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)

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.


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.


The geometric object to use display the 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 colour = "red" or size = 3. They may also be parameters to the paired geom/stat.


function to use. Must be vectorised.


Optionally, restrict the range of the function to this range.


number of points to interpolate along


list of additional arguments to pass to fun


If 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. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.


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().


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

  • group

  • y

Learn more about setting these aesthetics in vignette("ggplot2-specs").

Computed variables


x's along a grid


value of function evaluated at corresponding x

  • stat_function
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)
# }
Documentation reproduced from package ggplot2, version 3.1.1, License: GPL-2 | file LICENSE

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