geom_mosaic: Mosaic plots.
In ggmosaic: Mosaic Plots in the 'ggplot2' Framework

Description Usage Arguments Computed variables Examples

A mosaic plot is a convenient graphical summary of the conditional distributions in a contingency table and is composed of spines in alternating directions.

geom_mosaic(
  mapping = NULL,
  data = NULL,
  stat = "mosaic",
  position = "identity",
  na.rm = FALSE,
  divider = mosaic(),
  offset = 0.01,
  show.legend = NA,
  inherit.aes = FALSE,
  ...
)

stat_mosaic_text(
  mapping = NULL,
  data = NULL,
  geom = "Text",
  position = "identity",
  na.rm = FALSE,
  divider = mosaic(),
  show.legend = NA,
  inherit.aes = TRUE,
  offset = 0.01,
  ...
)

stat_mosaic(
  mapping = NULL,
  data = NULL,
  geom = "mosaic",
  position = "identity",
  na.rm = FALSE,
  divider = mosaic(),
  show.legend = NA,
  inherit.aes = TRUE,
  offset = 0.01,
  ...
)

`mapping`	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.
`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)`).
`stat`	The statistical transformation to use on the data for this layer, as a string.
`position`	Position adjustment, either as a string, or the result of a call to a position adjustment function.
`na.rm`	If `FALSE` (the default), removes missing values with a warning. If `TRUE` silently removes missing values.
`divider`	Divider function. The default divider function is mosaic() which will use spines in alternating directions. The four options for partitioning: `vspine` Vertical spine partition: width constant, height varies. `hspine` Horizontal spine partition: height constant, width varies. `vbar` Vertical bar partition: height constant, width varies. `hbar` Horizontal bar partition: width constant, height varies.
`offset`	Set the space between the first spine
`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()`.
`...`	other arguments passed on to `layer`. These are often aesthetics, used to set an aesthetic to a fixed value, like `color = 'red'` or `size = 3`. They may also be parameters to the paired geom/stat.
`geom`	The geometric object to use display the data

x: location of center of the rectangle
y: location of center of the rectangle

xmin: location of bottom left corner
xmax: location of bottom right corner
ymin: location of top left corner
ymax: location of top right corner

data(titanic)

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class), fill = Survived))
# good practice: use the 'dependent' variable (or most important variable)
# as fill variable

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class, Age), fill = Survived))

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class), conds = product(Age), fill = Survived))

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Survived, Class), fill = Age))

# Just excluded for timing. Examples are included in testing to make sure they work
## Not run: 
data(happy)

ggplot(data = happy) + geom_mosaic(aes(x = product(happy)), divider="hbar")

ggplot(data = happy) + geom_mosaic(aes(x = product(happy))) +
  coord_flip()

# weighting is important
ggplot(data = happy) +
  geom_mosaic(aes(weight=wtssall, x=product(happy)))

ggplot(data = happy) + geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy)) +
  theme(axis.text.x=element_text(angle=35))

ggplot(data = happy) +
  geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy), na.rm=TRUE)

ggplot(data = happy) +
  geom_mosaic(aes(weight=wtssall, x=product(health, sex, degree), fill=happy),
  na.rm=TRUE)

# here is where a bit more control over the spacing of the bars is helpful:
# set labels manually:
ggplot(data = happy) +
  geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) +
  scale_x_productlist("Age", labels=c(17+1:72))

# thin out labels manually:
labels <- c(17+1:72)
labels[labels %% 5 != 0] <- ""
ggplot(data = happy) +
  geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) +
  scale_x_productlist("Age", labels=labels)

ggplot(data = happy) +
  geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy, conds = product(sex)),
  divider=mosaic("v"), na.rm=TRUE, offset=0.001) +
  scale_x_productlist("Age", labels=labels)

ggplot(data = happy) +
  geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset = 0) +
  facet_grid(sex~.) +
  scale_x_productlist("Age", labels=labels)

ggplot(data = happy) +
  geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)),
  divider=mosaic("h"))

ggplot(data = happy) +
  geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), offset=.005)

# Spine example
ggplot(data = happy) +
 geom_mosaic(aes(weight = wtssall, x = product(health), fill = health)) +
 facet_grid(happy~.)

## End(Not run) # end of don't run