> #Split
> set.seed(1)
> trainIndex <- sample(nrow(newdata), nrow(newdata) * .6)
> train <- newdata[trainIndex, ]
> test <- newdata[-trainIndex, ]
> tree <- tree(Vote ~ ., data = test)
> plot(tree)
> text(tree, pretty = 2)
> title(main = "Train Tree")
> summary(tree)

Classification tree:
tree(formula = Vote ~ ., data = test)
Variables actually used in tree construction:
[1] "Age"             "Family.Income"   "Political.Party" "Bush.Approval"   "Ideology"       
[6] "Race"            "Region"         
Number of terminal nodes:  12 
Residual mean deviance:  0.1024 = 39.74 / 388 
Misclassification error rate: 0.0275 = 11 / 400