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,
...
)
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.
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.
The statistical transformation to use on the data for this layer, as a string.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
If TRUE
, the default, relative scaling is calculated separately
for each panel. If FALSE
, relative scaling is calculated globally.
If 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.
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 linewidth = 3
. They may also be parameters
to the paired geom/stat.
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 = after_stat(density)
(see examples).
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
, linewidth
, as in geom_ridgeline
.
point_shape
, point_colour
, point_size
, point_fill
, point_alpha
, point_stroke
, as in geom_ridgeline
.
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 = after_stat(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