Violin plot.

Usage

geom_violin(mapping = NULL, data = NULL, stat = "ydensity", draw_quantiles = NULL, position = "dodge", trim = TRUE, scale = "area", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
stat_ydensity(mapping = NULL, data = NULL, geom = "violin", position = "dodge", adjust = 1, kernel = "gaussian", trim = TRUE, scale = "area", 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), is combined with the default mapping at the top level of the plot. You only need to supply mapping if there isn't a mapping defined for the plot.
data
A data frame. If specified, overrides the default data frame defined at the top level of the plot.
draw_quantiles
If not(NULL) (default), draw horizontal lines at the given quantiles of the density estimate.
position
Position adjustment, either as a string, or the result of a call to a position adjustment function.
trim
If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don't trim the tails.
scale
if "area" (default), all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width", all violins have the same maximum width.
na.rm
If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
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. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.
...
other arguments passed on to layer. There are three types of arguments you can use here:
  • Aesthetics: to set an aesthetic to a fixed value, like color = "red" or size = 3.
  • Other arguments to the layer, for example you override the default stat associated with the layer.
  • Other arguments passed on to the stat.
geom, stat
Use to override the default connection between geom_violin and stat_ydensity.
adjust
see density for details
kernel
kernel used for density estimation, see density for details

Description

Violin plot.

Aesthetics

geom_violin understands the following aesthetics (required aesthetics are in bold):

  • x
  • y
  • alpha
  • colour
  • fill
  • linetype
  • size
  • weight

Computed variables

density
density estimate

scaled
density estimate, scaled to maximum of 1

count
density * number of points - probably useless for violin plots

violinwidth
density scaled for the violin plot, according to area, counts or to a constant maximum width

n
number of points

width
width of violin bounding box

References

Hintze, J. L., Nelson, R. D. (1998) Violin Plots: A Box Plot-Density Trace Synergism. The American Statistician 52, 181-184.

Examples

p <- ggplot(mtcars, aes(factor(cyl), mpg)) p + geom_violin()

p + geom_violin() + geom_jitter(height = 0)

p + geom_violin() + coord_flip()

# Scale maximum width proportional to sample size: p + geom_violin(scale = "count")

# Scale maximum width to 1 for all violins: p + geom_violin(scale = "width")

# Default is to trim violins to the range of the data. To disable: p + geom_violin(trim = FALSE)

# Use a smaller bandwidth for closer density fit (default is 1). p + geom_violin(adjust = .5)

# Add aesthetic mappings # Note that violins are automatically dodged when any aesthetic is # a factor p + geom_violin(aes(fill = cyl))

p + geom_violin(aes(fill = factor(cyl)))

p + geom_violin(aes(fill = factor(vs)))

p + geom_violin(aes(fill = factor(am)))

# Set aesthetics to fixed value p + geom_violin(fill = "grey80", colour = "#3366FF")

# Show quartiles p + geom_violin(draw_quantiles = c(0.25, 0.5, 0.75))

# Scales vs. coordinate transforms ------- if (require("ggplot2movies")) { # Scale transformations occur before the density statistics are computed. # Coordinate transformations occur afterwards. Observe the effect on the # number of outliers. m <- ggplot(movies, aes(y = votes, x = rating, group = cut_width(rating, 0.5))) m + geom_violin() m + geom_violin() + scale_y_log10() m + geom_violin() + coord_trans(y = "log10") m + geom_violin() + scale_y_log10() + coord_trans(y = "log10") # Violin plots with continuous x: # Use the group aesthetic to group observations in violins ggplot(movies, aes(year, budget)) + geom_violin() ggplot(movies, aes(year, budget)) + geom_violin(aes(group = cut_width(year, 10)), scale = "width") }
Warning message: Removed 53573 rows containing non-finite values (stat_ydensity).

See also

geom_violin for examples, and stat_density for examples with data along the x axis.