Compute empirical cumulative distribution
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.
stat_ecdf( mapping = NULL, data = NULL, geom = "step", position = "identity", ..., n = NULL, pad = TRUE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
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 |
n |
if NULL, do not interpolate. If not NULL, this is the number of points to interpolate with. |
pad |
If |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
x in data
cumulative density corresponding x
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()
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.