scpcp: Static Categorical Parallel Coordinates Plot

Description

This function creates a static CPCP plot using base R graphics. The function offers color brush / highlighting and several options for the labels and colors. Efficiency is improved by replacing sets of parallel lines by polygons. A grid-based version is under construction. For an interactive version without labeling see cpcp.

Usage

scpcp(data, gap = 0.2, sort.individual = TRUE, level.width = 0.2,
 polygon = TRUE, base.colour = alpha("black", 0.7), 
 lab.opt = list(rot = 0, col = 1, bg = TRUE, abbr = FALSE,
  abbr.var=12,hide.sel=TRUE), 
 sel = NULL, sel.hide = TRUE, sel.palette = NULL,
 col.opt = list(), plot = TRUE, return.coords = !plot)

Arguments

data

The data.frame which can contain a variable called "Freq".

gap

The size of the gaps between the categories as a total proportion.

sort.individual

Whether or not the cases (lines) are additionally rearranged according to the neighboring variable. This minimizes crossings.

level.width

The width of the rectangles representing the variables/categories.

polygon

Whether or not to replace parallel lines by polygons. This improves both efficiency and undesirable color effects.

base.colour

The standard color used for the cases which are not highlighted via sel.

lab.opt

A list with options for the labels. See e.g. rmb

sel

A selection defining colors. This can either be an integer vector, a factor or an expression which returns such a vector. For instance sel="data[,4]" colors by the fourth variable, sel="sample(1:6,nrow(data),T)") leads to rando

sel.hide

Whether or not to hide the sel variable or plot it as the first variable.

sel.palette

The color palette for the selection. See getcolors.

col.opt

A list of options for the color palette. See getcolors.

plot

Whether or not to plot.

return.coords

Whether or not to return the coordinates per observation. plot = FALSE together with return.coords = FALSE is therefore rather stupid. The polygon coordinates are currently not returned.

Value

Either a logical value or the coordinates defining the polylines per observation.

References

Alexander Pilhoefer, Antony Unwin (2013). New Approaches in Visualization of Categorical Data: R Package extracat. Journal of Statistical Software, 53(7), 1-25. URL http://www.jstatsoft.org/v53/i07/

Examples

Run this code

data(Titanic)
titanic <- as.data.frame(Titanic)

scpcp(titanic)

#scpcp(titanic, level.width=0)

#scpcp(titanic, gap=0)

#default with highlighting
scpcp(titanic, sel="data[,4]")

# random colors like for instance from a clustering
scpcp(titanic, sel="sample(1:6,nrow(data),T)")

# another one with some formal changes
require(scales)
scpcp(data=titanic,sel="Sex=='Male' & Survived=='Yes'", sel.palette = "w",
 col.opt=list(alpha=0.7,border=alpha(1,0.3)), gap = 0.5, level.width= 0.3)

# mushroom data from the UCI machine learning repository
MR <- read.table("http://rosuda.org/mitarbeiter/pilhoefer/agaricus.dat",
	sep="\t",quote="",header=TRUE)

levels(MR$stalk_root) <- c(levels(MR$stalk_root),"N/A")
MR$stalk_root[which(is.na(MR$stalk_root))] <- "N/A"

op <- optile(MR[,1:12], method="joint")


scpcp(op, sel = "odor",sel.palette="w",
	 col.opt = list(border = alpha(1,0.1)), lab.opt=list(rot=45))


# ADAC ecotest data with four clusterings (k-means, mclust, hc Ward, hc complete)
eco <- read.table("http://rosuda.org/mitarbeiter/pilhoefer/eco2plus.dat",
	sep="\t",quote="",header=TRUE)

# illustrate reordering success using coloring
scpcp(eco[,13:16], sel = "data[,1]", sel.palette="d")

scpcp(optile(eco[,13:16]), sel = "data[,1]", sel.palette="d",
	 col.opt = list(border=alpha(1,0.1)))

# car classes (lower to upper class)
eco$Klasse <- factor(eco$Klasse, levels = levels(eco$Klasse)[c(3,1,2,7,4,5,6)])

scpcp(eco[,17:20], sel = eco$Klasse, sel.palette="s", col.opt = list(h=140))

# the color variable included
scpcp(eco[,c(3,17:20)], sel = eco$Klasse, sel.palette="s", 
	col.opt = list(h=140),lab.opt = list(abbr=5))