The {ggalluvial} package is a {ggplot2} extension for producing
alluvial plots in a {tidyverse} framework. The
design and functionality were originally inspired by the {alluvial} package and
have benefitted from the feedback of many users. This vignette
Unlike most alluvial and related diagrams, the plots produced by
{ggalluvial} are uniquely determined by the data set and statistical
transformation. The distinction is detailed in this blog
post.
Many other resources exist for visualizing categorical data in R,
including several more basic plot types that are likely to more
accurately convey proportions to viewers when the data are not so
structured as to warrant an alluvial plot. In particular, check out
Michael Friendly’s {vcd}
and {vcdExtra} packages for a variety of statistically-motivated
categorical data visualization techniques, Hadley Wickham’s {productplots} package
and Haley Jeppson and Heike Hofmann’s descendant {ggmosaic}
package for product or mosaic plots, and Nicholas Hamilton’s {ggtern} package for ternary
coordinates. Other related packages are mentioned below.
Alluvial plots
Here’s a quintessential alluvial plot:
The next section details how the elements of this image encode
information about the underlying dataset. For now, we use the image as a
point of reference to define the following elements of a typical
alluvial plot:
- An axis is a dimension (variable) along which the data are
vertically arranged at a fixed horizontal position. The plot above uses
three categorical axes:
Class, Sex, and
Age.
- The groups at each axis are depicted as opaque blocks called
strata. For example, the
Class axis contains four
strata: 1st, 2nd, 3rd, and
Crew.
- Horizontal (x-) splines called alluvia span the width of
the plot. In this plot, each alluvium corresponds to a fixed value of
each axis variable, indicated by its vertical position at the axis, as
well as of the
Survived variable, indicated by its fill
color.
- The segments of the alluvia between pairs of adjacent axes are
flows.
- The alluvia intersect the strata at lodes. The lodes are
not visualized in the above plot, but they can be inferred as filled
rectangles extending the flows through the strata at each end of the
plot or connecting the flows on either side of the center stratum.
As the examples in the next section will demonstrate, which of these
elements are incorporated into an alluvial plot depends on both how the
underlying data is structured and what the creator wants the plot to
communicate.
Alluvial data
{ggalluvial} recognizes two formats of “alluvial data”, treated in
detail in the following subsections, but which basically correspond to
the “wide” and “long” formats of categorical repeated measures data. A
third, tabular (or array), form is popular for storing data with
multiple categorical dimensions, such as the Titanic and
UCBAdmissions datasets. For consistency with tidy data principles
and {ggplot2} conventions, {ggalluvial} does not accept tabular input;
base::as.data.frame() converts such an array to an
acceptable data frame.
Alluvia (wide) format
The wide format reflects the visual arrangement of an alluvial plot,
but “untwisted”: Each row corresponds to a cohort of observations that
take a specific value at each variable, and each variable has its own
column. An additional column contains the quantity of each row, e.g. the
number of observational units in the cohort, which may be used to
control the heights of the strata. Basically, the wide format consists of
one row per alluvium. This is the format into which the base
function as.data.frame() transforms a frequency table, for
instance the 3-dimensional UCBAdmissions dataset:
head(as.data.frame(UCBAdmissions), n = 12)
## Admit Gender Dept Freq
## 1 Admitted Male A 512
## 2 Rejected Male A 313
## 3 Admitted Female A 89
## 4 Rejected Female A 19
## 5 Admitted Male B 353
## 6 Rejected Male B 207
## 7 Admitted Female B 17
## 8 Rejected Female B 8
## 9 Admitted Male C 120
## 10 Rejected Male C 205
## 11 Admitted Female C 202
## 12 Rejected Female C 391
is_alluvia_form(as.data.frame(UCBAdmissions), axes = 1:3, silent = TRUE)
## [1] TRUE
This format is inherited from the first release of {ggalluvial},
which modeled it after usage in {alluvial}: The user declares any number
of axis variables, which stat_alluvium() and
stat_stratum() recognize and process in a consistent
way:
ggplot(as.data.frame(UCBAdmissions),
aes(y = Freq, axis1 = Gender, axis2 = Dept)) +
geom_alluvium(aes(fill = Admit), width = 1/12) +
geom_stratum(width = 1/12, fill = "black", color = "grey") +
geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(limits = c("Gender", "Dept"), expand = c(.05, .05)) +
scale_fill_brewer(type = "qual", palette = "Set1") +
ggtitle("UC Berkeley admissions and rejections, by sex and department")
An important feature of these plots is the meaningfulness of the
vertical axis: No gaps are inserted between the strata, so the total
height of the plot reflects the cumulative quantity of the observations.
The plots produced by {ggalluvial} conform (somewhat; keep reading) to
the “grammar of graphics” principles of {ggplot2}, and this prevents
users from producing “free-floating” visualizations like the Sankey
diagrams showcased here.
{ggalluvial} parameters and native {ggplot2} functionality can also
produce parallel sets
plots, illustrated here using the HairEyeColor dataset:
ggplot(as.data.frame(HairEyeColor),
aes(y = Freq,
axis1 = Hair, axis2 = Eye, axis3 = Sex)) +
geom_alluvium(aes(fill = Eye),
width = 1/8, knot.pos = 0, reverse = FALSE) +
scale_fill_manual(values = c(Brown = "#70493D", Hazel = "#E2AC76",
Green = "#3F752B", Blue = "#81B0E4")) +
guides(fill = "none") +
geom_stratum(alpha = .25, width = 1/8, reverse = FALSE) +
geom_text(stat = "stratum", aes(label = after_stat(stratum)),
reverse = FALSE) +
scale_x_continuous(breaks = 1:3, labels = c("Hair", "Eye", "Sex")) +
coord_flip() +
ggtitle("Eye colors of 592 subjects, by sex and hair color")
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.
(The warning is due to the “Hair” and “Eye” axes having the value
“Brown” in common.)
This format and functionality are useful for many applications and
will be retained in future versions. They also involve some conspicuous
deviations from {ggplot2} norms:
- The
axis[0-9]* position aesthetics are non-standard:
they are not an explicit set of parameters but a family based on a
regular expression pattern; and at least one, but no specific one, is
required.
stat_alluvium() ignores any argument to the
group aesthetic; instead,
StatAlluvium$compute_panel() uses group to
link the rows of the internally-transformed dataset that correspond to
the same alluvium.- The horizontal axis must be manually corrected (using
scale_x_discrete() or scale_x_continuous()) to
reflect the implicit categorical variable identifying the axis.
Furthermore, format aesthetics like fill are necessarily
fixed for each alluvium; they cannot, for example, change from axis to
axis according to the value taken at each. This means that, although
they can reproduce the branching-tree structure of parallel sets, this
format cannot be used to produce alluvial plots with color schemes such
as those featured here
(“Controlling colors”), which are “reset” at each axis.
Note also that the stratum variable produced by
stat_stratum() (called by geom_text()) is
computed during the statistical transformation and must be recovered
using after_stat() as a calculated
aesthetic.
Lodes (long) format
The long format recognized by {ggalluvial} contains one row per
lode, and can be understood as the result of “gathering” (in a
deprecated {dplyr} sense) or “pivoting” (in the Microsoft Excel or
current {dplyr} sense) the axis columns of a dataset in the alluvia
format into a key-value pair of columns encoding the axis as the key and
the stratum as the value. This format requires an additional indexing
column that links the rows corresponding to a common cohort, i.e. the
lodes of a single alluvium:
UCB_lodes <- to_lodes_form(as.data.frame(UCBAdmissions),
axes = 1:3,
id = "Cohort")
head(UCB_lodes, n = 12)
## Freq Cohort x stratum
## 1 512 1 Admit Admitted
## 2 313 2 Admit Rejected
## 3 89 3 Admit Admitted
## 4 19 4 Admit Rejected
## 5 353 5 Admit Admitted
## 6 207 6 Admit Rejected
## 7 17 7 Admit Admitted
## 8 8 8 Admit Rejected
## 9 120 9 Admit Admitted
## 10 205 10 Admit Rejected
## 11 202 11 Admit Admitted
## 12 391 12 Admit Rejected
is_lodes_form(UCB_lodes, key = x, value = stratum, id = Cohort, silent = TRUE)
## [1] TRUE
The functions that convert data between wide (alluvia) and long
(lodes) format include several parameters that help preserve ancillary
information. See help("alluvial-data") for examples.
The same stat and geom can receive data in this format using a
different set of positional aesthetics, also specific to
{ggalluvial}:
x, the “key” variable indicating the axis to which the
row corresponds, which are to be arranged along the horizontal
axis;stratum, the “value” taken by the axis variable
indicated by x; andalluvium, the indexing scheme that links the rows of a
single alluvium.
Heights can vary from axis to axis, allowing users to produce bump
charts like those showcased here. In these cases, the
strata contain no more information than the alluvia and often are not
plotted. For convenience, both stat_alluvium() and
stat_flow() will accept arguments for x and
alluvium even if none is given for stratum. As an
example, we can group countries in the Refugees dataset by
region, in order to compare refugee volumes at different scales:
data(Refugees, package = "alluvial")
country_regions <- c(
Afghanistan = "Middle East",
Burundi = "Central Africa",
`Congo DRC` = "Central Africa",
Iraq = "Middle East",
Myanmar = "Southeast Asia",
Palestine = "Middle East",
Somalia = "Horn of Africa",
Sudan = "Central Africa",
Syria = "Middle East",
Vietnam = "Southeast Asia"
)
Refugees$region <- country_regions[Refugees$country]
ggplot(data = Refugees,
aes(x = year, y = refugees, alluvium = country)) +
geom_alluvium(aes(fill = country, colour = country),
alpha = .75, decreasing = FALSE) +
scale_x_continuous(breaks = seq(2003, 2013, 2)) +
theme_bw() +
theme(axis.text.x = element_text(angle = -30, hjust = 0)) +
scale_fill_brewer(type = "qual", palette = "Set3") +
scale_color_brewer(type = "qual", palette = "Set3") +
facet_wrap(~ region, scales = "fixed") +
ggtitle("refugee volume by country and region of origin")
The format allows us to assign aesthetics that change from axis to
axis along the same alluvium, which is useful for repeated measures
datasets. This requires generating a separate graphical object for each
flow, as implemented in geom_flow(). The plot below uses a
set of (changes to) students’ academic curricula over the course of
several semesters. Since geom_flow() calls
stat_flow() by default (see the next example), we override
it with stat_alluvium() in order to track each student
across all semesters:
data(majors)
majors$curriculum <- as.factor(majors$curriculum)
ggplot(majors,
aes(x = semester, stratum = curriculum, alluvium = student,
fill = curriculum, label = curriculum)) +
scale_fill_brewer(type = "qual", palette = "Set2") +
geom_flow(stat = "alluvium", lode.guidance = "frontback",
color = "darkgray") +
geom_stratum() +
theme(legend.position = "bottom") +
ggtitle("student curricula across several semesters")
The stratum heights y are unspecified, so each row is
given unit height. This example demonstrates one way {ggalluvial}
handles missing data. The alternative is to set the parameter
na.rm to TRUE. Missing data handling
(specifically, the order of the strata) also depends on whether the
stratum variable is character or factor/numeric.
Finally, lode format gives us the option to aggregate the flows
between adjacent axes, which may be appropriate when the transitions
between adjacent axes are of primary importance. We can demonstrate this
option on data from the influenza vaccination surveys conducted by the
RAND American Life Panel. The
data, including one question from each of three surveys, has been
aggregated by response profile: Each “subject” (mapped to
alluvium) actually represents a cohort of subjects who
responded the same way on all three questions, and the size of each
cohort (mapped to y) is recorded in “freq”.
data(vaccinations)
vaccinations <- transform(vaccinations,
response = factor(response, rev(levels(response))))
ggplot(vaccinations,
aes(x = survey, stratum = response, alluvium = subject,
y = freq,
fill = response, label = response)) +
scale_x_discrete(expand = c(.1, .1)) +
geom_flow() +
geom_stratum(alpha = .5) +
geom_text(stat = "stratum", size = 3) +
theme(legend.position = "none") +
ggtitle("vaccination survey responses at three points in time")
This plot ignores any continuity between the flows between axes. This
“memoryless” statistical transformation yields a less cluttered plot, in
which at most one flow proceeds from each stratum at one axis to each
stratum at the next, but at the cost of being able to track each cohort
across the entire plot.