Compute function for each x value

This stat makes it easy to superimpose a function on top of an existing plot. The function is called with a grid of evenly spaced values along the x axis, and the results are drawn (by default) with a line.

stat_function(mapping = NULL, data = NULL, geom = "path",
  position = "identity", ..., fun, xlim = NULL, n = 101, args = list(),
  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.
geom	The geometric object to use display the 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.
fun	function to use. Must be vectorised.
xlim	Optionally, restrict the range of the function to this range.
n	number of points to interpolate along
args	list of additional arguments to pass to fun
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.
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.

Aesthetics

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

• group

• y

Computed variables

x

x's along a grid

y

value of function evaluated at corresponding x

Examples

set.seed(1492)
df <- data.frame(
  x = rnorm(100)
)
x <- df$x
base <- ggplot(df, aes(x)) + geom_density()
base + stat_function(fun = dnorm, colour = "red")
base + stat_function(fun = dnorm, colour = "red", args = list(mean = 3))

# Plot functions without data
# Examples adapted from Kohske Takahashi

# Specify range of x-axis
ggplot(data.frame(x = c(0, 2)), aes(x)) +
  stat_function(fun = exp, geom = "line")

# Plot a normal curve
ggplot(data.frame(x = c(-5, 5)), aes(x)) + stat_function(fun = dnorm)

# To specify a different mean or sd, use the args parameter to supply new values
ggplot(data.frame(x = c(-5, 5)), aes(x)) +
  stat_function(fun = dnorm, args = list(mean = 2, sd = .5))

# Two functions on the same plot
f <- ggplot(data.frame(x = c(0, 10)), aes(x))
f + stat_function(fun = sin, colour = "red") +
  stat_function(fun = cos, colour = "blue")

# Using a custom function
test <- function(x) {x ^ 2 + x + 20}
f + stat_function(fun = test)