Alluvial Plots
Alluvial and Sankey Diagrams
The aforementioned plots are methods for visualising the flow of data through a stream of markers. I was motivated to show this because enough of you deal in orders, tickets, and the like the flow visualisation of a system might prove of use. I will work with a familiar dataset. These are data on Admissions at the University of California Berkeley. The data exist as an internal R file in tabular form.
library(tidyverse)
library(ggalluvial) # if this is not installed, install.packages("ggalluvial")
data("UCBAdmissions") # This dataset is built in as a set of tables.
UCBAdmissions # What does it look like?## , , Dept = A
##
## Gender
## Admit Male Female
## Admitted 512 89
## Rejected 313 19
##
## , , Dept = B
##
## Gender
## Admit Male Female
## Admitted 353 17
## Rejected 207 8
##
## , , Dept = C
##
## Gender
## Admit Male Female
## Admitted 120 202
## Rejected 205 391
##
## , , Dept = D
##
## Gender
## Admit Male Female
## Admitted 138 131
## Rejected 279 244
##
## , , Dept = E
##
## Gender
## Admit Male Female
## Admitted 53 94
## Rejected 138 299
##
## , , Dept = F
##
## Gender
## Admit Male Female
## Admitted 22 24
## Rejected 351 317UCBADF <- data.frame(UCBAdmissions) # Force it into a data.frame
UCBADF # This is what the data structure needs to look like.## 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
## 13 Admitted Male D 138
## 14 Rejected Male D 279
## 15 Admitted Female D 131
## 16 Rejected Female D 244
## 17 Admitted Male E 53
## 18 Rejected Male E 138
## 19 Admitted Female E 94
## 20 Rejected Female E 299
## 21 Admitted Male F 22
## 22 Rejected Male F 351
## 23 Admitted Female F 24
## 24 Rejected Female F 317An Alluvial
This is the tidy version that we worked with at the individual level. To make this code work, change the below locations to import the same data.
load(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/UCBtidy.RData"))
head(DiscriminationUCB)## M.F Admit Dept
## 1 Male Yes A
## 2 Male Yes A
## 3 Male Yes A
## 4 Male Yes A
## 5 Male Yes A
## 6 Male Yes ATo put this data in a table, using the %>% pipe operator, we will pass the tidy data, group it by the elements of the alluvial, and then generate the counts.
DUCBT <- DiscriminationUCB %>% group_by(M.F,Dept,Admit) %>% summarise(count = n()) %>% ungroup()
DUCBT## # A tibble: 24 x 4
## M.F Dept Admit count
## <fct> <fct> <fct> <int>
## 1 Female A No 19
## 2 Female A Yes 89
## 3 Female B No 8
## 4 Female B Yes 17
## 5 Female C No 391
## 6 Female C Yes 202
## 7 Female D No 244
## 8 Female D Yes 131
## 9 Female E No 299
## 10 Female E Yes 94
## # … with 14 more rowsggalluvial()
The alluvial requires an additional package ggalluvial. We can install it through
install.packages("ggalluvial")What can it do? It needs data. The y axis is always the total counts in the cells. Then we set axes with a number after to show the phases from left to right. So here, axis1 will be gender and axis two will be Department. Admitted and non-admitted students flowed with colors depicting them move through the system. We want to track them by their admitted status. The alluvial itself has y as Frequency and the various axis* as the phases to track. The outcome of interest enters the fill so that color shows the outcome of interest flowing through the strata.
With the system data
This is the vignette solution to these data with the package. Extending it to any data is a two step process.
UCBADF %>% ggplot(.,
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", label.strata = TRUE) +
scale_x_discrete(limits = c("Gender", "Dept"), expand = c(.05, .05)) + # Fix the x axis
scale_fill_brewer(type = "qual", palette = "Set1") + # Give it nice colors
ggtitle("UC Berkeley admissions and rejections, by sex and department") # give it a title
A simple one [or as simple as I can]
A lot of the code is just prettying. The most basic plot needs this:
ggplot(UCBADF, # plot the data
aes(y = Freq, axis1 = Gender, axis2 = Dept)) + # what are the named axes
geom_alluvium(aes(fill = Admit), width = 1/12) + # what variable will fill the paths; Admission here.
geom_stratum(width = 1/12, fill = "black", color = "grey") + # This set the strata that our people will move through The one 12 is 12 combinations; the two colors here dfine the background and text for the labels.
geom_label(stat = "stratum", label.strata = TRUE) # This labels them.
Same with our data.
ggplot(DUCBT,
aes(y = count, axis1 = M.F, axis2 = Dept)) +
geom_alluvium(aes(fill = Admit), width = 1/12) +
geom_stratum(width = 1/12, fill = "black", color = "grey") +
geom_label(stat = "stratum", label.strata = TRUE) 
The Titanic: Multiple Phases
data("Titanic")
Titanic## , , Age = Child, Survived = No
##
## Sex
## Class Male Female
## 1st 0 0
## 2nd 0 0
## 3rd 35 17
## Crew 0 0
##
## , , Age = Adult, Survived = No
##
## Sex
## Class Male Female
## 1st 118 4
## 2nd 154 13
## 3rd 387 89
## Crew 670 3
##
## , , Age = Child, Survived = Yes
##
## Sex
## Class Male Female
## 1st 5 1
## 2nd 11 13
## 3rd 13 14
## Crew 0 0
##
## , , Age = Adult, Survived = Yes
##
## Sex
## Class Male Female
## 1st 57 140
## 2nd 14 80
## 3rd 75 76
## Crew 192 20TDF <- as.data.frame(Titanic)
TDF## Class Sex Age Survived Freq
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 3rd Male Child No 35
## 4 Crew Male Child No 0
## 5 1st Female Child No 0
## 6 2nd Female Child No 0
## 7 3rd Female Child No 17
## 8 Crew Female Child No 0
## 9 1st Male Adult No 118
## 10 2nd Male Adult No 154
## 11 3rd Male Adult No 387
## 12 Crew Male Adult No 670
## 13 1st Female Adult No 4
## 14 2nd Female Adult No 13
## 15 3rd Female Adult No 89
## 16 Crew Female Adult No 3
## 17 1st Male Child Yes 5
## 18 2nd Male Child Yes 11
## 19 3rd Male Child Yes 13
## 20 Crew Male Child Yes 0
## 21 1st Female Child Yes 1
## 22 2nd Female Child Yes 13
## 23 3rd Female Child Yes 14
## 24 Crew Female Child Yes 0
## 25 1st Male Adult Yes 57
## 26 2nd Male Adult Yes 14
## 27 3rd Male Adult Yes 75
## 28 Crew Male Adult Yes 192
## 29 1st Female Adult Yes 140
## 30 2nd Female Adult Yes 80
## 31 3rd Female Adult Yes 76
## 32 Crew Female Adult Yes 20ggplot(TDF,
aes(y = Freq, axis1 = Class, axis2 = Age, axis3 = Sex, axis4=Survived)) +
geom_alluvium(aes(fill = Survived), width = 1/24) +
geom_stratum(width = 1/12, fill = "white", color = "black") +
geom_label(stat = "stratum", label.strata = TRUE) +
scale_x_discrete(limits = c("Class", "Age", "Sex")) + # Fix the x axis
scale_fill_brewer(type = "qual", palette = "Set1") + # Give it nice colors
ggtitle("The Fate of Titanic Passengers", subtitle="Class, Sex, Age") # give it a title 
Beyonce Palettes
Now for one better, we can combine variables. I will use the titanic data and combine Age and Sex into a new variable people. They will now flow through Class to Survival starting with four types of people. I recently discovered Beyonce palettes; I will use Beyonce 41 for this alluvial.
# devtools::install_github("dill/beyonce")
library(beyonce)
TDF2 <- TDF %>% mutate(People = Sex:Age, AgeS = Age:Survived)
ggplot(TDF2,
aes(y = Freq, axis1 = People, axis2 = Class, axis3=Survived)) +
geom_alluvium(aes(fill = AgeS), width = 1/24) +
geom_stratum(width = 1/12, fill = "white", color = "black") +
geom_label(stat = "stratum", label.strata = TRUE) +
scale_x_discrete(limits = c("People", "Class", "Survived")) + # Fix the x axis
scale_fill_manual(values = beyonce_palette(41)) + # Give it nice colors
ggtitle("The Fate of Titanic Passengers", subtitle="Class, People") + # give it a title
theme_minimal()
Robert W. Walker
Associate Professor of Quantitative Methods
My research interests include causal inference, statistical computation and data visualization.