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

Pie Chart

A http://en.wikipedia.org/wiki/Pie_chartpie chart partitions a circle into proportions related to some data. The R function pie is used to produce a pie chart. A pie chart can be used to again display the proportion of entities spread across some partitioning of the dataset.

Pie charts are a perennial favourite even though common wisdom suggests avoiding them. The human eye is not well suited to differentiating angular variations and a bar chart provides a better alternative. However, many people still enjoy the look of a pie chart

In our example, using the wine dataset, the data is partitioned on categorical variable Type. The default plot produced by pie will produce quite a respectable looking pie chart. We add in to the basic plot the percentage of entities in each category, including this with the labels of the pie chart. This helps in communicating the distribution of the data over Type.

[width=0.7]rplot-wine-pie

load("wine.Rdata")
attach(wine)
percent <- round(summary(Type) * 100 / nrow(wine))
labels <- sprintf("%s (%d%%)", levels(Type), percent)
pie(summary(Type), lab=labels)

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

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