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

The Adult Survey Dataset

The survey dataset is a little larger (3.8MB) and illustrates many more of the options to the read.csv function. The data was extracted from the US Census Bureau database, and is again available from the UCI Machine Learning Repository.

UCI <- "ftp://ftp.ics.uci.edu/pub"
REPOS <- "machine-learning-databases"
survey.url <- sprintf("%s/%s/adult/adult.data", UCI, REPOS)
survey <- read.csv(survey.url, header=F, strip.white=TRUE,
                   na.strings="?",
                   col.names=c("Age", "Workclass", "fnlwgt", 
                     "Education", "Education.Num", "Marital.Status", 
                     "Occupation", "Relationship", "Race", "Sex", 
                     "Capital.Gain", "Capital.Loss", 
                     "Hours.Per.Week", "Native.Country", 
                     "Salary.Group"))
write.table(survey, "survey.csv", sep=",", row.names=F)
save(survey, file="survey.Rdata", compress=TRUE)
}

http://rattle.togaware.com/code/get-survey.R

> dim(survey)
[1] 32561    15
> str(survey)
`data.frame':   32561 obs. of  15 variables:
 $ Age           : int  39 50 38 53 28 37 49 52 31 42 ...
 $ Workclass     : Factor w/ 8 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
 $ fnlwgt        : int  77516 83311 215646 234721 338409 284582 160187 ...
 $ Education     : Factor w/ 16 levels "10th","11th",..: 10 10 12 2 10 13 7 ...
 $ Education.Num : int  13 13 9 7 13 14 5 9 14 13 ...
 $ Marital.Status: Factor w/ 7 levels "Divorced",..: 5 3 1 3 3 3 4 3 5 3 ...
 $ Occupation    : Factor w/ 14 levels "Adm-clerical",..: 1 4 6 6 10 4 8 ...
 $ Relationship  : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 ...
 $ Race          : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 ...
 $ Sex           : Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
 $ Capital.Gain  : int  2174 0 0 0 0 0 0 0 14084 5178 ...
 $ Capital.Loss  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Hours.Per.Week: int  40 13 40 40 40 40 16 45 50 40 ...
 $ Native.Country: Factor w/ 41 levels "Cambodia","Canada",..: 39 39 39 39 ...
 $ Salary.Group  : Factor w/ 2 levels "<=50K",">50K": 1 1 1 1 1 1 1 2 2 2 ...

Once again, the dataset can be read in from the CSV file or else loaded as an R dataset:

> survey <- read.csv("survey.csv")
OR
> load("survey.RData")

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