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 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
mappingif there is no plot mapping.
- The data to be displayed in this layer. There are three
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
fortifyfor which variables will be created.
functionwill 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.
- 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.
FALSEnever includes, and
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.
- geom, stat
- 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 = 1/2means 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.
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) 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, ..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")