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Stat for density ridgeline plots

Source: R/stat_density_quant.R
stat_density_quant.Rd

This stat is the default stat that will be used in geom_density_quant when I get around to making it. Nevertheless, it still works with geom_density. It is very similar to stat_density and stat_density_ridges as it was built as a sort of combination of the two. One of the key differences between this function and those two is that this one uses the Sheather & Jones ("sj") as the default bandwidth selector. This is done because this is a better bandwidth selector than Silverman's ("nrd0") which is the default for the other two functions. In addition, this function allows you to add quantile lines similar to stat_density_ridges.

Usage

stat_density_quant(
  mapping = NULL,
  data = NULL,
  geom = geom,
  position = "stack",
  ...,
  bw = "sj",
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  na.rm = FALSE,
  bounds = c(-Inf, Inf),
  show.legend = NA,
  inherit.aes = TRUE,
  quantile_lines = FALSE,
  calc_ecdf = FALSE,
  quantiles = 4
)

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)).

geom

The geometric object to use to display the data. geom_density is the default.

position

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.

bw

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. Default is sj.

adjust

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

Kernel. See list of available kernels in density().

n

number of equally spaced points at which the density is to be estimated, should be a power of two, see density() for details

na.rm

If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.

bounds

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.

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().

quantile_lines

Logical. Determines if quantile lines should be drawn or not. FALSE is default.

calc_ecdf

If TRUE, stat_density_ridges calculates an empirical cumulative distribution function (ecdf) and returns a variable ecdf and a variable quantile. Both can be mapped onto aesthetics via stat(ecdf) and stat(quantile), respectively.

quantiles

Sets the number of quantiles the data should be broken into. Used if either calc_ecdf = TRUE or quantile_lines = TRUE. If quantiles is an integer then the data will be cut into that many equal quantiles. If it is a vector of probabilities then the data will cut by them.