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Create a half-violin half-dot plot, useful for visualising the distribution and the sample size at the same time.

Usage

geom_violindot(
  mapping = NULL,
  data = NULL,
  trim = TRUE,
  scale = c("area", "count", "width"),
  show.legend = NA,
  inherit.aes = TRUE,
  dots_size = 0.7,
  dots_color = NULL,
  dots_fill = NULL,
  binwidth = 0.05,
  position_dots = ggplot2::position_nudge(x = -0.025, y = 0),
  ...,
  size_dots = dots_size,
  color_dots = dots_color,
  fill_dots = dots_fill
)

Arguments

mapping

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.

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. 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)).

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.

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. It can also be a named logical vector to finely select the aesthetics to display.

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().

binwidth

When method is "dotdensity", this specifies maximum bin width. When method is "histodot", this specifies bin width. Defaults to 1/30 of the range of the data

position_dots

Position adjustment for dots, either as a string, or the result of a call to a position adjustment function.

...

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.

size_dots, dots_size

Size adjustment for dots.

color_dots, dots_color

Color adjustment for dots.

fill_dots, dots_fill

Fill adjustment for dots.

Examples

library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violindot() +
  theme_modern()