Create ridgeline plot — geom_density

geom_density_ridges arranges multiple density plots in a staggered fashion, as in the cover of the famous Joy Division album Unknown Pleasures.

geom_density_ridges(
  mapping = NULL,
  data = NULL,
  stat = "density_ridges",
  position = "points_sina",
  panel_scaling = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_density_ridges2(
  mapping = NULL,
  data = NULL,
  stat = "density_ridges",
  position = "points_sina",
  panel_scaling = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

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.
stat	The statistical transformation to use on the data for this layer, as a string.
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
panel_scaling	If `TRUE`, the default, relative scaling is calculated separately for each panel. If `FALSE`, relative scaling is calculated globally.
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.
...	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.

Details

By default, this geom calculates densities from the point data mapped onto the x axis. If density calculation is not wanted, use stat="identity" or use geom_ridgeline. The difference between geom_density_ridges and geom_ridgeline is that geom_density_ridges will provide automatic scaling of the ridgelines (controlled by the scale aesthetic), whereas geom_ridgeline will plot the data as is. Note that when you set stat="identity", the height aesthetic must be provided.

Note that the default stat_density_ridges makes joint density estimation across all datasets. This may not generate the desired result when using faceted plots. As an alternative, you can set stat = "density" to use stat_density. In this case, it is required to add the aesthetic mapping height = ..density.. (see examples).

Aesthetics

Required aesthetics are in bold.

x
y
group Defines the grouping. Not needed if a categorical variable is mapped onto y, but needed otherwise. Will typically be the same variable as is mapped to y.
height The height of each ridgeline at the respective x value. Automatically calculated and provided by stat_density_ridges if the default stat is not changed.
scale A scaling factor to scale the height of the ridgelines relative to the spacing between them. A value of 1 indicates that the maximum point of any ridgeline touches the baseline right above, assuming even spacing between baselines.
rel_min_height Lines with heights below this cutoff will be removed. The cutoff is measured relative to the overall maximum, so rel_min_height=0.01 would remove everything that is 1\ ridgelines. Default is 0, so nothing is removed. alpha
colour, fill, group, alpha, linetype, size, as in geom_ridgeline.
point_shape, point_colour, point_size, point_fill, point_alpha, point_stroke, as in geom_ridgeline.

Examples

library(ggplot2)

# set the `rel_min_height` argument to remove tails
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(rel_min_height = 0.005) +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  theme_ridges()
#> Picking joint bandwidth of 0.181

# set the `scale` to determine how much overlap there is among the plots
ggplot(diamonds, aes(x = price, y = cut)) +
  geom_density_ridges(scale = 4) +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  theme_ridges()
#> Picking joint bandwidth of 458

# the same figure with colors, and using the ggplot2 density stat
ggplot(diamonds, aes(x = price, y = cut, fill = cut, height = ..density..)) +
  geom_density_ridges(scale = 4, stat = "density") +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  scale_fill_brewer(palette = 4) +
  theme_ridges() + theme(legend.position = "none")

# use geom_density_ridges2() instead of geom_density_ridges() for solid polygons
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges2() +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  theme_ridges()
#> Picking joint bandwidth of 0.181