Plotting Predictors

Example: Wage data

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

##       year           age               sex                    maritl    
##  Min.   :2003   Min.   :18.00   1. Male  :3000   1. Never Married: 648  
##  1st Qu.:2004   1st Qu.:33.75   2. Female:   0   2. Married      :2074  
##  Median :2006   Median :42.00                    3. Widowed      :  19  
##  Mean   :2006   Mean   :42.41                    4. Divorced     : 204  
##  3rd Qu.:2008   3rd Qu.:51.00                    5. Separated    :  55  
##  Max.   :2009   Max.   :80.00                                           
##                                                                         
##        race                   education                     region    
##  1. White:2480   1. < HS Grad      :268   2. Middle Atlantic   :3000  
##  2. Black: 293   2. HS Grad        :971   1. New England       :   0  
##  3. Asian: 190   3. Some College   :650   3. East North Central:   0  
##  4. Other:  37   4. College Grad   :685   4. West North Central:   0  
##                  5. Advanced Degree:426   5. South Atlantic    :   0  
##                                           6. East South Central:   0  
##                                           (Other)              :   0  
##            jobclass               health      health_ins      logwage     
##  1. Industrial :1544   1. <=Good     : 858   1. Yes:2083   Min.   :3.000  
##  2. Information:1456   2. >=Very Good:2142   2. No : 917   1st Qu.:4.447  
##                                                            Median :4.653  
##                                                            Mean   :4.654  
##                                                            3rd Qu.:4.857  
##                                                            Max.   :5.763  
##                                                                           
##       wage       
##  Min.   : 20.09  
##  1st Qu.: 85.38  
##  Median :104.92  
##  Mean   :111.70  
##  3rd Qu.:128.68  
##  Max.   :318.34  
## 

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] 2102   12

## [1] 898  12

Feature plot (caret package)

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

#Qplot (ggplot2 package)

qplot(age,wage,data=training)

Qplot with color (ggplot2 package)

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

Add regression smoothers (ggplot2 package)

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

cut2, making factors (Hmisc package)

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

## cutWage
## [ 20.1, 91.7) [ 91.7,119.0) [119.0,318.3] 
##           703           727           672

Boxplots with cut2

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

Boxplots with points overlayed

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

# Tables

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

##                
## cutWage         1. Industrial 2. Information
##   [ 20.1, 91.7)           454            249
##   [ 91.7,119.0)           359            368
##   [119.0,318.3]           263            409

prop.table(t1,1)

##                
## cutWage         1. Industrial 2. Information
##   [ 20.1, 91.7)     0.6458037      0.3541963
##   [ 91.7,119.0)     0.4938102      0.5061898
##   [119.0,318.3]     0.3913690      0.6086310

Density plots

qplot(wage,colour=education,data=training,geom="density", lwd = 1)