alluvial {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, cex = par("cex"),
  cex.axis = par("cex.axis"))

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

cex, cex.axis

numeric, scaling of fonts of category labels and axis labels respectively. See par.

Value

Invisibly a list with elements:

endpoints

A list of matrices of y-coordinates of endpoints of the alluvia. x-coordinates are consecutive natural numbers.

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")