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

Cleaning the Survey Dataset

We summarise a number of cleaning operations that might be performed on the survey dataset.

Remove entities with null values:

> load("survey.RData")
> survey <- na.omit(survey)
> dim(survey)
[1] 30162    15

Remove non-numeric data:

> load("survey.RData")
> rmcols <- rev(seq(1,ncol(survey))[as.logical(lapply(survey, is.factor))])
> for (i in rmcols) survey[[i]] <- NULL
> dim(survey)
[1] 32561     6
> colnames(survey)
[1] "Age"            "fnlwgt"         "Education.Num"  "Capital.Gain"  
[5] "Capital.Loss"   "Hours.Per.Week"

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