to layer. There are three types of arguments you can use here:
  • Aesthetics: to set an aesthetic to a fixed value, like color = "red" or size = 3.
  • Other arguments to the layer, for example you override the default stat associated with the layer.
  • Other arguments passed on to the stat.
geom, stat
Use to override the default connection between geom_density and stat_density.
see density for details
kernel used for density estimation, 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.


A kernel density estimate, useful for display the distribution of variables with underlying smoothness.


geom_density understands the following aesthetics (required aesthetics are in bold):

  • x
  • y
  • alpha
  • colour
  • fill
  • linetype
  • size
  • weight

Computed variables

density estimate

density * number of points - useful for stacked density plots

density estimate, scaled to maximum of 1


ggplot(diamonds, aes(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)
Warning message: Removed 45 rows containing non-finite values (stat_density).

ggplot(diamonds, aes(depth, fill = cut, colour = cut)) + geom_density(alpha = 0.1) + xlim(55, 70)
Warning message: 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")

See also

See geom_histogram, geom_freqpoly for other methods of displaying continuous distribution. See geom_violin for a compact density display.