stat_density_ridges.Rd
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 rightmost 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(0.5stat(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()#>