alluvial_ts: Alluvial diagram for multiple time series data

In alluvial: Alluvial Diagrams

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)
}

Example output

Loading required package: reshape2

alluvial documentation built on May 2, 2019, 1:29 p.m.