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Plot Cumulative Tail

Source: R/stat_cumulative_tails.R
stat_cumulative_tail.Rd

Nice extention of the default ggplot2 stats. However, it should be easier to use the plot_cumulative_tails() function.

Usage

stat_cumulative_tail(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  bins = 100,
  ...
)

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

geom

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

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().

bins

number of bins

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

Details

stat_bin() is suitable only for continuous x data. If your x data is discrete, you probably want to use stat_count().

By default, the underlying computation (stat_bin()) uses 30 bins; this is not a good default, but the idea is to get you experimenting with different number of bins. You can also experiment modifying the binwidth with center or boundary arguments. binwidth overrides bins so you should do one change at a time. You may need to look at a few options to uncover the full story behind your data.

In addition to geom_histogram(), you can create a histogram plot by using scale_x_binned() with geom_bar(). This method by default plots tick marks in between each bar.

Orientation

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.

Aesthetics

geom_histogram() uses the same aesthetics as geom_bar(); geom_freqpoly() uses the same aesthetics as geom_line().

Computed variables

These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.

  • after_stat(count)
    number of points in bin.

  • after_stat(density)
    density of points in bin, scaled to integrate to 1.

  • after_stat(ncount)
    count, scaled to a maximum of 1.

  • after_stat(ndensity)
    density, scaled to a maximum of 1.

  • after_stat(width)
    widths of bins.

Dropped variables

weight

After binning, weights of individual data points (if supplied) are no longer available.

See also

stat_count(), which counts the number of cases at each x position, without binning. It is suitable for both discrete and continuous x data, whereas stat_bin() is suitable only for continuous x data.

Examples

library(ggplot2)
data("tcga_brca_test")
snvs <- SNVs(tcga_brca_test)

ggplot(snvs, aes(VAF, color = sample_id)) +
 stat_cumulative_tail()


ggplot(snvs, aes(VAF, y = stat(y), color = sample_id)) +
 stat_cumulative_tail()
#> Warning: `stat(y)` was deprecated in ggplot2 3.4.0.
#> ℹ Please use `after_stat(y)` instead.