DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Scatterplot

A http://en.wikipedia.org/wiki/scatterplotscatterplot presents points in 2-dimensional space corresponding to a pair of chosen variables. R's plot function defaults to a scatterplot. Relationships between pairs of variables can be seen through the use of a scatterplot and clusters and outliers can begin to be identified.

Using the wine dataset a plot is created to display http://en.wikipedia.org/wiki/PhenolsPhenols versus http://en.wikipedia.org/wiki/FlavanoidsFlavanoids. To add a little more interest to the plot, a different symbol (and for colour devices, a different colour) is used to display the three different values of Type for each point. The symbols are set using Type as the argument to pch, but after converting it to integers with as.integer. In a similar fashion, the colours are chosen to replace numbers in a transformation of the Type vector by indexing into the output of palette, achieved using lapply, and turning the result into a flat list, rather than a list of lists, using unlist.

We can start to understand that there is somewhat of a linear relationship between these two variables, and even more interesting is the clustering of Types.

[width=0.75]rplot-wine-scatter

iType <- as.integer(wine$Type)
colours <- unlist(lapply(iType, function(x){palette()[x+1]}))
plot(wine$Phenols, wine$Flavanoids, col=colours, pch=iType)
dev.off()

http://rattle.togaware.com/code/rplot-wine-scatter.R

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