stat_cross: Compute cross-tabulation statistics
In ggstats: Extension to 'ggplot2' for Plotting Stats

stat_cross

R Documentation

Compute cross-tabulation statistics

Description

Computes statistics of a 2-dimensional matrix using broom::augment.htest.

Usage

stat_cross(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  ...,
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  keep.zero.cells = FALSE
)

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`	Override the default connection with `ggplot2::geom_point()`.
`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.
`na.rm`	If `TRUE`, 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()`.
`keep.zero.cells`	If `TRUE`, cells with no observations are kept.

Value

A ggplot2 plot with the added statistic.

Aesthetics

stat_cross() requires the x and the y aesthetics.

Computed variables

observed: number of observations in x,y
prop: proportion of total
row.prop: row proportion
col.prop: column proportion
expected: expected count under the null hypothesis
resid: Pearson's residual
std.resid: standardized residual
row.observed: total number of observations within row
col.observed: total number of observations within column
total.observed: total number of observations within the table
phi: phi coefficients, see augment_chisq_add_phi()

Examples

library(ggplot2)
d <- as.data.frame(Titanic)

# plot number of observations
ggplot(d) +
  aes(x = Class, y = Survived, weight = Freq, size = after_stat(observed)) +
  stat_cross() +
  scale_size_area(max_size = 20)

# custom shape and fill colour based on chi-squared residuals
ggplot(d) +
  aes(
    x = Class, y = Survived, weight = Freq,
    size = after_stat(observed), fill = after_stat(std.resid)
  ) +
  stat_cross(shape = 22) +
  scale_fill_steps2(breaks = c(-3, -2, 2, 3), show.limits = TRUE) +
  scale_size_area(max_size = 20)


# custom shape and fill colour based on phi coeffients
ggplot(d) +
  aes(
    x = Class, y = Survived, weight = Freq,
    size = after_stat(observed), fill = after_stat(phi)
  ) +
  stat_cross(shape = 22) +
  scale_fill_steps2(show.limits = TRUE) +
  scale_size_area(max_size = 20)


# plotting the number of observations as a table
ggplot(d) +
  aes(
    x = Class, y = Survived, weight = Freq, label = after_stat(observed)
  ) +
  geom_text(stat = "cross")

# Row proportions with standardized residuals
ggplot(d) +
  aes(
    x = Class, y = Survived, weight = Freq,
    label = scales::percent(after_stat(row.prop)),
    size = NULL, fill = after_stat(std.resid)
  ) +
  stat_cross(shape = 22, size = 30) +
  geom_text(stat = "cross") +
  scale_fill_steps2(breaks = c(-3, -2, 2, 3), show.limits = TRUE) +
  facet_grid(Sex ~ .) +
  labs(fill = "Standardized residuals") +
  theme_minimal()

ggstats documentation built on April 12, 2025, 1:29 a.m.