This stat is the default stat used by
geom_density_ridges. It is very similar to
however there are a few differences. Most importantly, the density bandwidth is chosen across
the entire dataset.
stat_density_ridges( mapping = NULL, data = NULL, geom = "density_ridges", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, bandwidth = NULL, from = NULL, to = NULL, jittered_points = FALSE, quantile_lines = FALSE, calc_ecdf = FALSE, quantiles = 4, quantile_fun = quantile, n = 512, ... )
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.
The data to be displayed in this layer. There are three options:
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
function will be called with a single argument,
the plot data. The return value must be a
will be used as the layer data.
The geometric object to use to display the data.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
FALSE, the default, missing values are removed with
a warning. If
TRUE, missing values are silently removed.
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.
FALSE, overrides the default aesthetics,
rather than combining with them.
Bandwidth used for density calculation. If not provided, is estimated from the data.
The left and right-most points of the grid at which the density is to be estimated,
density(). If not provided, these are estimated from the data range and the bandwidth.
TRUE, carries the original point data over to the processed data frame,
so that individual points can be drawn by the various ridgeline geoms. The specific position of these
points is controlled by various position objects, e.g.
TRUE, enables the drawing of quantile lines. Overrides the
and sets it to
stat_density_ridges calculates an empirical cumulative distribution function (ecdf)
and returns a variable
ecdf and a variable
quantile. Both can be mapped onto aesthetics via
Sets the number of quantiles the data should be broken into. Used if either
calc_ecdf = TRUE
quantile_lines = TRUE. If
quantiles is an integer then the data will be cut into that many equal quantiles.
If it is a vector of probabilities then the data will cut by them.
Function that calculates quantiles. The function needs to accept two parameters,
x holding the raw data values and a vector
probs providing the probabilities that
define the quantiles. Default is
The number of equally spaced points at which the density is to be estimated. Should be a power of 2. Default is 512.
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.
library(ggplot2) # Examples of coloring by ecdf or quantiles ggplot(iris, aes(x = Sepal.Length, y = Species, fill = factor(stat(quantile)))) + stat_density_ridges( geom = "density_ridges_gradient", calc_ecdf = TRUE, quantiles = 5 ) + scale_fill_viridis_d(name = "Quintiles") + theme_ridges() #> Picking joint bandwidth of 0.181 ggplot(iris, aes( x = Sepal.Length, y = Species, fill = 0.5 - abs(0.5-stat(ecdf)) )) + stat_density_ridges(geom = "density_ridges_gradient", calc_ecdf = TRUE) + scale_fill_viridis_c(name = "Tail probability", direction = -1) + theme_ridges() #> Picking joint bandwidth of 0.181 ggplot(iris, aes( x = Sepal.Length, y = Species, fill = factor(stat(quantile)) )) + stat_density_ridges( geom = "density_ridges_gradient", calc_ecdf = TRUE, quantiles = c(0.025, 0.975) ) + scale_fill_manual( name = "Probability", values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"), labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]") ) + theme_ridges() #> Picking joint bandwidth of 0.181