R: Smoothed density estimates

geom_density {ggplot2}

R Documentation

Smoothed density estimates

Description

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.

Usage

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)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()` or `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 `mapping` if there is no plot mapping.
`data`	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.
`position`	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.
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`show.legend`	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.
`inherit.aes`	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()`.
`geom, stat`	Use to override the default connection between `geom_density` and `stat_density`.
`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 `stats::bw.nrd()`.
`adjust`	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`	Kernel. See list of available kernels in `density()`.
`n`	number of equally spaced points at which the density is to be estimated, should be a power of two, see `density()` for details
`trim`	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.

Aesthetics

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")

Computed variables

density: density estimate
count: density * number of points - useful for stacked density plots
scaled: density estimate, scaled to maximum of 1

Examples

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, 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")

[Package ggplot2 version 3.0.0 Index]