A violin plot is a compact display of a continuous distribution. It is a
blend of geom_boxplot() and geom_density(): a
violin plot is a mirrored density plot displayed in the same way as a
boxplot.
geom_violin(
mapping = NULL,
data = NULL,
stat = "ydensity",
position = "dodge",
...,
draw_quantiles = NULL,
trim = TRUE,
bounds = c(-Inf, Inf),
scale = "area",
na.rm = FALSE,
orientation = NA,
show.legend = NA,
inherit.aes = TRUE
)stat_ydensity(
mapping = NULL,
data = NULL,
geom = "violin",
position = "dodge",
...,
bw = "nrd0",
adjust = 1,
kernel = "gaussian",
trim = TRUE,
scale = "area",
drop = TRUE,
na.rm = FALSE,
orientation = NA,
show.legend = NA,
inherit.aes = TRUE,
bounds = c(-Inf, Inf)
)
Set of aesthetic mappings created by 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. All objects will be fortified to produce a data frame. See
fortify() for which variables will be created.
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. A function can be created
from a formula (e.g. ~ head(.x, 10)).
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The position argument accepts the following:
The result of calling a position function, such as position_jitter().
This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a
string, strip the function name of the position_ prefix. For example,
to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.
Other arguments passed on to layer()'s params argument. These
arguments broadly fall into one of 4 categories below. Notably, further
arguments to the position argument, or aesthetics that are required
can not be passed through .... Unknown arguments that are not part
of the 4 categories below are ignored.
Static aesthetics that are not mapped to a scale, but are at a fixed
value and apply to the layer as a whole. For example, colour = "red"
or linewidth = 3. The geom's documentation has an Aesthetics
section that lists the available options. The 'required' aesthetics
cannot be passed on to the params. Please note that while passing
unmapped aesthetics as vectors is technically possible, the order and
required length is not guaranteed to be parallel to the input data.
When constructing a layer using
a stat_*() function, the ... argument can be used to pass on
parameters to the geom part of the layer. An example of this is
stat_density(geom = "area", outline.type = "both"). The geom's
documentation lists which parameters it can accept.
Inversely, when constructing a layer using a
geom_*() function, the ... argument can be used to pass on parameters
to the stat part of the layer. An example of this is
geom_area(stat = "density", adjust = 0.5). The stat's documentation
lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through
.... This can be one of the functions described as
key glyphs, to change the display of the layer in the legend.
If not(NULL) (default), draw horizontal lines
at the given quantiles of the density estimate.
If TRUE (default), trim the tails of the violins
to the range of the data. If FALSE, don't trim the tails.
Known lower and upper bounds for estimated data. Default
c(-Inf, Inf) means that there are no (finite) bounds. If any bound is
finite, boundary effect of default density estimation will be corrected by
reflecting tails outside bounds around their closest edge. Data points
outside of bounds are removed with a warning.
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.
If FALSE, the default, missing values are removed with
a warning. If TRUE, missing values are silently removed.
The orientation of the layer. The default (NA)
automatically determines the orientation from the aesthetic mapping. In the
rare event that this fails it can be given explicitly by setting orientation
to either "x" or "y". See the Orientation section for more detail.
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.
It can also be a named logical vector to finely select the aesthetics to
display.
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().
Use to override the default connection between
geom_violin() and stat_ydensity(). For more information about
overriding these connections, see how the stat and
geom arguments work.
The smoothing bandwidth to be used.
If numeric, the standard deviation of the smoothing kernel.
If character, a rule to choose the bandwidth, as listed in
stats::bw.nrd(). Note that automatic calculation of the bandwidth does
not take weights into account.
A multiplicate bandwidth adjustment. This makes it possible
to adjust the bandwidth while still using the a bandwidth estimator.
For example, adjust = 1/2 means use half of the default bandwidth.
Kernel. See list of available kernels in density().
Whether to discard groups with less than 2 observations
(TRUE, default) or keep such groups for position adjustment purposes
(FALSE).
This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the orientation parameter, which can be either "x" or "y". The value gives the axis that the geom should run along, "x" being the default orientation you would expect for the geom.
geom_violin() understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
group
linetype
linewidth
weight
Learn more about setting these aesthetics in vignette("ggplot2-specs").
These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.
after_stat(density)
Density estimate.
after_stat(scaled)
Density estimate, scaled to a maximum of 1.
after_stat(count)
Density * number of points - probably useless for violin plots.
after_stat(violinwidth)
Density scaled for the violin plot, according to area, counts or to a constant maximum width.
after_stat(n)
Number of points.
after_stat(width)
Width of violin bounding box.
Hintze, J. L., Nelson, R. D. (1998) Violin Plots: A Box Plot-Density Trace Synergism. The American Statistician 52, 181-184.
geom_violin() for examples, and stat_density()
for examples with data along the x axis.
p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_violin()
# Orientation follows the discrete axis
ggplot(mtcars, aes(mpg, factor(cyl))) +
geom_violin()
# \donttest{
p + geom_violin() + geom_jitter(height = 0, width = 0.1)
# 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")
}
# }
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