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

Selectively Changing Vector Values

The next example changes the values in one vector (weights) according to some conditions on the values in another vector (data). The data vector is randomly sampled from the letters of the alphabet. Both vectors are the same length. Where data is larger than m, the weight is set to 2. Where it is between d and m, the weight is set to 3.

> weights <- rep(1, 10)
> data <- letters[sample(seq(1,length(letters)), 10)]
> data
 [1] "y" "b" "j" "m" "c" "q" "o" "a" "i" "p"
> weights[data > "m"] <- 2
> weights
 [1] 2 1 1 1 1 2 2 1 1 2
> weights[data <= "m" & data >= "d"] <- 3
> weights
 [1] 2 1 3 3 1 2 2 1 3 2

An example of where this might be useful is in data mining pre-processing where we wish to selectively change the weights associated with entities in a modelling exercise. The weights might indicate the relative important the specific entities. An example of this transformation is included in the usage of rpart in See Chapter .

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