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

Box and Whisker Plot

A simple box plot of randomly generated data. The box in each plot shows the median (as the line within the box) and one standard deviation from the mean (the extremes of the box). The whiskers show the second standard deviation, and the circles show outliers.

In this code we use R's rnorm function to generate a standard_normal random matrix of the given shape (rows by cols). The dataset is transformed to an R data frame. The x axis is labelled with an appropriate number of letters from the alphabet.

[width=0.7]rplot-boxplot

set.seed(2)
ds <- matrix(rnorm(19 * 100), ncol = 19)
pdf("graphics/rplot-boxplot.pdf")
  plot.new()
  plot.window(xlim = c(0, 20), ylim = range(ds), xaxs = "i")
  boxplot(as.data.frame(ds), add = TRUE, at = 1:19)
dev.off()

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

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