Compute empirical cumulative distribution
The empirical cumulative distribution function (ECDF) provides an alternative
visualisation of distribution. Compared to other visualisations that rely on
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)
- 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
mappingif there is no plot mapping.
- The data to be displayed in this layer. There are three
NULL, the default, the data is inherited from the plot data as specified in the call to
data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See
fortifyfor which variables will be created.
functionwill 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
color = "red"or
size = 3. They may also be parameters to the paired geom/stat.
- if NULL, do not interpolate. If not NULL, this is the number of points to interpolate with.
TRUE, pad the ecdf with additional points (-Inf, 0) and (Inf, 1)
FALSE(the default), removes missing values with a warning. If
TRUEsilently removes missing values.
- logical. Should this layer be included in the legends?
NA, the default, includes if any aesthetics are mapped.
FALSEnever includes, and
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.
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()