Title: | Alluvial Plots in 'ggplot2' |
---|---|
Description: | Alluvial plots use variable-width ribbons and stacked bar plots to represent multi-dimensional or repeated-measures data with categorical or ordinal variables; see Riehmann, Hanfler, and Froehlich (2005) <doi:10.1109/INFVIS.2005.1532152> and Rosvall and Bergstrom (2010) <doi:10.1371/journal.pone.0008694>. Alluvial plots are statistical graphics in the sense of Wilkinson (2006) <doi:10.1007/0-387-28695-0>; they share elements with Sankey diagrams and parallel sets plots but are uniquely determined from the data and a small set of parameters. This package extends Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics to generate alluvial plots from tidy data. |
Authors: | Jason Cory Brunson [aut, cre], Quentin D. Read [aut] |
Maintainer: | Jason Cory Brunson <[email protected]> |
License: | GPL-3 |
Version: | 0.12.5 |
Built: | 2024-08-10 13:28:11 UTC |
Source: | https://github.com/corybrunson/ggalluvial |
Alluvial plots consist of multiple horizontally-distributed columns (axes) representing factor variables, vertical divisions (strata) of these axes representing these variables' values; and splines (alluvial flows) connecting vertical subdivisions (lodes) within strata of adjacent axes representing subsets or amounts of observations that take the corresponding values of the corresponding variables. This function checks a data frame for either of two types of alluvial structure:
is_lodes_form( data, key, value, id, weight = NULL, site = NULL, logical = TRUE, silent = FALSE ) is_alluvia_form( data, ..., axes = NULL, weight = NULL, logical = TRUE, silent = FALSE ) to_lodes_form( data, ..., axes = NULL, key = "x", value = "stratum", id = "alluvium", diffuse = FALSE, discern = FALSE ) to_alluvia_form(data, key, value, id, distill = FALSE)
is_lodes_form( data, key, value, id, weight = NULL, site = NULL, logical = TRUE, silent = FALSE ) is_alluvia_form( data, ..., axes = NULL, weight = NULL, logical = TRUE, silent = FALSE ) to_lodes_form( data, ..., axes = NULL, key = "x", value = "stratum", id = "alluvium", diffuse = FALSE, discern = FALSE ) to_alluvia_form(data, key, value, id, distill = FALSE)
data |
A data frame. |
key , value , id
|
In |
weight |
Optional field of |
site |
Optional vector of fields of |
logical |
Defunct. Whether to return a logical value or a character string indicating the type of alluvial structure ("none", "lodes", or "alluvia"). |
silent |
Whether to print messages. |
... |
Used in |
axes |
In |
diffuse |
Fields of |
discern |
Logical value indicating whether to suffix values of the
variables used as axes that appear at more than one variable in order to
distinguish their factor levels. This forces the levels of the combined
factor variable |
distill |
A logical value indicating whether to include variables, other
than those passed to |
One row per lode, wherein each row encodes a subset or amount of
observations having a specific profile of axis values, a key
field
encodes the axis, a value
field encodes the value within each axis, and a
id
column identifies multiple lodes corresponding to the same subset or
amount of observations. is_lodes_form
tests for this structure.
One row per alluvium, wherein each row encodes a subset or amount of
observations having a specific profile of axis values and a set axes
of
fields encodes its values at each axis variable. is_alluvia_form
tests
for this structure.
to_lodes_form
takes a data frame with several designated variables to
be used as axes in an alluvial plot, and reshapes the data frame so that
the axis variable names constitute a new factor variable and their values
comprise another. Other variables' values will be repeated, and a
row-grouping variable can be introduced. This function invokes
tidyr::gather()
.
to_alluvia_form
takes a data frame with axis and axis value variables
to be used in an alluvial plot, and reshape the data frame so that the
axes constitute separate variables whose values are given by the value
variable. This function invokes tidyr::spread()
.
Other alluvial data manipulation:
self-adjoin
# Titanic data in alluvia format titanic_alluvia <- as.data.frame(Titanic) head(titanic_alluvia) is_alluvia_form(titanic_alluvia, weight = "Freq") # Titanic data in lodes format titanic_lodes <- to_lodes_form(titanic_alluvia, key = "x", value = "stratum", id = "alluvium", axes = 1:4) head(titanic_lodes) is_lodes_form(titanic_lodes, key = "x", value = "stratum", id = "alluvium", weight = "Freq") # again in lodes format, this time diffusing the `Class` variable titanic_lodes2 <- to_lodes_form(titanic_alluvia, key = variable, value = value, id = cohort, 1:3, diffuse = Class) head(titanic_lodes2) is_lodes_form(titanic_lodes2, key = variable, value = value, id = cohort, weight = Freq) # use `site` to separate data before lode testing is_lodes_form(titanic_lodes2, key = variable, value = value, id = Class, weight = Freq) is_lodes_form(titanic_lodes2, key = variable, value = value, id = Class, weight = Freq, site = cohort) # curriculum data in lodes format data(majors) head(majors) is_lodes_form(majors, key = "semester", value = "curriculum", id = "student") # curriculum data in alluvia format majors_alluvia <- to_alluvia_form(majors, key = "semester", value = "curriculum", id = "student") head(majors_alluvia) is_alluvia_form(majors_alluvia, tidyselect::starts_with("CURR")) # distill variables that vary within `id` values set.seed(1) majors$hypo_grade <- LETTERS[sample(5, size = nrow(majors), replace = TRUE)] majors_alluvia2 <- to_alluvia_form(majors, key = "semester", value = "curriculum", id = "student", distill = "most") head(majors_alluvia2) # options to distinguish strata at different axes gg <- ggplot(majors_alluvia, aes(axis1 = CURR1, axis2 = CURR7, axis3 = CURR13)) gg + geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = TRUE) + geom_stratum(width = 2/5, discern = TRUE) + geom_text(stat = "stratum", discern = TRUE, aes(label = after_stat(stratum))) gg + geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = FALSE) + geom_stratum(width = 2/5, discern = FALSE) + geom_text(stat = "stratum", discern = FALSE, aes(label = after_stat(stratum))) # warning when inappropriate ggplot(majors[majors$semester %in% paste0("CURR", c(1, 7, 13)), ], aes(x = semester, stratum = curriculum, alluvium = student, label = curriculum)) + geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = TRUE) + geom_stratum(width = 2/5, discern = TRUE) + geom_text(stat = "stratum", discern = TRUE)
# Titanic data in alluvia format titanic_alluvia <- as.data.frame(Titanic) head(titanic_alluvia) is_alluvia_form(titanic_alluvia, weight = "Freq") # Titanic data in lodes format titanic_lodes <- to_lodes_form(titanic_alluvia, key = "x", value = "stratum", id = "alluvium", axes = 1:4) head(titanic_lodes) is_lodes_form(titanic_lodes, key = "x", value = "stratum", id = "alluvium", weight = "Freq") # again in lodes format, this time diffusing the `Class` variable titanic_lodes2 <- to_lodes_form(titanic_alluvia, key = variable, value = value, id = cohort, 1:3, diffuse = Class) head(titanic_lodes2) is_lodes_form(titanic_lodes2, key = variable, value = value, id = cohort, weight = Freq) # use `site` to separate data before lode testing is_lodes_form(titanic_lodes2, key = variable, value = value, id = Class, weight = Freq) is_lodes_form(titanic_lodes2, key = variable, value = value, id = Class, weight = Freq, site = cohort) # curriculum data in lodes format data(majors) head(majors) is_lodes_form(majors, key = "semester", value = "curriculum", id = "student") # curriculum data in alluvia format majors_alluvia <- to_alluvia_form(majors, key = "semester", value = "curriculum", id = "student") head(majors_alluvia) is_alluvia_form(majors_alluvia, tidyselect::starts_with("CURR")) # distill variables that vary within `id` values set.seed(1) majors$hypo_grade <- LETTERS[sample(5, size = nrow(majors), replace = TRUE)] majors_alluvia2 <- to_alluvia_form(majors, key = "semester", value = "curriculum", id = "student", distill = "most") head(majors_alluvia2) # options to distinguish strata at different axes gg <- ggplot(majors_alluvia, aes(axis1 = CURR1, axis2 = CURR7, axis3 = CURR13)) gg + geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = TRUE) + geom_stratum(width = 2/5, discern = TRUE) + geom_text(stat = "stratum", discern = TRUE, aes(label = after_stat(stratum))) gg + geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = FALSE) + geom_stratum(width = 2/5, discern = FALSE) + geom_text(stat = "stratum", discern = FALSE, aes(label = after_stat(stratum))) # warning when inappropriate ggplot(majors[majors$semester %in% paste0("CURR", c(1, 7, 13)), ], aes(x = semester, stratum = curriculum, alluvium = student, label = curriculum)) + geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = TRUE) + geom_stratum(width = 2/5, discern = TRUE) + geom_text(stat = "stratum", discern = TRUE)
geom_alluvium
receives a dataset of the horizontal (x
) and vertical (y
,
ymin
, ymax
) positions of the lodes of an alluvial plot, the
intersections of the alluvia with the strata. It plots both the lodes
themselves, using geom_lode()
, and the flows between them, using
geom_flow()
.
geom_alluvium( mapping = NULL, data = NULL, stat = "alluvium", position = "identity", width = 1/3, knot.pos = 1/4, knot.prop = TRUE, curve_type = NULL, curve_range = NULL, segments = NULL, outline.type = "both", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) data_to_alluvium( data, knot.prop = TRUE, curve_type = "spline", curve_range = NULL, segments = NULL )
geom_alluvium( mapping = NULL, data = NULL, stat = "alluvium", position = "identity", width = 1/3, knot.pos = 1/4, knot.prop = TRUE, curve_type = NULL, curve_range = NULL, segments = NULL, outline.type = "both", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) data_to_alluvium( data, knot.prop = TRUE, curve_type = "spline", curve_range = NULL, segments = NULL )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data; override the default. |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
width |
Numeric; the width of each stratum, as a proportion of the distance between axes. Defaults to 1/3. |
knot.pos |
The horizontal distance of x-spline knots from each stratum
( |
knot.prop |
Logical; whether to interpret |
curve_type |
Character; the type of curve used to produce flows.
Defaults to |
curve_range |
For alternative |
segments |
The number of segments to be used in drawing each alternative curve (each curved boundary of each flow). If less than 3, will be silently changed to 3. |
outline.type |
Type of outline of each alluvium; one of |
na.rm |
Logical:
if |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Additional arguments passed to |
The helper function data_to_alluvium()
takes internal ggplot2 data
(mapped aesthetics) and curve parameters for a single alluvium as input and
returns a data frame of x
, y
, and shape
used by grid::xsplineGrob()
to render the alluvium.
geom_alluvium
, geom_flow
, geom_lode
, and geom_stratum
understand the
following aesthetics (required aesthetics are in bold):
x
y
ymin
ymax
alpha
colour
fill
linetype
size
group
group
is used internally; arguments are ignored.
Alluvium, flow, and lode geoms default to alpha = 0.5
. Learn more about
setting these aesthetics in vignette("ggplot2-specs", package = "ggplot2")
.
By default, geom_alluvium()
and geom_flow()
render flows between lodes as
filled regions between parallel x-splines. These graphical elements,
generated using grid::xsplineGrob()
, are
parameterized by the relative location of the knot (knot.pos
). They are
quick to render and clear to read, but users may prefer plots that use
differently-shaped ribbons.
A variety of such options are documented at, e.g., this easing functions cheat sheet and this blog post by Jeffrey Shaffer. Easing functions are
not (yet) used in ggalluvial, but several alternative curves are available.
Each is encoded as a continuous, increasing, bijective function from the unit
interval to itself, and each is rescaled so that its endpoints
meet the corresponding lodes. They are rendered piecewise-linearly, by
default using
segments = 48
. Summon each curve type by passing one of the
following strings to curve_type
:
"linear"
: , the unique degree-1 polynomial that takes
0 to 0 and 1 to 1
"cubic"
: , the unique
degree-3 polynomial that also is flat at both endpoints
"quintic"
: ,
the unique degree-5 polynomial that also has zero curvature
at both endpoints
"sine"
: the unique sinusoidal function that is flat at both
endpoints
"arctangent"
: the inverse tangent function, scaled and re-centered to the
unit interval from the interval centered at zero with
radius curve_range
"sigmoid"
: the sigmoid function, scaled and re-centered to the unit
interval from the interval centered at zero with radius
curve_range
Only the (default) "xspline"
option uses the knot.*
parameters, while
only the alternative curves use the segments
parameter, and only
"arctangent"
and "sigmoid"
use the curve_range
parameter. (Both are
ignored if not needed.) Larger values of curve_range
result in greater
compression and steeper slopes. The NULL
default will be changed to
2+sqrt(3)
for "arctangent"
and to 6
for "sigmoid"
.
These package-specific options set global values for curve_type
,
curve_range
, and segments
that will be defaulted to when not manually
set:
ggalluvial.curve_type
: defaults to "xspline"
.
ggalluvial.curve_range
: defaults to NA
, which triggers the
curve-specific default values.
ggalluvial.segments
: defaults to 48L
.
See base::options()
for how to use options.
The previously defunct parameters axis_width
and ribbon_bend
have been
discontinued. Use width
and knot.pos
instead.
ggplot2::layer()
for additional arguments and
stat_alluvium()
and
stat_flow()
for the corresponding stats.
Other alluvial geom layers:
geom_flow()
,
geom_lode()
,
geom_stratum()
# basic ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, fill = Survived)) + geom_alluvium() + scale_x_discrete(limits = c("Class", "Sex", "Age")) gg <- ggplot(alluvial::Refugees, aes(y = refugees, x = year, alluvium = country)) # time series bump chart (sigmoid flows) gg + geom_alluvium(aes(fill = country, colour = country), width = 1/4, alpha = 2/3, decreasing = FALSE, curve_type = "sigmoid") # time series line plot of refugees data, sorted by country gg + geom_alluvium(aes(fill = country, colour = country), decreasing = NA, width = 0, knot.pos = 0) # irregular spacing between axes of a continuous variable refugees_sub <- subset(alluvial::Refugees, year %in% c(2003, 2005, 2010, 2013)) gg <- ggplot(data = refugees_sub, aes(x = year, y = refugees, alluvium = country)) + theme_bw() + scale_fill_brewer(type = "qual", palette = "Set3") # proportional knot positioning (default) gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2) # constant knot positioning gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2, knot.pos = 1, knot.prop = FALSE) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2) # coarsely-segmented curves gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2, curve_type = "arctan", segments = 6) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2) # custom-ranged curves gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2, curve_type = "arctan", curve_range = 1) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2)
# basic ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, fill = Survived)) + geom_alluvium() + scale_x_discrete(limits = c("Class", "Sex", "Age")) gg <- ggplot(alluvial::Refugees, aes(y = refugees, x = year, alluvium = country)) # time series bump chart (sigmoid flows) gg + geom_alluvium(aes(fill = country, colour = country), width = 1/4, alpha = 2/3, decreasing = FALSE, curve_type = "sigmoid") # time series line plot of refugees data, sorted by country gg + geom_alluvium(aes(fill = country, colour = country), decreasing = NA, width = 0, knot.pos = 0) # irregular spacing between axes of a continuous variable refugees_sub <- subset(alluvial::Refugees, year %in% c(2003, 2005, 2010, 2013)) gg <- ggplot(data = refugees_sub, aes(x = year, y = refugees, alluvium = country)) + theme_bw() + scale_fill_brewer(type = "qual", palette = "Set3") # proportional knot positioning (default) gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2) # constant knot positioning gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2, knot.pos = 1, knot.prop = FALSE) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2) # coarsely-segmented curves gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2, curve_type = "arctan", segments = 6) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2) # custom-ranged curves gg + geom_alluvium(aes(fill = country), alpha = .75, decreasing = FALSE, width = 1/2, curve_type = "arctan", curve_range = 1) + geom_stratum(aes(stratum = country), decreasing = FALSE, width = 1/2)
geom_flow
receives a dataset of the horizontal (x
) and vertical (y
,
ymin
, ymax
) positions of the lodes of an alluvial plot, the
intersections of the alluvia with the strata. It reconfigures these into
alluvial segments connecting pairs of corresponding lodes in adjacent strata
and plots filled x-splines between each such pair, using a provided knot
position parameter knot.pos
, and filled rectangles at either end, using a
provided width
.
geom_flow( mapping = NULL, data = NULL, stat = "flow", position = "identity", width = 1/3, knot.pos = 1/4, knot.prop = TRUE, curve_type = NULL, curve_range = NULL, segments = NULL, outline.type = "both", aes.flow = "forward", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) positions_to_flow( x0, x1, ymin0, ymax0, ymin1, ymax1, kp0, kp1, knot.prop, curve_type, curve_range, segments )
geom_flow( mapping = NULL, data = NULL, stat = "flow", position = "identity", width = 1/3, knot.pos = 1/4, knot.prop = TRUE, curve_type = NULL, curve_range = NULL, segments = NULL, outline.type = "both", aes.flow = "forward", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) positions_to_flow( x0, x1, ymin0, ymax0, ymin1, ymax1, kp0, kp1, knot.prop, curve_type, curve_range, segments )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data; override the default. |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
width |
Numeric; the width of each stratum, as a proportion of the distance between axes. Defaults to 1/3. |
knot.pos |
The horizontal distance of x-spline knots from each stratum
( |
knot.prop |
Logical; whether to interpret |
curve_type |
Character; the type of curve used to produce flows.
Defaults to |
curve_range |
For alternative |
segments |
The number of segments to be used in drawing each alternative curve (each curved boundary of each flow). If less than 3, will be silently changed to 3. |
outline.type |
Type of outline of each alluvium; one of |
aes.flow |
Character; how inter-lode flows assume aesthetics from lodes. Options are "forward" and "backward". |
na.rm |
Logical:
if |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Additional arguments passed to |
x0 , x1 , ymin0 , ymax0 , ymin1 , ymax1 , kp0 , kp1
|
Numeric corner and knot position data for the ribbon of a single flow. |
The helper function positions_to_flow()
takes the corner and knot positions
and curve parameters for a single flow as input and returns a data frame of
x
, y
, and shape
used by grid::xsplineGrob()
to render the flow.
geom_alluvium
, geom_flow
, geom_lode
, and geom_stratum
understand the
following aesthetics (required aesthetics are in bold):
x
y
ymin
ymax
alpha
colour
fill
linetype
size
group
group
is used internally; arguments are ignored.
Alluvium, flow, and lode geoms default to alpha = 0.5
. Learn more about
setting these aesthetics in vignette("ggplot2-specs", package = "ggplot2")
.
By default, geom_alluvium()
and geom_flow()
render flows between lodes as
filled regions between parallel x-splines. These graphical elements,
generated using grid::xsplineGrob()
, are
parameterized by the relative location of the knot (knot.pos
). They are
quick to render and clear to read, but users may prefer plots that use
differently-shaped ribbons.
A variety of such options are documented at, e.g., this easing functions cheat sheet and this blog post by Jeffrey Shaffer. Easing functions are
not (yet) used in ggalluvial, but several alternative curves are available.
Each is encoded as a continuous, increasing, bijective function from the unit
interval to itself, and each is rescaled so that its endpoints
meet the corresponding lodes. They are rendered piecewise-linearly, by
default using
segments = 48
. Summon each curve type by passing one of the
following strings to curve_type
:
"linear"
: , the unique degree-1 polynomial that takes
0 to 0 and 1 to 1
"cubic"
: , the unique
degree-3 polynomial that also is flat at both endpoints
"quintic"
: ,
the unique degree-5 polynomial that also has zero curvature
at both endpoints
"sine"
: the unique sinusoidal function that is flat at both
endpoints
"arctangent"
: the inverse tangent function, scaled and re-centered to the
unit interval from the interval centered at zero with
radius curve_range
"sigmoid"
: the sigmoid function, scaled and re-centered to the unit
interval from the interval centered at zero with radius
curve_range
Only the (default) "xspline"
option uses the knot.*
parameters, while
only the alternative curves use the segments
parameter, and only
"arctangent"
and "sigmoid"
use the curve_range
parameter. (Both are
ignored if not needed.) Larger values of curve_range
result in greater
compression and steeper slopes. The NULL
default will be changed to
2+sqrt(3)
for "arctangent"
and to 6
for "sigmoid"
.
These package-specific options set global values for curve_type
,
curve_range
, and segments
that will be defaulted to when not manually
set:
ggalluvial.curve_type
: defaults to "xspline"
.
ggalluvial.curve_range
: defaults to NA
, which triggers the
curve-specific default values.
ggalluvial.segments
: defaults to 48L
.
See base::options()
for how to use options.
The previously defunct parameters axis_width
and ribbon_bend
have been
discontinued. Use width
and knot.pos
instead.
ggplot2::layer()
for additional arguments and
stat_alluvium()
and
stat_flow()
for the corresponding stats.
Other alluvial geom layers:
geom_alluvium()
,
geom_lode()
,
geom_stratum()
# use of strata and labels ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age)) + geom_flow() + scale_x_discrete(limits = c("Class", "Sex", "Age")) + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + ggtitle("Alluvial plot of Titanic passenger demographic data") # use of facets, with quintic flows ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex)) + geom_flow(aes(fill = Age), width = .4, curve_type = "quintic") + geom_stratum(width = .4) + geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 3) + scale_x_discrete(limits = c("Class", "Sex")) + facet_wrap(~ Survived, scales = "fixed") # time series alluvia of WorldPhones data wph <- as.data.frame(as.table(WorldPhones)) names(wph) <- c("Year", "Region", "Telephones") ggplot(wph, aes(x = Year, alluvium = Region, y = Telephones)) + geom_flow(aes(fill = Region, colour = Region), width = 0, outline.type = "full") # treat 'Year' as a number rather than as a factor wph$Year <- as.integer(as.character(wph$Year)) ggplot(wph, aes(x = Year, alluvium = Region, y = Telephones)) + geom_flow(aes(fill = Region, colour = Region), width = 0, outline.type = "upper") # hold the knot positions fixed ggplot(wph, aes(x = Year, alluvium = Region, y = Telephones)) + geom_flow(aes(fill = Region, colour = Region), width = 0, outline.type = "lower", knot.prop = FALSE) # rightward flow aesthetics for vaccine survey data, with cubic flows data(vaccinations) vaccinations$response <- factor(vaccinations$response, rev(levels(vaccinations$response))) # annotate with proportional counts ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, fill = response)) + geom_lode() + geom_flow(curve_type = "cubic") + geom_stratum(alpha = 0) + geom_text(stat = "stratum", aes(label = round(after_stat(prop), 3))) # annotate fixed-width ribbons with counts ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, weight = freq, fill = response)) + geom_lode() + geom_flow(curve_type = "cubic") + geom_stratum(alpha = 0) + geom_text(stat = "flow", aes(label = after_stat(n), hjust = (after_stat(flow) == "to")))
# use of strata and labels ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age)) + geom_flow() + scale_x_discrete(limits = c("Class", "Sex", "Age")) + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + ggtitle("Alluvial plot of Titanic passenger demographic data") # use of facets, with quintic flows ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex)) + geom_flow(aes(fill = Age), width = .4, curve_type = "quintic") + geom_stratum(width = .4) + geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 3) + scale_x_discrete(limits = c("Class", "Sex")) + facet_wrap(~ Survived, scales = "fixed") # time series alluvia of WorldPhones data wph <- as.data.frame(as.table(WorldPhones)) names(wph) <- c("Year", "Region", "Telephones") ggplot(wph, aes(x = Year, alluvium = Region, y = Telephones)) + geom_flow(aes(fill = Region, colour = Region), width = 0, outline.type = "full") # treat 'Year' as a number rather than as a factor wph$Year <- as.integer(as.character(wph$Year)) ggplot(wph, aes(x = Year, alluvium = Region, y = Telephones)) + geom_flow(aes(fill = Region, colour = Region), width = 0, outline.type = "upper") # hold the knot positions fixed ggplot(wph, aes(x = Year, alluvium = Region, y = Telephones)) + geom_flow(aes(fill = Region, colour = Region), width = 0, outline.type = "lower", knot.prop = FALSE) # rightward flow aesthetics for vaccine survey data, with cubic flows data(vaccinations) vaccinations$response <- factor(vaccinations$response, rev(levels(vaccinations$response))) # annotate with proportional counts ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, fill = response)) + geom_lode() + geom_flow(curve_type = "cubic") + geom_stratum(alpha = 0) + geom_text(stat = "stratum", aes(label = round(after_stat(prop), 3))) # annotate fixed-width ribbons with counts ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, weight = freq, fill = response)) + geom_lode() + geom_flow(curve_type = "cubic") + geom_stratum(alpha = 0) + geom_text(stat = "flow", aes(label = after_stat(n), hjust = (after_stat(flow) == "to")))
geom_alluvium
receives a dataset of the horizontal (x
) and vertical (y
,
ymin
, ymax
) positions of the lodes of an alluvial plot, the
intersections of the alluvia with the strata. It plots rectangles for these
lodes of a provided width
.
geom_lode( mapping = NULL, data = NULL, stat = "alluvium", position = "identity", width = 1/3, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
geom_lode( mapping = NULL, data = NULL, stat = "alluvium", position = "identity", width = 1/3, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data; override the default. |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
width |
Numeric; the width of each stratum, as a proportion of the distance between axes. Defaults to 1/3. |
na.rm |
Logical:
if |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Additional arguments passed to |
geom_alluvium
, geom_flow
, geom_lode
, and geom_stratum
understand the
following aesthetics (required aesthetics are in bold):
x
y
ymin
ymax
alpha
colour
fill
linetype
size
group
group
is used internally; arguments are ignored.
Alluvium, flow, and lode geoms default to alpha = 0.5
. Learn more about
setting these aesthetics in vignette("ggplot2-specs", package = "ggplot2")
.
The previously defunct parameters axis_width
and ribbon_bend
have been
discontinued. Use width
and knot.pos
instead.
ggplot2::layer()
for additional arguments and
stat_alluvium()
and
stat_stratum()
for the corresponding stats.
Other alluvial geom layers:
geom_alluvium()
,
geom_flow()
,
geom_stratum()
# one axis ggplot(as.data.frame(Titanic), aes(y = Freq, axis = Class)) + geom_lode(aes(fill = Class, alpha = Survived)) + scale_x_discrete(limits = c("Class")) + scale_alpha_manual(values = c(.25, .75)) gg <- ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, fill = Survived)) # alluvia and lodes gg + geom_alluvium() + geom_lode() # lodes as strata gg + geom_alluvium() + geom_stratum(stat = "alluvium")
# one axis ggplot(as.data.frame(Titanic), aes(y = Freq, axis = Class)) + geom_lode(aes(fill = Class, alpha = Survived)) + scale_x_discrete(limits = c("Class")) + scale_alpha_manual(values = c(.25, .75)) gg <- ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, fill = Survived)) # alluvia and lodes gg + geom_alluvium() + geom_lode() # lodes as strata gg + geom_alluvium() + geom_stratum(stat = "alluvium")
geom_stratum
receives a dataset of the horizontal (x
) and vertical (y
,
ymin
, ymax
) positions of the strata of an alluvial plot. It plots
rectangles for these strata of a provided width
.
geom_stratum( mapping = NULL, data = NULL, stat = "stratum", position = "identity", show.legend = NA, inherit.aes = TRUE, width = 1/3, na.rm = FALSE, ... )
geom_stratum( mapping = NULL, data = NULL, stat = "stratum", position = "identity", show.legend = NA, inherit.aes = TRUE, width = 1/3, na.rm = FALSE, ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data; override the default. |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
width |
Numeric; the width of each stratum, as a proportion of the distance between axes. Defaults to 1/3. |
na.rm |
Logical:
if |
... |
Additional arguments passed to |
geom_alluvium
, geom_flow
, geom_lode
, and geom_stratum
understand the
following aesthetics (required aesthetics are in bold):
x
y
ymin
ymax
alpha
colour
fill
linetype
size
group
group
is used internally; arguments are ignored.
Alluvium, flow, and lode geoms default to alpha = 0.5
. Learn more about
setting these aesthetics in vignette("ggplot2-specs", package = "ggplot2")
.
The previously defunct parameters axis_width
and ribbon_bend
have been
discontinued. Use width
and knot.pos
instead.
ggplot2::layer()
for additional arguments and
stat_stratum()
for the corresponding stat.
Other alluvial geom layers:
geom_alluvium()
,
geom_flow()
,
geom_lode()
# full axis width ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, axis4 = Survived)) + geom_stratum(width = 1) + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age", "Survived")) # use of facets ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex)) + geom_flow(aes(fill = Survived)) + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex")) + facet_wrap(~ Age, scales = "free_y")
# full axis width ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, axis4 = Survived)) + geom_stratum(width = 1) + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age", "Survived")) # use of facets ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex)) + geom_flow(aes(fill = Survived)) + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex")) + facet_wrap(~ Age, scales = "free_y")
These functions control the order of lodes within strata in an alluvial
diagram. They are invoked by stat_alluvium()
and can be passed to
the lode.guidance
parameter.
lode_zigzag(n, i) lode_zagzig(n, i) lode_forward(n, i) lode_rightward(n, i) lode_backward(n, i) lode_leftward(n, i) lode_frontback(n, i) lode_rightleft(n, i) lode_backfront(n, i) lode_leftright(n, i)
lode_zigzag(n, i) lode_zagzig(n, i) lode_forward(n, i) lode_rightward(n, i) lode_backward(n, i) lode_leftward(n, i) lode_frontback(n, i) lode_rightleft(n, i) lode_backfront(n, i) lode_leftright(n, i)
n |
Numeric, a positive integer |
i |
Numeric, a positive integer at most |
Each function orders the numbers 1 through n
, starting at index
i
. The choice of function made in stat_alluvium()
determines the order in which the other axes contribute to the sorting of
lodes within each index axis. After starting at i
, the functions order
the remaining axes as follows:
zigzag
: Zigzag outward from i
, starting in the outward direction
zigzag
: Zigzag outward from i
, starting in the inward direction
forward
: Increasing order (alias rightward
)
backward
: Decreasing order (alias leftward
)
frontback
: Proceed forward from i
to n
, then backward to 1
(alias rightleft
)
backfront
: Proceed backward from i
to 1, then forward to n
(alias leftright
)
An extended discussion of how strata and lodes are arranged in alluvial
plots, including the effects of different lode guidance functions, can be
found in the vignette "The Order of the Rectangles" via
vignette("order-rectangles", package = "ggalluvial")
.
This data set follows the major curricula of 10 students across 8 academic semesters. Missing values indicate undeclared majors. The data were kindly contributed by Dario Bonaretti.
A data frame with 80 rows and 3 variables:
student
student identifier
semester
character tag for odd-numbered semesters
curriculum
declared major program
This function binds a dataset to itself along adjacent pairs of a key
variable. It is invoked by geom_flow()
to convert data in lodes
form to something similar to alluvia form.
self_adjoin( data, key, by = NULL, link = NULL, keep.x = NULL, keep.y = NULL, suffix = c(".x", ".y") )
self_adjoin( data, key, by = NULL, link = NULL, keep.x = NULL, keep.y = NULL, suffix = c(".x", ".y") )
data |
A data frame in lodes form (repeated measures data; see
|
key |
Column of |
by |
Character vector of variables to self-adjoin by; passed to
|
link |
Character vector of variables to adjoin. Will be replaced by
pairs of variables suffixed by |
keep.x , keep.y
|
Character vector of variables to associate with the
first (respectively, second) copy of |
suffix |
Suffixes to add to the adjoined |
self_adjoin
invokes dplyr::mutate-joins
functions in order to convert
a dataset with measures along a discrete key
variable into a dataset
consisting of column bindings of these measures (by any by
variables) along
adjacent values of key
.
Other alluvial data manipulation:
alluvial-data
# self-adjoin `majors` data data(majors) major_changes <- self_adjoin(majors, key = semester, by = "student", link = c("semester", "curriculum")) major_changes$change <- major_changes$curriculum.x == major_changes$curriculum.y head(major_changes) # self-adjoin `vaccinations` data data(vaccinations) vaccination_steps <- self_adjoin(vaccinations, key = survey, by = "subject", link = c("survey", "response"), keep.x = c("freq")) head(vaccination_steps) vaccination_steps <- self_adjoin(vaccinations, key = survey, by = "subject", link = c("survey", "response"), keep.x = c("freq"), keep.y = c("start_date", "end_date")) head(vaccination_steps)
# self-adjoin `majors` data data(majors) major_changes <- self_adjoin(majors, key = semester, by = "student", link = c("semester", "curriculum")) major_changes$change <- major_changes$curriculum.x == major_changes$curriculum.y head(major_changes) # self-adjoin `vaccinations` data data(vaccinations) vaccination_steps <- self_adjoin(vaccinations, key = survey, by = "subject", link = c("survey", "response"), keep.x = c("freq")) head(vaccination_steps) vaccination_steps <- self_adjoin(vaccinations, key = survey, by = "subject", link = c("survey", "response"), keep.x = c("freq"), keep.y = c("start_date", "end_date")) head(vaccination_steps)
Given a dataset with alluvial structure, stat_alluvium
calculates the
centroids (x
and y
) and heights (ymin
and ymax
) of the lodes, the
intersections of the alluvia with the strata. It leverages the group
aesthetic for plotting purposes (for now).
stat_alluvium( mapping = NULL, data = NULL, geom = "alluvium", position = "identity", decreasing = NULL, reverse = NULL, absolute = NULL, discern = FALSE, negate.strata = NULL, aggregate.y = NULL, cement.alluvia = NULL, lode.guidance = NULL, lode.ordering = NULL, aes.bind = NULL, infer.label = FALSE, min.y = NULL, max.y = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
stat_alluvium( mapping = NULL, data = NULL, geom = "alluvium", position = "identity", decreasing = NULL, reverse = NULL, absolute = NULL, discern = FALSE, negate.strata = NULL, aggregate.y = NULL, cement.alluvia = NULL, lode.guidance = NULL, lode.ordering = NULL, aes.bind = NULL, infer.label = FALSE, min.y = NULL, max.y = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use display the data; override the default. |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
decreasing |
Logical; whether to arrange the strata at each axis in the
order of the variable values ( |
reverse |
Logical; if |
absolute |
Logical; if some cases or strata are negative, whether to
arrange them (respecting |
discern |
Passed to |
negate.strata |
A vector of values of the |
aggregate.y |
Deprecated alias for |
cement.alluvia |
Logical value indicating whether to aggregate |
lode.guidance |
The function to prioritize the axis variables for
ordering the lodes within each stratum, or else a character string
identifying the function. Character options are "zigzag", "frontback",
"backfront", "forward", and "backward" (see |
lode.ordering |
Deprecated in favor of the |
aes.bind |
At what grouping level, if any, to prioritize differentiation
aesthetics when ordering the lodes within each stratum. Defaults to
|
infer.label |
Logical; whether to assign the |
min.y , max.y
|
Numeric; bounds on the heights of the strata to be
rendered. Use these bounds to exclude strata outside a certain range, for
example when labeling strata using |
na.rm |
Logical:
if |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Additional arguments passed to |
stat_alluvium
, stat_flow
, and stat_stratum
require one
of two sets of aesthetics:
x
and at least one of alluvium
and stratum
any number of axis[0-9]*
(axis1
, axis2
, etc.)
Use x
, alluvium
, and/or stratum
for data in lodes format
and axis[0-9]*
for data in alluvia format (see alluvial-data
).
Arguments to parameters inconsistent with the format will be ignored.
Additionally, each stat_*()
accepts the following optional
aesthetics:
y
weight
order
group
label
y
controls the heights of the alluvia,
and may be aggregated across equivalent observations.
weight
applies to the computed variables (see that section below)
but does not affect the positional aesthetics.
order
, recognized by stat_alluvium()
and stat_flow()
, is used to
arrange the lodes within each stratum. It tolerates duplicates and takes
precedence over the differentiation aesthetics (when aes.bind
is not
"none"
) and lode guidance with respect to the remaining axes. (It replaces
the deprecated parameter lode.ordering
.)
group
is used internally; arguments are ignored.
label
is used to label the strata or lodes and must take a unique value
across the observations within each stratum or lode.
These and any other aesthetics are aggregated as follows:
Numeric aesthetics, including y
, are summed.
Character and factor aesthetics, including label
,
are assigned to strata or lodes provided they take unique values across the
observations within each (and are otherwise assigned NA
).
These can be used with
ggplot2::after_stat()
to control aesthetic evaluation.
n
number of cases in lode
count
cumulative weight of lode
prop
weighted proportion of lode
stratum
value of variable used to define strata
deposit
order in which (signed) strata are deposited
lode
lode label distilled from alluvia
(stat_alluvium()
and stat_flow()
only)
flow
direction of flow "to"
or "from"
from its axis
(stat_flow()
only)
The numerical variables n
, count
, and prop
are calculated after the
data are grouped by x
and weighted by weight
(in addition to y
).
The integer variable deposit
is used internally to sort the data before
calculating heights. The character variable lode
is obtained from
alluvium
according to distill
.
stat_stratum
, stat_alluvium
, and stat_flow
order strata and lodes
according to the values of several parameters, which must be held fixed
across every layer in an alluvial plot. These package-specific options set
global values for these parameters that will be defaulted to when not
manually set:
ggalluvial.decreasing
(each stat_*
): defaults to NA
.
ggalluvial.reverse
(each stat_*
): defaults to TRUE
.
ggalluvial.absolute
(each stat_*
): defaults to TRUE
.
ggalluvial.cement.alluvia
(stat_alluvium
): defaults to FALSE
.
ggalluvial.lode.guidance
(stat_alluvium
): defaults to "zigzag"
.
ggalluvial.aes.bind
(stat_alluvium
and stat_flow
): defaults to
"none"
.
See base::options()
for how to use options.
The previously defunct parameters weight
and aggregate.wts
have been
discontinued. Use y
and cement.alluvia
instead.
ggplot2::layer()
for additional arguments and geom_alluvium()
,
geom_lode()
, and geom_flow()
for the corresponding geoms.
Other alluvial stat layers:
stat_flow()
,
stat_stratum()
# illustrate positioning ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, color = Survived)) + stat_stratum(geom = "errorbar") + geom_line(stat = "alluvium") + stat_alluvium(geom = "pointrange") + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age")) # lode ordering examples gg <- ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age)) + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age")) # use of lode controls gg + geom_flow(aes(fill = Survived, alpha = Sex), stat = "alluvium", lode.guidance = "forward") # prioritize aesthetic binding gg + geom_flow(aes(fill = Survived, alpha = Sex), stat = "alluvium", aes.bind = "alluvia", lode.guidance = "forward") # use of custom lode order gg + geom_flow(aes(fill = Survived, alpha = Sex, order = sample(x = 32)), stat = "alluvium") # use of custom luide guidance function lode_custom <- function(n, i) { stopifnot(n == 3) switch( i, `1` = 1:3, `2` = c(2, 3, 1), `3` = 3:1 ) } gg + geom_flow(aes(fill = Survived, alpha = Sex), stat = "alluvium", aes.bind = "flow", lode.guidance = lode_custom) # omit missing elements & reverse the `y` axis ggplot(ggalluvial::majors, aes(x = semester, stratum = curriculum, alluvium = student, y = 1)) + geom_alluvium(fill = "darkgrey", na.rm = TRUE) + geom_stratum(aes(fill = curriculum), color = NA, na.rm = TRUE) + theme_bw() + scale_y_reverse() # alluvium cementation examples gg <- ggplot(ggalluvial::majors, aes(x = semester, stratum = curriculum, alluvium = student, fill = curriculum)) + geom_stratum() # diagram with outlined alluvia and labels gg + geom_flow(stat = "alluvium", color = "black") + geom_text(aes(label = after_stat(lode)), stat = "alluvium") # cemented diagram with default distillation (first most common alluvium) gg + geom_flow(stat = "alluvium", color = "black", cement.alluvia = TRUE) + geom_text(aes(label = after_stat(lode)), stat = "alluvium", cement.alluvia = TRUE) # cemented diagram with custom label distillation gg + geom_flow(stat = "alluvium", color = "black", cement.alluvia = TRUE) + geom_text(aes(label = after_stat(lode)), stat = "alluvium", cement.alluvia = TRUE, distill = function(x) paste(x, collapse = "; ")) data(babynames, package = "babynames") # a discontiguous alluvium bn <- subset(babynames, prop >= .01 & sex == "F" & year > 1962 & year < 1968) ggplot(data = bn, aes(x = year, alluvium = name, y = prop)) + geom_alluvium(aes(fill = name, color = name == "Tammy"), decreasing = TRUE, show.legend = FALSE) + scale_color_manual(values = c("#00000000", "#000000")) # expanded to include missing values bn2 <- merge(bn, expand.grid(year = unique(bn$year), name = unique(bn$name)), all = TRUE) ggplot(data = bn2, aes(x = year, alluvium = name, y = prop)) + geom_alluvium(aes(fill = name, color = name == "Tammy"), decreasing = TRUE, show.legend = FALSE) + scale_color_manual(values = c("#00000000", "#000000")) # with missing values filled in with zeros bn2$prop[is.na(bn2$prop)] <- 0 ggplot(data = bn2, aes(x = year, alluvium = name, y = prop)) + geom_alluvium(aes(fill = name, color = name == "Tammy"), decreasing = TRUE, show.legend = FALSE) + scale_color_manual(values = c("#00000000", "#000000")) # use negative y values to encode deaths versus survivals titanic <- as.data.frame(Titanic) titanic <- transform(titanic, Lives = Freq * (-1) ^ (Survived == "No")) ggplot(subset(titanic, Class != "Crew"), aes(axis1 = Class, axis2 = Sex, axis3 = Age, y = Lives)) + geom_alluvium(aes(alpha = Survived, fill = Class), absolute = FALSE) + geom_stratum(absolute = FALSE) + geom_text(stat = "stratum", aes(label = after_stat(stratum)), absolute = FALSE) + scale_x_discrete(limits = c("Class", "Sex", "Age"), expand = c(.1, .05)) + scale_alpha_discrete(range = c(.25, .75), guide = "none") # faceting with common alluvia ggplot(titanic, aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age)) + facet_wrap(~ Survived) + geom_alluvium() + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) ggplot(transform(alluvial::Refugees, id = 1), aes(y = refugees, x = year, alluvium = id)) + facet_wrap(~ country) + geom_alluvium(alpha = .75, color = "darkgrey") + scale_x_continuous(breaks = seq(2004, 2012, 4))
# illustrate positioning ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, color = Survived)) + stat_stratum(geom = "errorbar") + geom_line(stat = "alluvium") + stat_alluvium(geom = "pointrange") + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age")) # lode ordering examples gg <- ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age)) + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age")) # use of lode controls gg + geom_flow(aes(fill = Survived, alpha = Sex), stat = "alluvium", lode.guidance = "forward") # prioritize aesthetic binding gg + geom_flow(aes(fill = Survived, alpha = Sex), stat = "alluvium", aes.bind = "alluvia", lode.guidance = "forward") # use of custom lode order gg + geom_flow(aes(fill = Survived, alpha = Sex, order = sample(x = 32)), stat = "alluvium") # use of custom luide guidance function lode_custom <- function(n, i) { stopifnot(n == 3) switch( i, `1` = 1:3, `2` = c(2, 3, 1), `3` = 3:1 ) } gg + geom_flow(aes(fill = Survived, alpha = Sex), stat = "alluvium", aes.bind = "flow", lode.guidance = lode_custom) # omit missing elements & reverse the `y` axis ggplot(ggalluvial::majors, aes(x = semester, stratum = curriculum, alluvium = student, y = 1)) + geom_alluvium(fill = "darkgrey", na.rm = TRUE) + geom_stratum(aes(fill = curriculum), color = NA, na.rm = TRUE) + theme_bw() + scale_y_reverse() # alluvium cementation examples gg <- ggplot(ggalluvial::majors, aes(x = semester, stratum = curriculum, alluvium = student, fill = curriculum)) + geom_stratum() # diagram with outlined alluvia and labels gg + geom_flow(stat = "alluvium", color = "black") + geom_text(aes(label = after_stat(lode)), stat = "alluvium") # cemented diagram with default distillation (first most common alluvium) gg + geom_flow(stat = "alluvium", color = "black", cement.alluvia = TRUE) + geom_text(aes(label = after_stat(lode)), stat = "alluvium", cement.alluvia = TRUE) # cemented diagram with custom label distillation gg + geom_flow(stat = "alluvium", color = "black", cement.alluvia = TRUE) + geom_text(aes(label = after_stat(lode)), stat = "alluvium", cement.alluvia = TRUE, distill = function(x) paste(x, collapse = "; ")) data(babynames, package = "babynames") # a discontiguous alluvium bn <- subset(babynames, prop >= .01 & sex == "F" & year > 1962 & year < 1968) ggplot(data = bn, aes(x = year, alluvium = name, y = prop)) + geom_alluvium(aes(fill = name, color = name == "Tammy"), decreasing = TRUE, show.legend = FALSE) + scale_color_manual(values = c("#00000000", "#000000")) # expanded to include missing values bn2 <- merge(bn, expand.grid(year = unique(bn$year), name = unique(bn$name)), all = TRUE) ggplot(data = bn2, aes(x = year, alluvium = name, y = prop)) + geom_alluvium(aes(fill = name, color = name == "Tammy"), decreasing = TRUE, show.legend = FALSE) + scale_color_manual(values = c("#00000000", "#000000")) # with missing values filled in with zeros bn2$prop[is.na(bn2$prop)] <- 0 ggplot(data = bn2, aes(x = year, alluvium = name, y = prop)) + geom_alluvium(aes(fill = name, color = name == "Tammy"), decreasing = TRUE, show.legend = FALSE) + scale_color_manual(values = c("#00000000", "#000000")) # use negative y values to encode deaths versus survivals titanic <- as.data.frame(Titanic) titanic <- transform(titanic, Lives = Freq * (-1) ^ (Survived == "No")) ggplot(subset(titanic, Class != "Crew"), aes(axis1 = Class, axis2 = Sex, axis3 = Age, y = Lives)) + geom_alluvium(aes(alpha = Survived, fill = Class), absolute = FALSE) + geom_stratum(absolute = FALSE) + geom_text(stat = "stratum", aes(label = after_stat(stratum)), absolute = FALSE) + scale_x_discrete(limits = c("Class", "Sex", "Age"), expand = c(.1, .05)) + scale_alpha_discrete(range = c(.25, .75), guide = "none") # faceting with common alluvia ggplot(titanic, aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age)) + facet_wrap(~ Survived) + geom_alluvium() + geom_stratum() + geom_text(stat = "stratum", aes(label = after_stat(stratum))) ggplot(transform(alluvial::Refugees, id = 1), aes(y = refugees, x = year, alluvium = id)) + facet_wrap(~ country) + geom_alluvium(alpha = .75, color = "darkgrey") + scale_x_continuous(breaks = seq(2004, 2012, 4))
Given a dataset with alluvial structure, stat_flow
calculates the centroids
(x
and y
) and heights (ymin
and ymax
) of the flows between each pair
of adjacent axes.
stat_flow( mapping = NULL, data = NULL, geom = "flow", position = "identity", decreasing = NULL, reverse = NULL, absolute = NULL, discern = FALSE, negate.strata = NULL, aes.bind = NULL, infer.label = FALSE, min.y = NULL, max.y = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
stat_flow( mapping = NULL, data = NULL, geom = "flow", position = "identity", decreasing = NULL, reverse = NULL, absolute = NULL, discern = FALSE, negate.strata = NULL, aes.bind = NULL, infer.label = FALSE, min.y = NULL, max.y = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use display the data; override the default. |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
decreasing |
Logical; whether to arrange the strata at each axis in the
order of the variable values ( |
reverse |
Logical; if |
absolute |
Logical; if some cases or strata are negative, whether to
arrange them (respecting |
discern |
Passed to |
negate.strata |
A vector of values of the |
aes.bind |
At what grouping level, if any, to prioritize differentiation
aesthetics when ordering the lodes within each stratum. Defaults to
|
infer.label |
Logical; whether to assign the |
min.y , max.y
|
Numeric; bounds on the heights of the strata to be
rendered. Use these bounds to exclude strata outside a certain range, for
example when labeling strata using |
na.rm |
Logical:
if |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Additional arguments passed to |
stat_alluvium
, stat_flow
, and stat_stratum
require one
of two sets of aesthetics:
x
and at least one of alluvium
and stratum
any number of axis[0-9]*
(axis1
, axis2
, etc.)
Use x
, alluvium
, and/or stratum
for data in lodes format
and axis[0-9]*
for data in alluvia format (see alluvial-data
).
Arguments to parameters inconsistent with the format will be ignored.
Additionally, each stat_*()
accepts the following optional
aesthetics:
y
weight
order
group
label
y
controls the heights of the alluvia,
and may be aggregated across equivalent observations.
weight
applies to the computed variables (see that section below)
but does not affect the positional aesthetics.
order
, recognized by stat_alluvium()
and stat_flow()
, is used to
arrange the lodes within each stratum. It tolerates duplicates and takes
precedence over the differentiation aesthetics (when aes.bind
is not
"none"
) and lode guidance with respect to the remaining axes. (It replaces
the deprecated parameter lode.ordering
.)
group
is used internally; arguments are ignored.
label
is used to label the strata or lodes and must take a unique value
across the observations within each stratum or lode.
These and any other aesthetics are aggregated as follows:
Numeric aesthetics, including y
, are summed.
Character and factor aesthetics, including label
,
are assigned to strata or lodes provided they take unique values across the
observations within each (and are otherwise assigned NA
).
These can be used with
ggplot2::after_stat()
to control aesthetic evaluation.
n
number of cases in lode
count
cumulative weight of lode
prop
weighted proportion of lode
stratum
value of variable used to define strata
deposit
order in which (signed) strata are deposited
lode
lode label distilled from alluvia
(stat_alluvium()
and stat_flow()
only)
flow
direction of flow "to"
or "from"
from its axis
(stat_flow()
only)
The numerical variables n
, count
, and prop
are calculated after the
data are grouped by x
and weighted by weight
(in addition to y
).
The integer variable deposit
is used internally to sort the data before
calculating heights. The character variable lode
is obtained from
alluvium
according to distill
.
stat_stratum
, stat_alluvium
, and stat_flow
order strata and lodes
according to the values of several parameters, which must be held fixed
across every layer in an alluvial plot. These package-specific options set
global values for these parameters that will be defaulted to when not
manually set:
ggalluvial.decreasing
(each stat_*
): defaults to NA
.
ggalluvial.reverse
(each stat_*
): defaults to TRUE
.
ggalluvial.absolute
(each stat_*
): defaults to TRUE
.
ggalluvial.cement.alluvia
(stat_alluvium
): defaults to FALSE
.
ggalluvial.lode.guidance
(stat_alluvium
): defaults to "zigzag"
.
ggalluvial.aes.bind
(stat_alluvium
and stat_flow
): defaults to
"none"
.
See base::options()
for how to use options.
The previously defunct parameters weight
and aggregate.wts
have been
discontinued. Use y
and cement.alluvia
instead.
ggplot2::layer()
for additional arguments and
geom_alluvium()
and
geom_flow()
for the corresponding geoms.
Other alluvial stat layers:
stat_alluvium()
,
stat_stratum()
# illustrate positioning ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, color = Survived)) + stat_stratum(geom = "errorbar") + geom_line(stat = "flow") + stat_flow(geom = "pointrange") + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age")) # alluvium--flow comparison data(vaccinations) gg <- ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, fill = response)) + geom_stratum(alpha = .5) + geom_text(aes(label = response), stat = "stratum") # rightward alluvial aesthetics for vaccine survey data gg + geom_flow(stat = "alluvium", lode.guidance = "forward") # memoryless flows for vaccine survey data gg + geom_flow() # size filter examples gg <- ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, alluvium = subject, fill = response, label = response)) + stat_stratum(alpha = .5) + geom_text(stat = "stratum") # omit small flows gg + geom_flow(min.y = 50) # omit large flows gg + geom_flow(max.y = 100) # negate missing entries ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, alluvium = subject, fill = response, label = response, alpha = response != "Missing")) + stat_stratum(negate.strata = "Missing") + geom_flow(negate.strata = "Missing") + geom_text(stat = "stratum", alpha = 1, negate.strata = "Missing") + scale_alpha_discrete(range = c(.2, .6)) + guides(alpha = "none") # aesthetics that vary betwween and within strata data(vaccinations) vaccinations$subgroup <- LETTERS[1:2][rbinom( n = length(unique(vaccinations$subject)), size = 1, prob = .5 ) + 1][vaccinations$subject] ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, fill = response, label = response)) + geom_flow(aes(alpha = subgroup)) + scale_alpha_discrete(range = c(1/3, 2/3)) + geom_stratum(alpha = .5) + geom_text(stat = "stratum") # can even set aesthetics that vary both ways ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, label = response)) + geom_flow(aes(fill = interaction(response, subgroup)), aes.bind = "flows") + scale_alpha_discrete(range = c(1/3, 2/3)) + geom_stratum(alpha = .5) + geom_text(stat = "stratum")
# illustrate positioning ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, color = Survived)) + stat_stratum(geom = "errorbar") + geom_line(stat = "flow") + stat_flow(geom = "pointrange") + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + scale_x_discrete(limits = c("Class", "Sex", "Age")) # alluvium--flow comparison data(vaccinations) gg <- ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, fill = response)) + geom_stratum(alpha = .5) + geom_text(aes(label = response), stat = "stratum") # rightward alluvial aesthetics for vaccine survey data gg + geom_flow(stat = "alluvium", lode.guidance = "forward") # memoryless flows for vaccine survey data gg + geom_flow() # size filter examples gg <- ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, alluvium = subject, fill = response, label = response)) + stat_stratum(alpha = .5) + geom_text(stat = "stratum") # omit small flows gg + geom_flow(min.y = 50) # omit large flows gg + geom_flow(max.y = 100) # negate missing entries ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, alluvium = subject, fill = response, label = response, alpha = response != "Missing")) + stat_stratum(negate.strata = "Missing") + geom_flow(negate.strata = "Missing") + geom_text(stat = "stratum", alpha = 1, negate.strata = "Missing") + scale_alpha_discrete(range = c(.2, .6)) + guides(alpha = "none") # aesthetics that vary betwween and within strata data(vaccinations) vaccinations$subgroup <- LETTERS[1:2][rbinom( n = length(unique(vaccinations$subject)), size = 1, prob = .5 ) + 1][vaccinations$subject] ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, fill = response, label = response)) + geom_flow(aes(alpha = subgroup)) + scale_alpha_discrete(range = c(1/3, 2/3)) + geom_stratum(alpha = .5) + geom_text(stat = "stratum") # can even set aesthetics that vary both ways ggplot(vaccinations, aes(x = survey, stratum = response, alluvium = subject, y = freq, label = response)) + geom_flow(aes(fill = interaction(response, subgroup)), aes.bind = "flows") + scale_alpha_discrete(range = c(1/3, 2/3)) + geom_stratum(alpha = .5) + geom_text(stat = "stratum")
Given a dataset with alluvial structure, stat_stratum
calculates the
centroids (x
and y
) and heights (ymin
and ymax
) of the strata at each
axis.
stat_stratum( mapping = NULL, data = NULL, geom = "stratum", position = "identity", decreasing = NULL, reverse = NULL, absolute = NULL, discern = FALSE, distill = "first", negate.strata = NULL, infer.label = FALSE, label.strata = NULL, min.y = NULL, max.y = NULL, min.height = NULL, max.height = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
stat_stratum( mapping = NULL, data = NULL, geom = "stratum", position = "identity", decreasing = NULL, reverse = NULL, absolute = NULL, discern = FALSE, distill = "first", negate.strata = NULL, infer.label = FALSE, label.strata = NULL, min.y = NULL, max.y = NULL, min.height = NULL, max.height = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use display the data; override the default. |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
decreasing |
Logical; whether to arrange the strata at each axis in the
order of the variable values ( |
reverse |
Logical; if |
absolute |
Logical; if some cases or strata are negative, whether to
arrange them (respecting |
discern |
Passed to |
distill |
A function (or its name) to be used to distill alluvium values
to a single lode label, accessible via
|
negate.strata |
A vector of values of the |
infer.label |
Logical; whether to assign the |
label.strata |
Defunct; alias for |
min.y , max.y
|
Numeric; bounds on the heights of the strata to be
rendered. Use these bounds to exclude strata outside a certain range, for
example when labeling strata using |
min.height , max.height
|
Deprecated aliases for |
na.rm |
Logical:
if |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Additional arguments passed to |
stat_alluvium
, stat_flow
, and stat_stratum
require one
of two sets of aesthetics:
x
and at least one of alluvium
and stratum
any number of axis[0-9]*
(axis1
, axis2
, etc.)
Use x
, alluvium
, and/or stratum
for data in lodes format
and axis[0-9]*
for data in alluvia format (see alluvial-data
).
Arguments to parameters inconsistent with the format will be ignored.
Additionally, each stat_*()
accepts the following optional
aesthetics:
y
weight
order
group
label
y
controls the heights of the alluvia,
and may be aggregated across equivalent observations.
weight
applies to the computed variables (see that section below)
but does not affect the positional aesthetics.
order
, recognized by stat_alluvium()
and stat_flow()
, is used to
arrange the lodes within each stratum. It tolerates duplicates and takes
precedence over the differentiation aesthetics (when aes.bind
is not
"none"
) and lode guidance with respect to the remaining axes. (It replaces
the deprecated parameter lode.ordering
.)
group
is used internally; arguments are ignored.
label
is used to label the strata or lodes and must take a unique value
across the observations within each stratum or lode.
These and any other aesthetics are aggregated as follows:
Numeric aesthetics, including y
, are summed.
Character and factor aesthetics, including label
,
are assigned to strata or lodes provided they take unique values across the
observations within each (and are otherwise assigned NA
).
These can be used with
ggplot2::after_stat()
to control aesthetic evaluation.
n
number of cases in lode
count
cumulative weight of lode
prop
weighted proportion of lode
stratum
value of variable used to define strata
deposit
order in which (signed) strata are deposited
lode
lode label distilled from alluvia
(stat_alluvium()
and stat_flow()
only)
flow
direction of flow "to"
or "from"
from its axis
(stat_flow()
only)
The numerical variables n
, count
, and prop
are calculated after the
data are grouped by x
and weighted by weight
(in addition to y
).
The integer variable deposit
is used internally to sort the data before
calculating heights. The character variable lode
is obtained from
alluvium
according to distill
.
stat_stratum
, stat_alluvium
, and stat_flow
order strata and lodes
according to the values of several parameters, which must be held fixed
across every layer in an alluvial plot. These package-specific options set
global values for these parameters that will be defaulted to when not
manually set:
ggalluvial.decreasing
(each stat_*
): defaults to NA
.
ggalluvial.reverse
(each stat_*
): defaults to TRUE
.
ggalluvial.absolute
(each stat_*
): defaults to TRUE
.
ggalluvial.cement.alluvia
(stat_alluvium
): defaults to FALSE
.
ggalluvial.lode.guidance
(stat_alluvium
): defaults to "zigzag"
.
ggalluvial.aes.bind
(stat_alluvium
and stat_flow
): defaults to
"none"
.
See base::options()
for how to use options.
The previously defunct parameters weight
and aggregate.wts
have been
discontinued. Use y
and cement.alluvia
instead.
ggplot2::layer()
for additional arguments and geom_stratum()
for
the corresponding geom.
Other alluvial stat layers:
stat_alluvium()
,
stat_flow()
data(vaccinations) # only `stratum` assignment is necessary to generate strata ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, fill = response)) + stat_stratum(width = .5) # lode data, positioning with y labels ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, alluvium = subject, label = after_stat(count))) + stat_stratum(geom = "errorbar") + geom_text(stat = "stratum") # alluvium data, positioning with stratum labels ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, axis4 = Survived)) + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + stat_stratum(geom = "errorbar") + scale_x_discrete(limits = c("Class", "Sex", "Age", "Survived")) # omit labels for strata outside a y range ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, fill = response, label = response)) + stat_stratum(width = .5) + geom_text(stat = "stratum", min.y = 100) # date-valued axis variables ggplot(vaccinations, aes(x = end_date, y = freq, stratum = response, alluvium = subject, fill = response)) + stat_alluvium(geom = "flow", lode.guidance = "forward", width = 30) + stat_stratum(width = 30) + labs(x = "Survey date", y = "Number of respondents") admissions <- as.data.frame(UCBAdmissions) admissions <- transform(admissions, Count = Freq * (-1) ^ (Admit == "Rejected")) # use negative y values to encode rejection versus acceptance ggplot(admissions, aes(y = Count, axis1 = Dept, axis2 = Gender)) + geom_alluvium(aes(fill = Dept), width = 1/12) + geom_stratum(width = 1/12, fill = "black", color = "grey") + geom_label(stat = "stratum", aes(label = after_stat(stratum)), min.y = 200) + scale_x_discrete(limits = c("Department", "Gender"), expand = c(.05, .05)) # computed variable 'deposit' indicates order of each signed stratum ggplot(admissions, aes(y = Count, axis1 = Dept, axis2 = Gender)) + geom_alluvium(aes(fill = Dept), width = 1/12) + geom_stratum(width = 1/12, fill = "black", color = "grey") + geom_text(stat = "stratum", aes(label = after_stat(deposit)), color = "white") + scale_x_discrete(limits = c("Department", "Gender"), expand = c(.05, .05)) # fixed-width strata with acceptance and rejection totals ggplot(admissions, aes(y = sign(Count), weight = Count, axis1 = Dept, axis2 = Gender)) + geom_alluvium(aes(fill = Dept), width = 1/8) + geom_stratum(width = 1/8, fill = "black", color = "grey") + geom_text(stat = "stratum", aes(label = paste0(stratum, ifelse(nchar(as.character(stratum)) == 1L, ": ", "\n"), after_stat(n))), color = "white", size = 3) + scale_x_discrete(limits = c("Department", "Gender"), expand = c(.05, .05))
data(vaccinations) # only `stratum` assignment is necessary to generate strata ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, fill = response)) + stat_stratum(width = .5) # lode data, positioning with y labels ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, alluvium = subject, label = after_stat(count))) + stat_stratum(geom = "errorbar") + geom_text(stat = "stratum") # alluvium data, positioning with stratum labels ggplot(as.data.frame(Titanic), aes(y = Freq, axis1 = Class, axis2 = Sex, axis3 = Age, axis4 = Survived)) + geom_text(stat = "stratum", aes(label = after_stat(stratum))) + stat_stratum(geom = "errorbar") + scale_x_discrete(limits = c("Class", "Sex", "Age", "Survived")) # omit labels for strata outside a y range ggplot(vaccinations, aes(y = freq, x = survey, stratum = response, fill = response, label = response)) + stat_stratum(width = .5) + geom_text(stat = "stratum", min.y = 100) # date-valued axis variables ggplot(vaccinations, aes(x = end_date, y = freq, stratum = response, alluvium = subject, fill = response)) + stat_alluvium(geom = "flow", lode.guidance = "forward", width = 30) + stat_stratum(width = 30) + labs(x = "Survey date", y = "Number of respondents") admissions <- as.data.frame(UCBAdmissions) admissions <- transform(admissions, Count = Freq * (-1) ^ (Admit == "Rejected")) # use negative y values to encode rejection versus acceptance ggplot(admissions, aes(y = Count, axis1 = Dept, axis2 = Gender)) + geom_alluvium(aes(fill = Dept), width = 1/12) + geom_stratum(width = 1/12, fill = "black", color = "grey") + geom_label(stat = "stratum", aes(label = after_stat(stratum)), min.y = 200) + scale_x_discrete(limits = c("Department", "Gender"), expand = c(.05, .05)) # computed variable 'deposit' indicates order of each signed stratum ggplot(admissions, aes(y = Count, axis1 = Dept, axis2 = Gender)) + geom_alluvium(aes(fill = Dept), width = 1/12) + geom_stratum(width = 1/12, fill = "black", color = "grey") + geom_text(stat = "stratum", aes(label = after_stat(deposit)), color = "white") + scale_x_discrete(limits = c("Department", "Gender"), expand = c(.05, .05)) # fixed-width strata with acceptance and rejection totals ggplot(admissions, aes(y = sign(Count), weight = Count, axis1 = Dept, axis2 = Gender)) + geom_alluvium(aes(fill = Dept), width = 1/8) + geom_stratum(width = 1/8, fill = "black", color = "grey") + geom_text(stat = "stratum", aes(label = paste0(stratum, ifelse(nchar(as.character(stratum)) == 1L, ": ", "\n"), after_stat(n))), color = "white", size = 3) + scale_x_discrete(limits = c("Department", "Gender"), expand = c(.05, .05))
This data set is aggregated from three RAND American Life Panel (ALP) surveys that asked respondents their probability of vaccinating for influenza. Their responses were discretized to "Never" (0%), "Always" (100%), or "Sometimes" (any other value). After merging, missing responses were coded as "Missing" and respondents were grouped and counted by all three coded responses. The pre-processed data were kindly contributed by Raffaele Vardavas, and the complete surveys are freely available at the ALP website.
vaccinations
vaccinations
A data frame with 117 rows and 5 variables:
freq
number of respondents represented in each row
subject
identifier linking respondents across surveys
survey
survey designation from the ALP website
start_date
start date of survey
end_date
end date of survey
response
discretized probability of vaccinating for influenza