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

Histogram

A histogram allows the basic distribution of the data to be viewed. Here we plot the histogram for magnesium and alcohol content of various wines, and we might compare it with the previous stem-and-leaf plot which summarises the same data. The shape is basically the same, although in detail they go up and down at different points!

[width=]rplot-wine-hist

attach(wine)
par(mfrow=c(1, 2))
hist(Magnesium)
hist(Alcohol)

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

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