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

Review Data

Often we will find ourselves loading data from a CSV file which is readily supported by R (See Section ). On the first loading of the data we generally want to get a quick summary, using R's summary function. It is here that we might note that some numeric columns have become factors!

Consider the example of the cardiac dataset (See Section ).

> cardiac <- read.csv("cardiac.data", header=F)
> summary(cardiac)
[...]
      V10               V11           V12           V13           V14     
 Min.   :-172.00   52     : 13   60     : 23   49     :  9   ?      :376  
 1st Qu.:   3.75   36     : 10   ?      : 22   55     :  9   84     :  3  
 Median :  40.00   42     :  9   61     : 16   59     :  9   -157   :  2  
 Mean   :  33.68   10     :  8   56     : 14   62     :  9   -164   :  2  
 3rd Qu.:  66.00   33     :  8   58     : 13   26     :  8   -93    :  2  
 Max.   : 169.00   41     :  8   68     : 12   33     :  8   103    :  2  
                   (Other):396   (Other):352   (Other):400   (Other): 65  
[...]

Our understanding of the data might be that we expect these variables to be numeric. Indeed, the telltale sign is V14 having a ? as one of its values. A little more exploration to show the frequency of each value will indicate that the apparently nominal variables only have a single non-numeric value, the ? When we read the data from the CSV file we need to tell R that the ? is used to indicate missing values

> cardiac <- read.csv("cardiac.data", header=F, na.string="?")
> summary(cardiac)
[...]
      V11               V12               V13               V14       
 Min.   :-177.00   Min.   :-170.00   Min.   :-135.00   Min.   :-179.00
 1st Qu.:  14.00   1st Qu.:  41.00   1st Qu.:  12.00   1st Qu.:-124.50
 Median :  41.00   Median :  56.00   Median :  40.00   Median : -50.50
 Mean   :  36.15   Mean   :  48.91   Mean   :  36.72   Mean   : -13.59
 3rd Qu.:  63.25   3rd Qu.:  65.00   3rd Qu.:  62.00   3rd Qu.: 117.25
 Max.   : 179.00   Max.   : 176.00   Max.   : 166.00   Max.   : 178.00
 NA's   :   8.00   NA's   :  22.00   NA's   :   1.00   NA's   : 376.00
[...]

That's looking better. Note that the NAs are reported and that V14 has 376 of them, in accord with the previous observation of 376 ?'s.

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