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

Strings

R provides the usual string type and string manipulation functions:

> chartr("a-m", "m-z", "abcdef")	 # "mnopqr"
> grep
> nchar
> sub
> substr
> tolower("ABCdef")			 # "abcdef"
> toupper("ABCdef")			 # "ABCDEF"
> pmatch("png", c("jpeg", "png", "gif")) # 2

R provides some useful predefined variables, including:

> letters		# a b c d [...] z
> LETTERS		# A B C D [...] Z

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