# stat_density_ridges

##### Stat for density ridgeline plots

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.

- Keywords
- datasets

##### Usage

```
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,
...
)
```

##### Arguments

- mapping
Set of aesthetic mappings created by

`aes()`

or`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.- data
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.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.- 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

`FALSE`

, the default, missing values are removed with a warning. If`TRUE`

, missing values are silently removed.- show.legend
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.- inherit.aes
If

`FALSE`

, overrides the default aesthetics, rather than combining with them.- 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

`density()`

. If not provided, these are estimated from the data range and the bandwidth.- jittered_points
If

`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.`position_points_sina()`

or`position_raincloud()`

.- quantile_lines
If

`TRUE`

, enables the drawing of quantile lines. Overrides the`calc_ecdf`

setting and sets it to`TRUE`

.- calc_ecdf
If

`TRUE`

,`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`stat(ecdf)`

and`stat(quantile)`

, respectively.- quantiles
Sets the number of quantiles the data should be broken into. Used if either

`calc_ecdf = TRUE`

or`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.- quantile_fun
Function that calculates quantiles. The function needs to accept two parameters, a vector

`x`

holding the raw data values and a vector`probs`

providing the probabilities that define the quantiles. Default is`quantile`

.- 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

`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.

##### Examples

```
# NOT RUN {
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()
# }
```

*Documentation reproduced from package ggridges, version 0.5.2, License: GPL-2 | file LICENSE*