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Compute empirical cumulative distribution

Source: R/stat-ecdf.R
stat_ecdf.Rd

The empirical cumulative distribution function (ECDF) provides an alternative visualisation of distribution. Compared to other visualisations that rely on density (like geom_histogram()), the ECDF doesn't require any tuning parameters and handles both continuous and categorical variables. The downside is that it requires more training to accurately interpret, and the underlying visual tasks are somewhat more challenging.

Usage

stat_ecdf(
  mapping = NULL,
  data = NULL,
  geom = "step",
  position = "identity",
  ...,
  n = NULL,
  pad = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

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 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. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

...

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.

n

if NULL, do not interpolate. If not NULL, this is the number of points to interpolate with.

pad

If TRUE, pad the ecdf with additional points (-Inf, 0) and (Inf, 1)

na.rm

If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.

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().

Details

The statistic relies on the aesthetics assignment to guess which variable to use as the input and which to use as the output. Either x or y must be provided and one of them must be unused. The ECDF will be calculated on the given aesthetic and will be output on the unused one.

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

  • after_stat(ecdf)
    Cumulative density corresponding to x.

  • after_stat(y)
    [Superseded] For backward compatibility.

Examples

set.seed(1)
df <- data.frame(
  x = c(rnorm(100, 0, 3), rnorm(100, 0, 10)),
  g = gl(2, 100)
)
ggplot(df, aes(x)) +
  stat_ecdf(geom = "step")


# Don't go to positive/negative infinity
ggplot(df, aes(x)) +
  stat_ecdf(geom = "step", pad = FALSE)


# Multiple ECDFs
ggplot(df, aes(x, colour = g)) +
  stat_ecdf()