# Compute cross-tabulation statistics with `stat_cross()`

    library(ggstats)
    library(ggplot2)

This statistic is intended to be used with two discrete variables mapped to x and y aesthetics. It will compute several statistics of a cross-tabulated table using `broom::tidy.test()` and `stats::chisq.test()`. More precisely, the computed variables are:

- 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()`

By default, `stat_cross()` is using `ggplot2::geom_points()`. If you want to plot the number of observations, you need to map `after_stat(observed)` to an aesthetic (here size):

    d <- as.data.frame(Titanic)
    ggplot(d) +
      aes(x = Class, y = Survived, weight = Freq, size = after_stat(observed)) +
      stat_cross() +
      scale_size_area(max_size = 20)

Note that the weight aesthetic is taken into account by `stat_cross()`.

We can go further using a custom shape and filling points with standardized residual to identify visually cells who are over- or underrepresented.

    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)

We can easily recreate a cross-tabulated table.

    ggplot(d) +
      aes(x = Class, y = Survived, weight = Freq) +
      geom_tile(fill = "white", colour = "black") +
      geom_text(stat = "cross", mapping = aes(label = after_stat(observed))) +
      theme_minimal()

Even more complicated, we want to produce a table showing column proportions and where cells are filled with standardized residuals. Note that `stat_cross()` could be used with facets. In that case, computation is done separately in each facet.

    ggplot(d) +
      aes(
        x = Class, y = Survived, weight = Freq,
        label = scales::percent(after_stat(col.prop), accuracy = .1),
        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(rows = vars(Sex)) +
      labs(fill = "Standardized residuals") +
      theme_minimal()