DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Map Displays

Map displays often provide further insights into patterns that vary according to region. In this plot we illustrate some basic colouring of a map. This can be used to translate a variable into a colour which is then plot for the state. We use the rainbow function to generate the bright colours to use to colour the states.

library(maptools)
aus <- readShapePoly("australia.shp")
plot(aus, lwd=2, border="grey", xlim=c(115,155), ylim=c(-35,-20))
colours <- rainbow(8)
# Must be a better way than this....
nsw <- aus; nsw@plotOrder <- as.integer(c(2)); plot(nsw,col=colours[2],add=TRUE)
act <- aus; act@plotOrder <- as.integer(c(1)); plot(act,col=colours[1],add=TRUE)
nt  <- aus; nt@plotOrder  <- as.integer(c(3)); plot(nt, col=colours[3],add=TRUE)
qld <- aus; qld@plotOrder <- as.integer(c(4)); plot(qld,col=colours[4],add=TRUE)
sa  <- aus; sa@plotOrder  <- as.integer(c(5)); plot(sa, col=colours[5],add=TRUE)
tas <- aus; tas@plotOrder <- as.integer(c(6)); plot(tas,col=colours[6],add=TRUE)
vic <- aus; vic@plotOrder <- as.integer(c(7)); plot(vic,col=colours[7],add=TRUE)

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