Plotting predictors

Example: predicting wages

Image Credit http://www.cahs-media.org/the-high-cost-of-low-wages

Data from: ISLR package from the book: Introduction to statistical learning

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Example: Wage data

library(ISLR); library(ggplot2); library(caret);
data(Wage)
summary(Wage)

      year           age              sex                    maritl           race     
 Min.   :2003   Min.   :18.0   1. Male  :3000   1. Never Married: 648   1. White:2480  
 1st Qu.:2004   1st Qu.:33.8   2. Female:   0   2. Married      :2074   2. Black: 293  
 Median :2006   Median :42.0                    3. Widowed      :  19   3. Asian: 190  
 Mean   :2006   Mean   :42.4                    4. Divorced     : 204   4. Other:  37  
 3rd Qu.:2008   3rd Qu.:51.0                    5. Separated    :  55                  
 Max.   :2009   Max.   :80.0                                                           
                                                                                       
              education                     region               jobclass               health    
 1. < HS Grad      :268   2. Middle Atlantic   :3000   1. Industrial :1544   1. <=Good     : 858  
 2. HS Grad        :971   1. New England       :   0   2. Information:1456   2. >=Very Good:2142  
 3. Some College   :650   3. East North Central:   0                                              
 4. College Grad   :685   4. West North Central:   0                                              
 5. Advanced Degree:426   5. South Atlantic    :   0                                              
                          6. East South Central:   0                                              
                          (Other)              :   0                                              
  health_ins      logwage          wage      
 1. Yes:2083   Min.   :3.00   Min.   : 20.1  
 2. No : 917   1st Qu.:4.45   1st Qu.: 85.4  
               Median :4.65   Median :104.9  
               Mean   :4.65   Mean   :111.7  
               3rd Qu.:4.86   3rd Qu.:128.7  
               Max.   :5.76   Max.   :318.3  
                                             

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Get training/test sets

inTrain <- createDataPartition(y=Wage$wage,
                              p=0.7, list=FALSE)
training <- Wage[inTrain,]
testing <- Wage[-inTrain,]
dim(training); dim(testing)

[1] 898  12

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Feature plot (caret package)

featurePlot(x=training[,c("age","education","jobclass")],
            y = training$wage,
            plot="pairs")

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Qplot (ggplot2 package)

qplot(age,wage,data=training)

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Qplot with color (ggplot2 package)

qplot(age,wage,colour=jobclass,data=training)

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Add regression smoothers (ggplot2 package)

qq <- qplot(age,wage,colour=education,data=training)
qq +  geom_smooth(method='lm',formula=y~x)

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cut2, making factors (Hmisc package)

cutWage <- cut2(training$wage,g=3)
table(cutWage)

cutWage
[ 20.1, 91.7) [ 91.7,118.9) [118.9,318.3] 
          704           725           673 

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Boxplots with cut2

p1 <- qplot(cutWage,age, data=training,fill=cutWage,
      geom=c("boxplot"))
p1

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Boxplots with points overlayed

p2 <- qplot(cutWage,age, data=training,fill=cutWage,
      geom=c("boxplot","jitter"))
grid.arrange(p1,p2,ncol=2)

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Tables

t1 <- table(cutWage,training$jobclass)
t1

               
cutWage         1. Industrial 2. Information
  [ 20.1, 91.7)           437            267
  [ 91.7,118.9)           365            360
  [118.9,318.3]           263            410

prop.table(t1,1)

               
cutWage         1. Industrial 2. Information
  [ 20.1, 91.7)        0.6207         0.3793
  [ 91.7,118.9)        0.5034         0.4966
  [118.9,318.3]        0.3908         0.6092

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Density plots

qplot(wage,colour=education,data=training,geom="density")

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Notes and further reading

• Make your plots only in the training set
• Don’t use the test set for exploration!
• Things you should be looking for
• Imbalance in outcomes/predictors
• Outliers
• Groups of points not explained by a predictor
• Skewed variables
• ggplot2 tutorial
• caret visualizations