alluvial: Alluvial diagram

In alluvial: Alluvial Diagrams

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")

Example output

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