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

Boxplot by Class

With a boxplot it is often useful to display the distribution of one variable as it relates to some other variable. An example in the wine data would be to partition the data according to the Type, and then to explore the resulting distribution of, for example, Malic. This is achieved with the formula notation Malic ~ Type. The boxplot then allows us to understand any potential relationship between the input variable and the output variable. For such plots we enable the notch display, which indicates whether there is a significant difference between the medians. In the case here the median for Type 3 is significantly different from the other two, but the other two are not significantly different from each other.

load("wine.Rdata")
attach(wine)
boxplot(Malic ~ Type, notch=TRUE, xlab="Type", ylab="Malic")

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

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