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 ggplot version is under construction. A deprecated interactive version based on iplots (without labeling) is still available as extracat:::cpcp. For reordering of category orders in CPCP plots see steptile.

Usage

scpcp(data, freqvar = "Freq", max.N = 1e6, gap = 0.2,
 sort.individual = TRUE, level.width = 0.2,
 polygon = TRUE, base.colour = alpha("black", 0.7), label = TRUE,
 lab.opt = list(rot = 0, col = 1, bg = TRUE, abbr = FALSE,
  abbr.var=12,hide.sel=TRUE, var.labels = 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".

freqvar

Optional specification of a frequency variable.

max.N

The plot handles each case as a separate polyline, similar to conventional pcps. This option limits the number of observations.

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.

label

Whether or not to draw category labels.

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 random colors and sel="Sex=='Male' & Survived=='Yes'" selects survivors among the men (in the titanic data, see examples). The objects (observations/lines) are additionally sorted by their color which brings colors together and makes it possible to see proportions of the selections.

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

# NOT RUN {
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)

# }
# NOT RUN {
# mushroom data from the UCI machine learning repository
data(agaricus)
MR <- agaricus

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)
data(eco)

# 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))
# }