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

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



Set of aesthetic mappings created by aes() or 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)).


Override the default connection between geom_point and stat_prop.


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


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.


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


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().

If TRUE, cells with no observations are kept.


stat_prop requires the x and the y aesthetics.

Computed variables


number of observations in x,y


proportion of total


row proportion


column proportion


expected count under the null hypothesis


Pearson's residual


standardized residual


# Small function to display plots only if it's interactive p_ <- GGally::print_if_interactive d <- # plot number of observations p_(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 p_(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))
# plotting the number of observations as a table p_(ggplot(d) + aes( x = Class, y = Survived, weight = Freq, label = after_stat(observed) ) + geom_text(stat = "cross"))
# Row proportions with standardized residuals p_(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())
# can work with continuous or character variables data(tips, package = "reshape") p_(ggplot(tips) + aes(x = tip, y = as.character(day), size = after_stat(observed)) + stat_cross(alpha = .1, color = "blue") + scale_size_area(max_size = 12))
#> Warning: Chi-squared approximation may be incorrect