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

Building Strings

A typical usage of paste is to build a label vector to be used in a plot. The labels may want to include both the variable values being plotted and perhaps the percentage of observations having that value.

> labels <- c("A", "B", "C", "D")
> per    <- c(25, 10, 15, 45)
> paste(labels, rep(" (", 4), per, rep("%)",4), sep="")
[1] "A (25%)" "B (10%)" "C (15%)" "D (45%)"
> sprintf("%s (%d%%)", labels, per)
[1] "A (25%)" "B (10%)" "C (15%)" "D (45%)"

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.