ggmosaic (version 0.3.3)

geom_mosaic: Mosaic plots.

Description

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

Usage

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,
  ...
)

Arguments

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

Computed variables

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

Examples

# NOT RUN {
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~.)
# }
# NOT RUN {
 # end of don't run
# }