Perform the following steps in R:

1. Find the minimum cross-validation error of the classification tree model:

        > install.packages("mice")
        > install.packages("randomForest")
        > install.packages("VIM")
        > t = data.frame(x=c(1:100), y=c(1:100))  
        > t$x[sample(1:100,10)]=NA
        > t$y[sample(1:100,20)]=NA
        > aggr(t)

2. Tweaking the aggr function by adding prop and number will produce a slightly different output showing numbers for combinations also:

        > aggr(t, prop=T, numbers=T)

        > matrixplot(t)

        > md.pattern(t)
        Output
            x y 
        71 1 1 0
         9 0 1 1
        19 1 0 1
         1 0 0 2
        10 20 30
        > m <- mice(t, method="rf")
        > m
        Output
         iter imp variable
          1    1     x
        > mc <- complete(m)
        > anyNA(mc)
        Output
       [1] False