`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**: Pearsonâ€™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**):

```
<- as.data.frame(Titanic)
d 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()
```