alluvial (version 0.1-2)

alluvial_ts: 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

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)
}