scpcp: Static Categorical Parallel Coordinates Plot
In extracat: Categorical Data Analysis and Visualization

Description Usage Arguments Value Note Author(s) References See Also Examples

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.

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)

`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.

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

A grid-based version is still under construction. In polygon mode single cases are still shown as a line rather than a ribbon of width 1/n.

Karin Maria Gehweiler and Alexander Pilhoefer.

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/

steptile

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))

## End(Not run)