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
)
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)
).
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.
if NULL, do not interpolate. If not NULL, this is the number of points to interpolate with.
If TRUE
, pad the ecdf with additional points (-Inf, 0)
and (Inf, 1)
If FALSE
(the default), removes missing values with
a warning. If TRUE
silently removes missing values.
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()
.
cumulative density corresponding x
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.
# NOT RUN {
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()
# }
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