stat_density_ridges | R Documentation |
This stat is the default stat used by geom_density_ridges
. It is very similar to stat_density
,
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, ... )
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 to display the data. |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
bandwidth |
Bandwidth used for density calculation. If not provided, is estimated from the data. |
from, to |
The left and right-most points of the grid at which the density is to be estimated,
as in |
jittered_points |
If |
quantile_lines |
If |
calc_ecdf |
If |
quantiles |
Sets the number of quantiles the data should be broken into. Used if either |
quantile_fun |
Function that calculates quantiles. The function needs to accept two parameters,
a vector |
n |
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 |
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() 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() 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()
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