Quickly remove zero variance variables from a data.frame

Question

I have a large data.frame that was generated by a process outside my control, which may or may not contain variables with zero variance (i.e. all the observations are the same). I would like to build a predictive model based on this data, and obviously these variables are of no use.

Here's the function I'm currently using to remove such variables from the data.frame. It's currently based on apply, and I was wondering if there are any obvious ways to speed this function up, so that it works quickly on very large datasets, with a large number (400 or 500) of variables?

set.seed(1)
dat <- data.frame(
    A=factor(rep("X",10),levels=c('X','Y')),
    B=round(runif(10)*10),
    C=rep(10,10),
    D=c(rep(10,9),1),
    E=factor(rep("A",10)),
    F=factor(rep(c("I","J"),5)),
    G=c(rep(10,9),NA)
)
zeroVar <- function(data, useNA = 'ifany') {
    out <- apply(data, 2, function(x) {length(table(x, useNA = useNA))})
    which(out==1)
}

And here's the result of the process:

> dat
   A B  C  D E F  G
1  X 3 10 10 A I 10
2  X 4 10 10 A J 10
3  X 6 10 10 A I 10
4  X 9 10 10 A J 10
5  X 2 10 10 A I 10
6  X 9 10 10 A J 10
7  X 9 10 10 A I 10
8  X 7 10 10 A J 10
9  X 6 10 10 A I 10
10 X 1 10  1 A J NA

> dat[,-zeroVar(dat)]
   B  D F  G
1  3 10 I 10
2  4 10 J 10
3  6 10 I 10
4  9 10 J 10
5  2 10 I 10
6  9 10 J 10
7  9 10 I 10
8  7 10 J 10
9  6 10 I 10
10 1  1 J NA

> dat[,-zeroVar(dat, useNA = 'no')]
   B  D F
1  3 10 I
2  4 10 J
3  6 10 I
4  9 10 J
5  2 10 I
6  9 10 J
7  9 10 I
8  7 10 J
9  6 10 I
10 1  1 J

Answer 1

You may also want to look into the nearZeroVar() function in the caret package.

If you have one event out of 1000, it might be a good idea to discard these data (but this depends on the model). nearZeroVar() can do that.