Package 'alluvial'

Alluvial diagram

Description

Drawing alluvial diagrams, also known as parallel set plots.

Usage

alluvial(
  ...,
  freq,
  col = "gray",
  border = 0,
  layer,
  hide = FALSE,
  alpha = 0.5,
  gap.width = 0.05,
  xw = 0.1,
  cw = 0.1,
  blocks = TRUE,
  ordering = NULL,
  axis_labels = NULL,
  mar = c(2, 1, 1, 1),
  cex = par("cex"),
  xlim_offset = c(0, 0),
  ylim_offset = c(0, 0),
  cex.axis = par("cex.axis"),
  axes = TRUE,
  ann = TRUE,
  title = NULL
)

Arguments

...	vectors or data frames, all for the same number of observations
freq	numeric, vector of frequencies of the same length as the number of observations
col	vector of colors of the stripes
border	vector of border colors for the stripes
layer	numeric, order of drawing of the stripes
hide	logical, should particular stripe be plotted
alpha	numeric, vector of transparency of the stripes
gap.width	numeric, relative width of inter-category gaps
xw	numeric, the distance from the set axis to the control points of the xspline
cw	numeric, width of the category axis
blocks	logical, whether to use blocks to tie the flows together at each category, versus contiguous ribbons (also admits character value "bookends")
ordering	list of numeric vectors allowing to reorder the alluvia on each axis separately, see Examples
axis_labels	character, labels of the axes, defaults to variable names in the data
mar	numeric, plot margins as in par
cex, cex.axis	numeric, scaling of fonts of category labels and axis labels respectively. See par.
xlim_offset, ylim_offset	numeric vectors of length 2, passed to xlim and ylim of plot, and allow for adjusting the limits of the plotting region
axes	logical, whether to draw axes, defaults to TRUE
ann	logical, whether to draw annotations: category labels. Defaults to TRUE
title	character, plot title

Value

Invisibly a list with elements:

endpoints	A data frame with data on locations of the stripes with columns: ... Vectors/data frames supplied to alluvial through ... that define the axes .bottom,.top Y locations of bottom and top coordinates respectively at which the stripes originate from the axis .axis .axis Axis number counting from the left
category_midpoints	List of vectors of Y locations of category block midpoints.
alluvium_midpoints	A data frame with location of midpoints on each alluvium segement with columns: ... Vectors/data frames supplied to alluvial through the ... .axis_from, .axis_to IDs of axes that a segment originates from and goes to .x, .y X and Y locations of the alluvium midpoints .slope The (approximate) slope of the alluvium at the midpoint

Note

Please mind that the API is planned to change to be more compatible with dplyr verbs.

Examples

# Titanic data
tit <- as.data.frame(Titanic)

# 2d
tit2d <- aggregate( Freq ~ Class + Survived, data=tit, sum)
alluvial( tit2d[,1:2], freq=tit2d$Freq, xw=0.0, alpha=0.8,
         gap.width=0.1, col= "steelblue", border="white",
         layer = tit2d$Survived != "Yes" )

alluvial( tit2d[,1:2], freq=tit2d$Freq, 
         hide=tit2d$Freq < 150,
         xw=0.0, alpha=0.8,
         gap.width=0.1, col= "steelblue", border="white",
         layer = tit2d$Survived != "Yes" )

# 3d
tit3d <- aggregate( Freq ~ Class + Sex + Survived, data=tit, sum)

alluvial(tit3d[,1:3], freq=tit3d$Freq, alpha=1, xw=0.2,
         col=ifelse( tit3d$Survived == "No", "red", "gray"),
         layer = tit3d$Sex != "Female",
         border="white")


# 4d
alluvial( tit[,1:4], freq=tit$Freq, border=NA,
         hide = tit$Freq < quantile(tit$Freq, .50),
         col=ifelse( tit$Class == "3rd" & tit$Sex == "Male", "red", "gray") )

# 3d example with custom ordering
# Reorder "Sex" axis according to survival status
ord <- list(NULL, with(tit3d, order(Sex, Survived)), NULL)
alluvial(tit3d[,1:3], freq=tit3d$Freq, alpha=1, xw=0.2,
         col=ifelse( tit3d$Survived == "No", "red", "gray"),
         layer = tit3d$Sex != "Female",
         border="white", ordering=ord)

# Possible blocks options
for (blocks in c(TRUE, FALSE, "bookends")) {
    
    # Elaborate alluvial diagram from main examples file
    alluvial( tit[, 1:4], freq = tit$Freq, border = NA,
              hide = tit$Freq < quantile(tit$Freq, .50),
              col = ifelse( tit$Class == "3rd" & tit$Sex == "Male",
                            "red", "gray" ),
              blocks = blocks )
}


# Data returned
x <- alluvial( tit2d[,1:2], freq=tit2d$Freq, xw=0.0, alpha=0.8,
          gap.width=0.1, col= "steelblue", border="white",
          layer = tit2d$Survived != "Yes" )
points( rep(1, 16), x$endpoints[[1]], col="green")
points( rep(2, 16), x$endpoints[[2]], col="blue")

---

Alluvial diagram for multiple time series data

Description

This is a variant of alluvial diagram suitable for multiple (cross-sectional) time series. It also works with continuous variables equivalent to time

Usage

alluvial_ts(
  dat,
  wave = NA,
  ygap = 1,
  col = NA,
  alpha = NA,
  plotdir = "up",
  rankup = FALSE,
  lab.cex = 1,
  lab.col = "black",
  xmargin = 0.1,
  axis.col = "black",
  title = NA,
  title.cex = 1,
  axis.cex = 1,
  grid = FALSE,
  grid.col = "grey80",
  grid.lwd = 1,
  leg.mode = TRUE,
  leg.x = 0.1,
  leg.y = 0.9,
  leg.cex = 1,
  leg.col = "black",
  leg.lty = NA,
  leg.lwd = NA,
  leg.max = NA,
  xlab = NA,
  ylab = NA,
  xlab.pos = 2,
  ylab.pos = 1,
  lwd = 1,
  ...
)

Arguments

dat	data.frame of time-series (or suitable equivalent continuously disaggregated data), with 3 columns (in order: category, time-variable, value) with <= 1 row for each category-time combination
wave	numeric, curve wavyness defined in terms of x axis data range - i.e. bezier point offset. Experiment to get this right
ygap	numeric, vertical distance between polygons - a multiple of 10% of the mean data value
col	colour, value or vector of length matching the number of unique categories. Individual colours of vector are mapped to categories in alpha-numeric order
alpha	numeric, [0,1] polygon fill transparency
plotdir	character, string ('up', 'down' or 'centred') giving the vertical alignment of polygon stacks
rankup	logical, rank polygons on time axes upward by magnitude (largest to smallest) or not
lab.cex	numeric, category label font size
lab.col	colour, of category label
xmargin	numeric [0,1], proportional space for category labels
axis.col	colour, of axes
title	character, plot title
title.cex	numeric, plot title font size
axis.cex	numeric, font size of x-axis break labels
grid	logical, plot vertical axes
grid.col	colour, of grid axes
grid.lwd	numeric, line width of grid axes
leg.mode	logical, draw y-axis scale legend inside largest data point (TRUE default) or alternatively with custom position/value (FALSE)
leg.x, leg.y	numeric [0,1], x/y positions of legend if leg.mode = FALSE
leg.cex	numeric, legend text size
leg.col	colour, of legend lines and text
leg.lty	numeric, code for legend line type
leg.lwd	numeric, legend line width
leg.max	numeric, legend scale line width
xlab, ylab	character, x-axis / y-axis titles
xlab.pos, ylab.pos	numeric, perpendicular offset for axis titles
lwd	numeric, value or vector of length matching the number of unique categories for polygon stroke line width. Individual values of vector are mapped to categories in alpha-numeric order
...	arguments to pass to polygon()

Examples

if( require(reshape2) )
{
data(Refugees)
reshape2::dcast(Refugees, country ~ year, value.var = 'refugees')
d <- Refugees

set.seed(39) # for nice colours
cols <- hsv(h = sample(1:10/10), s = sample(3:12)/15, v = sample(3:12)/15)

alluvial_ts(d)
alluvial_ts(d, wave = .2, ygap = 5, lwd = 3)
alluvial_ts(d, wave = .3, ygap = 5, col = cols)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, rankup = TRUE)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, plotdir = 'down')
alluvial_ts(d, wave = .3, ygap = 5, col = cols, plotdir = 'centred', grid=TRUE,
            grid.lwd = 5)
alluvial_ts(d, wave =  0, ygap = 0, col = cols, alpha = .9, border = 'white',
            grid = TRUE, grid.lwd = 5)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, xmargin = 0.4)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, xmargin = 0.3, lab.cex = .7)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, xmargin = 0.3, lab.cex=.7,
            leg.cex=.7, leg.col = 'white')
alluvial_ts(d, wave = .3, ygap = 5, col = cols, leg.mode = FALSE, leg.x = .1,
            leg.y = .7, leg.max = 3e6)
alluvial_ts(d, wave = .3, ygap = 5, col = cols, plotdir = 'centred', alpha=.9,
            grid = TRUE, grid.lwd = 5, xmargin = 0.2, lab.cex = .7, xlab = '',
            ylab = '', border = NA, axis.cex = .8, leg.cex = .7,
            leg.col='white', 
         title = "UNHCR-recognised refugees\nTop 10 countries (2003-13)\n")

# non time-series example - Virginia deaths dataset
d <- reshape2::melt(data.frame(age=row.names(VADeaths), VADeaths), id.vars='age')[,c(2,1,3)]
names(d) = c('pop_group','age_group','deaths')
alluvial_ts(d)
}

---

POLPAN voting flows

Description

Voting choices across three consecutive Polish parliamentary elections (2007, 2011, 2015) reported by respondents of the Polish Panel Study (POLPAN). Each row represents a unique combination of voting choices and gives the number of respondents with that pattern.

Usage

data(Polpan)

Format

A data frame with 51 rows and 4 columns:

party2007

Party voted for in the 2007 parliamentary election.

party2011

Party voted for in the 2011 parliamentary election.

party2015

Party voted for in the 2015 parliamentary election.

n

Number of respondents with this combination of voting choices.

Each of the partyXXXX columns takes one of the following values: "PO" (Civic Platform / Platforma Obywatelska), "PiS" (Law and Justice / Prawo i Sprawiedliwosc), "PSL" (Polish People's Party / Polskie Stronnictwo Ludowe), "Left" (left-wing parties), "Other", or "not voted".

Source

Polish Panel Study (POLPAN, https://www.polpan.org), Marta Kołczyńska (https://martakolczynska.com/) and her blog post.

Examples

# color palette
p <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", 
    "#FF7F00", "#FFFF33")

# How did the 2007 voters vote in subsequent elections?
alluvial(
    Polpan[,1:3],
    freq = Polpan$n,
    col = p[match(Polpan$party2007, sort(unique(Polpan$party2007)))]
)

# Remove flows with freqs less than 10
alluvial(
    Polpan[,1:3],
    freq = Polpan$n,
    col = p[match(Polpan$party2007, sort(unique(Polpan$party2007)))],
    hide = Polpan$n < 5
)


# How did the 2015 voters vote in preceding elections?
alluvial(
    Polpan[,1:3],
    freq = Polpan$n,
    col = p[match(Polpan$party2015, sort(unique(Polpan$party2007)))],
    hide = Polpan$n < 5
)

---

Refugees data

Description

Top 10 countries/territories of origin (excluding "Various") for period 2003-13 of UNHCR statistics on "Persons recognized as refugees under the 1951 UN Convention/1967 Protocol, the 1969 OAU Convention, in accordance with the UNHCR Statute, persons granted a complementary form of protection and those granted temporary protection."

Format

Data frame with the following columns:

country

Country or territory of origin

year

Year (2003-13)

refugees

Persons recognized as refugees under the 1951 UN Convention, etc..

Source

http://data.un.org/Data.aspx?d=UNHCR&f=indID%3aType-Ref

---