High-Dimensional Data Plots

There are a number of "high-dimensional" data plots implemented in R that use different graphical approaches for illustrating relationships among many variables at a time.  One such plot is the scatter plot matrix which we have seen previously.  The other plot types are most conveniently described by simply constructing them for a typical data set

Stars plot

library(maps)
library(maptools)
attach(orstationc)
# outlines of Oregon counties (lines)
orotl.shp <- readShapeLines(file.choose(),
     proj4string=CRS("+proj=longlat"))

# stars
plot(orotl.shp)
col.red <- rep("red",length(orstationc[,1]))
stars(orstationc[,5:10], locations=as.matrix(cbind(lon, lat)),
col.stars=col.red, len=0.5, key.loc=c(-118,41.2), labels=NULL, add=T)

Rectangles

# rectangles
plot(orotl.shp)
pjul.scale <- (pjul-min(pjul))/(max(pjul)-min(pjul)) # width
pjan.scale <- (pjan-min(pjan))/(max(pjan)-min(pjan)) # height
rects <- cbind(pjul.scale, pjan.scale)
symbols(lon, lat, rectangles=rects, add=T)

Thermometers

# thermometers
plot(orotl.shp)
t1 <- rep(0.05, length(orstationc[,1])) # width
t2 <- (pann-min(pann))/(max(pann)-min(pann)) # height
t3 <- pjan/pann # shaded proportion
thermo <- cbind(t1, t2, t3)
symbols(lon, lat, thermometer=thermo, add=T)

Parallel coordinates display

# parallel coordinates display
library(lattice)
parallel(~cbind(pjul,pjan,pann,tjul,tjan,tann))