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

Basic Bar Chart

A bar chart is useful to illustrate distributions of the population. In the following bar chart five classes of patient disease groups are identified, and a distribution of three age groups is shown for each disease group.

[width=0.8]rplot-bar

bars <- matrix(c(35, 15, 26, 25, 18,
                 45, 42, 48, 12, 35,
                 20, 63, 26, 34, 28), nrow=3, byrow=T)
rownames(bars) <- c("0-19", "19-60", "61-100")
colnames(bars) <- c("Asthma", "Cancer", "Diabetes", "Mental", "Musculo")
col <- c("lightyellow", "mistyrose","darkkhaki")
barplot(bars, beside=TRUE, col=col)
legend("topright", rownames(bars), title="Age Groups", fill=col)

http://rattle.togaware.com/code/rplot-bar.R

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