Computes statistics of a 2-dimensional matrix using broom::augment.htest.
stat_cross(
mapping = NULL,
data = NULL,
geom = "point",
position = "identity",
...,
na.rm = TRUE,
show.legend = NA,
inherit.aes = TRUE,
keep.zero.cells = FALSE
)
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.
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 with
ggplot2::geom_point()
.
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.
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.
A ggplot2
plot with the added statistic.
stat_cross()
requires the x and the y aesthetics.
number of observations in x,y
proportion of total
row proportion
column proportion
expected count under the null hypothesis
Pearson's residual
standardized residual
total number of observations within row
total number of observations within column
total number of observations within the table
phi coefficients, see augment_chisq_add_phi()
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)
# \donttest{
# 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()
# }