Compute empirical weighted cumulative distribution

Version of ggplot2::stat_ecdf that adds a weights property for each observation, to produce an empirical weighted cumulative distribution function. 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 discrete variables. The downside is that it requires more training to accurately interpret, and the underlying visual tasks are somewhat more challenging.

Usage

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

Arguments

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.

Computed variables

x

x in data

y

cumulative density corresponding x

See also

wquantile

Examples

library(ggplot2)

n <- 100
df <- data.frame(
  x = c(rnorm(n, 0, 10), rnorm(n, 0, 10)),
  g = gl(2, n),
  w = c(rep(1/n, n), sort(runif(n))^sqrt(n))
)
ggplot(df, aes(x, weights = w)) + stat_ewcdf(geom = "step")


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


# Multiple ECDFs
ggplot(df, aes(x, colour = g, weights = w)) + stat_ewcdf()

ggplot(df, aes(x, colour = g, weights = w)) +
  stat_ewcdf() +
  facet_wrap(vars(g), ncol = 1)