Package 'ggmosaic' reference manual

Title:	Mosaic Plots in the 'ggplot2' Framework
Description:	Mosaic plots in the 'ggplot2' framework. Mosaic plot functionality is provided in a single 'ggplot2' layer by calling the geom 'mosaic'.
Authors:	Haley Jeppson [aut, cre] , Heike Hofmann [aut] , Di Cook [aut] , Hadley Wickham [ctb]
Maintainer:	Haley Jeppson <[email protected]>
License:	GPL (>= 2)
Version:	0.3.4
Built:	2024-11-14 05:45:55 UTC
Source:	https://github.com/haleyjeppson/ggmosaic

Template for a double decker plot. A double decker plot is composed of a sequence of spines in the same direction, with the final spine in the opposite direction.

Description

Template for a double decker plot. A double decker plot is composed of a sequence of spines in the same direction, with the final spine in the opposite direction.

Usage

ddecker(direction = "h")
ddecker(direction = "h")

Arguments

direction

direction of first split

Flying Etiquette Survey Data

Description

Data from the results of a SurveyMonkey survey commissioned by FiveThirtyEight for the story 41 Percent of Fliers Say It’s Rude To Recline Your Airplane Seat.

Usage

fly
fly

Format

A data frame with 1040 rows and 27 variables:

id: Respondent ID
flight_freq: How often do you travel by plane?
do_you_recline: Do you ever recline your seat when you fly?
height: How tall are you?
has_child_under_18: Do you have any children under 18?
three_seats_two_arms: n a row of three seats, who should get to use the two arm rests?
two_seats_one_arm: In a row of two seats, who should get to use the middle arm rest?
window_shade: Who should have control over the window shade?
rude_to_move_to_unsold_seat: Is it rude to move to an unsold seat on a plane?
rude_to_talk_to_neighbor: Generally speaking, is it rude to say more than a few words to the stranger sitting next to you on a plane?
six_hr_flight_leave_seat: On a six hour flight from NYC to LA, how many times is it acceptable to get up if you're not in an aisle seat?
reclining_obligation_to_behind: Under normal circumstances, does a person who reclines their seat during a flight have any obligation to the person sitting behind them?
rude_to_recline: Is it rude to recline your seat on a plane?
eliminate_reclining: Given the opportunity, would you eliminate the possibility of reclining seats on planes entirely?
rude_to_switch_seats_friends: Is it rude to ask someone to switch seats with you in order to be closer to friends?
rude_to_switch_seats_family: Is it rude to ask someone to switch seats with you in order to be closer to family?
rude_to_wake_neighbor_bathroom: Is it rude to wake a passenger up if you are trying to go to the bathroom?
rude_to_wake_neighbor_walk: Is it rude to wake a passenger up if you are trying to walk around?
rude_to_bring_baby: In general, is it rude to bring a baby on a plane?
rude_to_bring_unruly_child: In general, is it rude to knowingly bring unruly children on a plane?
use_electronics_takeoff: Have you ever used personal electronics during take off or landing in violation of a flight attendant's direction?
smoked_inflight: Have you ever smoked a cigarette in an airplane bathroom when it was against the rules?
gender: Gender
age: Age
household_income: Household Income
education: Education
region: Region

Source

https://github.com/fivethirtyeight/data/tree/master/flying-etiquette-survey

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,
  ...
)
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()`. 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. When using a `⁠geom_*()⁠` function to construct a layer, the `stat` argument can be used the override the default coupling between geoms and stats. The `stat` argument accepts the following: A `Stat` ggproto subclass, for example `StatCount`. A string naming the stat. To give the stat as a string, strip the function name of the `stat_` prefix. For example, to use `stat_count()`, give the stat as `"count"`. For more information and other ways to specify the stat, see the layer stat documentation.
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`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 to display the data for this layer. When using a `⁠stat_*()⁠` function to construct a layer, the `geom` argument can be used to override the default coupling between stats and geoms. The `geom` argument accepts the following: A `Geom` ggproto subclass, for example `GeomPoint`. A string naming the geom. To give the geom as a string, strip the function name of the `geom_` prefix. For example, to use `geom_point()`, give the geom as `"point"`. For more information and other ways to specify the geom, see the layer geom documentation.

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


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

# if there is only one variable inside `product()`,
# `product()` can be omitted
ggplot(data = titanic) +
  geom_mosaic(aes(x = Class, fill = Survived))

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))

# if there is only one variable inside `product()`,
# `product()` can be omitted
ggplot(data = titanic) +
  geom_mosaic(aes(x = Class, conds = 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
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

# if there is only one variable inside `product()`,
# `product()` can be omitted
ggplot(data = titanic) +
  geom_mosaic(aes(x = Class, fill = Survived))

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))

# if there is only one variable inside `product()`,
# `product()` can be omitted
ggplot(data = titanic) +
  geom_mosaic(aes(x = Class, conds = 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

Jittered dots in Mosaic plots.

Description

A mosaic plat with jittered dots

Usage

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

stat_mosaic_jitter(
  mapping = NULL,
  data = NULL,
  geom = "mosaic_jitter",
  position = "identity",
  na.rm = FALSE,
  divider = mosaic(),
  show.legend = NA,
  inherit.aes = TRUE,
  offset = 0.01,
  drop_level = FALSE,
  seed = NA,
  ...
)
geom_mosaic_jitter(
  mapping = NULL,
  data = NULL,
  stat = "mosaic_jitter",
  position = "identity",
  na.rm = FALSE,
  divider = mosaic(),
  offset = 0.01,
  drop_level = FALSE,
  seed = NA,
  show.legend = NA,
  inherit.aes = FALSE,
  ...
)

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

Arguments

`mapping`	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.
`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. When using a `⁠geom_*()⁠` function to construct a layer, the `stat` argument can be used the override the default coupling between geoms and stats. The `stat` argument accepts the following: A `Stat` ggproto subclass, for example `StatCount`. A string naming the stat. To give the stat as a string, strip the function name of the `stat_` prefix. For example, to use `stat_count()`, give the stat as `"count"`. For more information and other ways to specify the stat, see the layer stat documentation.
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`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
`drop_level`	Generate points for the max - 1 level
`seed`	Random seed passed to `set.seed`. Defaults to `NA`, which means that `set.seed` will not be called.
`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 to display the data for this layer. When using a `⁠stat_*()⁠` function to construct a layer, the `geom` argument can be used to override the default coupling between stats and geoms. The `geom` argument accepts the following: A `Geom` ggproto subclass, for example `GeomPoint`. A string naming the geom. To give the geom as a string, strip the function name of the `geom_` prefix. For example, to use `geom_point()`, give the geom as `"point"`. For more information and other ways to specify the geom, see the layer geom documentation.

Computed variables

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

data(titanic)

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class), fill = Survived), alpha = 0.3) +
  geom_mosaic_jitter(aes(x = product(Class), color = Survived))

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class)), alpha = 0.1) +
  geom_mosaic_jitter(aes(x = product(Class), color = Survived), drop_level = TRUE)

ggplot(data = titanic) +
  geom_mosaic(alpha = 0.3, aes(x = product(Class, Sex),  fill = Survived),
              divider = c("vspine", "hspine", "hspine")) +
  geom_mosaic_jitter(aes(x = product(Class, Sex), color = Survived),
              divider = c("vspine", "hspine", "hspine"))

 ggplot(data = titanic) +
  geom_mosaic(alpha = 0.3, aes(x = product(Class), conds = product(Sex),  fill = Survived),
              divider = c("vspine", "hspine", "hspine")) +
  geom_mosaic_jitter(aes(x = product(Class), conds = product(Sex), fill = Survived),
              divider = c("vspine", "hspine", "hspine"))
data(titanic)

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class), fill = Survived), alpha = 0.3) +
  geom_mosaic_jitter(aes(x = product(Class), color = Survived))

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class)), alpha = 0.1) +
  geom_mosaic_jitter(aes(x = product(Class), color = Survived), drop_level = TRUE)

ggplot(data = titanic) +
  geom_mosaic(alpha = 0.3, aes(x = product(Class, Sex),  fill = Survived),
              divider = c("vspine", "hspine", "hspine")) +
  geom_mosaic_jitter(aes(x = product(Class, Sex), color = Survived),
              divider = c("vspine", "hspine", "hspine"))

 ggplot(data = titanic) +
  geom_mosaic(alpha = 0.3, aes(x = product(Class), conds = product(Sex),  fill = Survived),
              divider = c("vspine", "hspine", "hspine")) +
  geom_mosaic_jitter(aes(x = product(Class), conds = product(Sex), fill = Survived),
              divider = c("vspine", "hspine", "hspine"))

Labeling for Mosaic plots.

Description

A mosaic plot with text or labels

Usage

geom_mosaic_text(
  mapping = NULL,
  data = NULL,
  stat = "mosaic",
  position = "identity",
  na.rm = FALSE,
  divider = mosaic(),
  offset = 0.01,
  show.legend = NA,
  inherit.aes = FALSE,
  as.label = FALSE,
  repel = FALSE,
  repel_params = NULL,
  check_overlap = FALSE,
  ...
)
geom_mosaic_text(
  mapping = NULL,
  data = NULL,
  stat = "mosaic",
  position = "identity",
  na.rm = FALSE,
  divider = mosaic(),
  offset = 0.01,
  show.legend = NA,
  inherit.aes = FALSE,
  as.label = FALSE,
  repel = FALSE,
  repel_params = NULL,
  check_overlap = FALSE,
  ...
)

Arguments

`mapping`	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.
`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. When using a `⁠geom_*()⁠` function to construct a layer, the `stat` argument can be used the override the default coupling between geoms and stats. The `stat` argument accepts the following: A `Stat` ggproto subclass, for example `StatCount`. A string naming the stat. To give the stat as a string, strip the function name of the `stat_` prefix. For example, to use `stat_count()`, give the stat as `"count"`. For more information and other ways to specify the stat, see the layer stat documentation.
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`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()`.
`as.label`	Show as a ggplot label (box with round corners)
`repel`	Use ggrepel wo labels don't overlap
`repel_params`	List of ggrepel parameters (e.g. list(point.padding = 0))
`check_overlap`	If `TRUE`, text that overlaps previous text in the same layer will not be plotted. `check_overlap` happens at draw time and in the order of the data. Therefore data should be arranged by the label column before calling `geom_label()` or `geom_text()`.
`...`	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.

Examples

data(titanic)

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

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class, Sex),  fill = Survived),
              divider = c("vspine", "hspine", "hspine")) +
  geom_mosaic_text(aes(x = product(Class, Sex), fill = Survived),
              divider = c("vspine", "hspine", "hspine"), size = 2)

ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE)

# avoid overlapping text
ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE, check_overlap = TRUE)

# or use ggrepel
ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE, repel = TRUE)

# and as a label
ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE, repel = TRUE, as.label=TRUE)

data(titanic)

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

ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class, Sex),  fill = Survived),
              divider = c("vspine", "hspine", "hspine")) +
  geom_mosaic_text(aes(x = product(Class, Sex), fill = Survived),
              divider = c("vspine", "hspine", "hspine"), size = 2)

ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE)

# avoid overlapping text
ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE, check_overlap = TRUE)

# or use ggrepel
ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE, repel = TRUE)

# and as a label
ggplot(data = happy) +
  geom_mosaic(aes(x = product(health), fill = happy), na.rm = TRUE, show.legend = FALSE) +
  geom_mosaic_text(aes(x = product(happy, health)), na.rm = TRUE, repel = TRUE, as.label=TRUE)

Launch shiny app

Description

Shiny app "EDA with Mosaic Plots" for interactive exploratory model building

Usage

ggmosaic_app(example = c("mosaics", "models"), ...)
ggmosaic_app(example = c("mosaics", "models"), ...)

Arguments

`example`	Selected shiny app to launch.
`...`	arguments passed on.

Data related to happiness from the general social survey.

Description

The data is a small sample of variables related to happiness from the general social survey (GSS). The GSS is a yearly cross-sectional survey of Americans, run since 1972. We combine data for more than 25 years to yield over 60 thousand observations, and of the over 5,000 variables, we select some variables that are related to happiness:

Usage

data(happy)
data(happy)

Format

A data frame with 62466 rows and 11 variables

year. year of the response, 1972 to 2018.
age. age in years: 18–89 (89 stands for all 89 year olds and older).
degree. highest education: lt high school, high school, junior college, bachelor, graduate.
finrela. how is your financial status compared to others: far below, below average, average, above average, far above.
happy. happiness: very happy, pretty happy, not too happy.
health. health: excellent, good, fair, poor.
marital. marital status: married, never married, divorced, widowed, separated.
sex. sex: female, male.
polviews. from extremely conservative to extremely liberal.
partyid. party identification: strong republican, not str republican, ind near rep, independent, ind near dem, not str democrat, strong democrat, other party.
wtssall. probability weight. 0.39–8.74

Horizontal bar partition: width constant, height varies.

Description

Horizontal bar partition: width constant, height varies.

Usage

hbar(data, bounds, offset = 0.02, max = NULL)
hbar(data, bounds, offset = 0.02, max = NULL)

Arguments

`data`	bounds data frame
`bounds`	bounds of space to partition
`offset`	space between spines
`max`	maximum value

Horizontal spine partition: height constant, width varies.

Description

Horizontal spine partition: height constant, width varies.

Usage

hspine(data, bounds, offset = offset, max = NULL)
hspine(data, bounds, offset = offset, max = NULL)

Arguments

`data`	bounds data frame
`bounds`	bounds of space to partition
`offset`	space between spines
`max`	maximum value

Template for a mosaic plot. A mosaic plot is composed of spines in alternating directions.

Description

Template for a mosaic plot. A mosaic plot is composed of spines in alternating directions.

Usage

mosaic(direction = "h")
mosaic(direction = "h")

Arguments

direction

direction of first split

Wrapper for a list

Description

Wrapper for a list

Usage

product(...)
product(...)

Arguments

...

Unquoted variables going into the product plot.

Examples

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

Helper function for determining scales

Description

Used internally to determine class of variable x

Usage

## S3 method for class 'productlist'
scale_type(x)
## S3 method for class 'productlist'
scale_type(x)

Arguments

x

variable

Value

character string "productlist"

Determining scales for mosaics

Description

Determining scales for mosaics

Usage

scale_x_productlist(
  name = ggplot2::waiver(),
  breaks = product_breaks(),
  minor_breaks = NULL,
  labels = product_labels(),
  limits = NULL,
  expand = ggplot2::waiver(),
  oob = scales::censor,
  na.value = NA_real_,
  transform = "identity",
  position = "bottom",
  sec.axis = ggplot2::waiver()
)

scale_y_productlist(
  name = ggplot2::waiver(),
  breaks = product_breaks(),
  minor_breaks = NULL,
  labels = product_labels(),
  limits = NULL,
  expand = ggplot2::waiver(),
  oob = scales::censor,
  na.value = NA_real_,
  transform = "identity",
  position = "left",
  sec.axis = ggplot2::waiver()
)

ScaleContinuousProduct
scale_x_productlist(
  name = ggplot2::waiver(),
  breaks = product_breaks(),
  minor_breaks = NULL,
  labels = product_labels(),
  limits = NULL,
  expand = ggplot2::waiver(),
  oob = scales::censor,
  na.value = NA_real_,
  transform = "identity",
  position = "bottom",
  sec.axis = ggplot2::waiver()
)

scale_y_productlist(
  name = ggplot2::waiver(),
  breaks = product_breaks(),
  minor_breaks = NULL,
  labels = product_labels(),
  limits = NULL,
  expand = ggplot2::waiver(),
  oob = scales::censor,
  na.value = NA_real_,
  transform = "identity",
  position = "left",
  sec.axis = ggplot2::waiver()
)

ScaleContinuousProduct

Arguments

`name`	set to pseudo waiver function `product_names` by default.
`breaks`	One of: `NULL` for no breaks `waiver()` for the default breaks computed by the transformation object A numeric vector of positions A function that takes the limits as input and returns breaks as output (e.g., a function returned by `scales::extended_breaks()`). Note that for position scales, limits are provided after scale expansion. Also accepts rlang lambda function notation.
`minor_breaks`	One of: `NULL` for no minor breaks `waiver()` for the default breaks (one minor break between each major break) A numeric vector of positions A function that given the limits returns a vector of minor breaks. Also accepts rlang lambda function notation. When the function has two arguments, it will be given the limits and major breaks.
`labels`	One of: `NULL` for no labels `waiver()` for the default labels computed by the transformation object A character vector giving labels (must be same length as `breaks`) An expression vector (must be the same length as breaks). See ?plotmath for details. A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation.
`limits`	One of: `NULL` to use the default scale range A numeric vector of length two providing limits of the scale. Use `NA` to refer to the existing minimum or maximum A function that accepts the existing (automatic) limits and returns new limits. Also accepts rlang lambda function notation. Note that setting limits on positional scales will remove data outside of the limits. If the purpose is to zoom, use the limit argument in the coordinate system (see `coord_cartesian()`).
`expand`	For position scales, a vector of range expansion constants used to add some padding around the data to ensure that they are placed some distance away from the axes. Use the convenience function `expansion()` to generate the values for the `expand` argument. The defaults are to expand the scale by 5% on each side for continuous variables, and by 0.6 units on each side for discrete variables.
`oob`	One of: Function that handles limits outside of the scale limits (out of bounds). Also accepts rlang lambda function notation. The default (`scales::censor()`) replaces out of bounds values with `NA`. `scales::squish()` for squishing out of bounds values into range. `scales::squish_infinite()` for squishing infinite values into range.
`na.value`	Missing values will be replaced with this value.
`transform`	For continuous scales, the name of a transformation object or the object itself. Built-in transformations include "asn", "atanh", "boxcox", "date", "exp", "hms", "identity", "log", "log10", "log1p", "log2", "logit", "modulus", "probability", "probit", "pseudo_log", "reciprocal", "reverse", "sqrt" and "time". A transformation object bundles together a transform, its inverse, and methods for generating breaks and labels. Transformation objects are defined in the scales package, and are called `⁠transform_<name>⁠`. If transformations require arguments, you can call them from the scales package, e.g. `scales::transform_boxcox(p = 2)`. You can create your own transformation with `scales::new_transform()`.
`position`	For position scales, The position of the axis. `left` or `right` for y axes, `top` or `bottom` for x axes.
`sec.axis`	specify a secondary axis

Format

An object of class ScaleContinuousProduct (inherits from ScaleContinuousPosition, ScaleContinuous, Scale, ggproto, gg) of length 5.

Spine partition: divide longest dimension.

Description

Spine partition: divide longest dimension.

Usage

spine(data, bounds, offset = offset, max = NULL)
spine(data, bounds, offset = offset, max = NULL)

Arguments

`data`	bounds data frame
`bounds`	bounds of space to partition
`offset`	space between spines
`max`	maximum value

Internal helper function

Description

Squeeze pieces to lie within specified bounds; directly copied from package productplots

Usage

squeeze(pieces, bounds = bound())
squeeze(pieces, bounds = bound())

Arguments

`pieces`	rectangle specified via l(eft), r(ight), b(ottom), t(op)
`bounds`	rectangle specified via l(eft), r(ight), b(ottom), t(op)

Value

re-scaled values for piece according to boundaries given by bounds

Author(s)

Hadley Wickham

Geom proto

Description

Geom proto

Description

Geom proto

Description

Geom proto

Theme for mosaic plots

Description

Themes set the general aspect of the plot such as the colour of the background, gridlines, the size and colour of fonts. theme_mosaic provides access to the regular ggplot2 theme, but removes any background, most of the gridlines, and ensures an aspect ratio of 1 for better viewing of the mosaics.

Arguments

`base_size`	base font size
`base_family`	base font family

Examples

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

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

Passengers and crew on board the Titanic

Description

A dataset containing some demographics and survival of people on board the Titanic

Usage

titanic
titanic

Format

A data frame with 2201 rows and 4 variables:

Class: factor variable containing the class of a passenger (1st, 2nd, 3rd) or crew.
Sex: Male/Female.
Age: Child/Adult. This information is not very reliable, because it was inferred from boarding documents that did not state actual age in years.
Survived: Yes/No.

Vertical bar partition: height constant, width varies.

Description

Vertical bar partition: height constant, width varies.

Usage

vbar(data, bounds, offset = 0.02, max = NULL)
vbar(data, bounds, offset = 0.02, max = NULL)

Arguments

`data`	bounds data frame
`bounds`	bounds of space to partition
`offset`	space between spines
`max`	maximum value

Vertical spine partition: width constant, height varies.

Description

Vertical spine partition: width constant, height varies.

Usage

vspine(data, bounds, offset = offset, max = NULL)
vspine(data, bounds, offset = offset, max = NULL)

Arguments

`data`	bounds data frame
`bounds`	bounds of space to partition
`offset`	space between spines
`max`	maximum value

Package 'ggmosaic'

Help Index

Template for a double decker plot. A double decker plot is composed of a sequence of spines in the same direction, with the final spine in the opposite direction.

Description

Usage

Arguments

Flying Etiquette Survey Data

Description

Usage

Format

Source

Mosaic plots.

Description

Usage

Arguments

Computed variables

Examples

Jittered dots in Mosaic plots.

Description

Usage

Arguments

Computed variables

Examples

Labeling for Mosaic plots.

Description

Usage

Arguments

Examples

Geom proto

Description

Geom proto

Description

Geom proto

Description

Launch shiny app

Description

Usage

Arguments

Data related to happiness from the general social survey.

Description

Usage

Format

Horizontal bar partition: width constant, height varies.

Description

Usage

Arguments

Horizontal spine partition: height constant, width varies.

Description

Usage

Arguments

Template for a mosaic plot. A mosaic plot is composed of spines in alternating directions.

Description

Usage

Arguments

Wrapper for a list

Description

Usage

Arguments

Examples

Helper function for determining scales

Description

Usage

Arguments

Value

Determining scales for mosaics

Description

Usage

Arguments

Format

Spine partition: divide longest dimension.

Description

Usage

Arguments

Internal helper function

Description

Usage

Arguments

Value

Author(s)

Geom proto