R: Help using dummyVars and adding back into data.frame - r

Question

I have a data.frame of 373127 obs. of 193 variables. Some variables are factors which I want to use dummyVars() to separate each factor into its own column. I then want to merge the separate dummy variable columns back into my original data.frame, so I thought I could do the whole thing with apply, but something is not working and I can't figure out what it is.
Sample:
dat_final <- apply(dummies.var1, 1, function(x) {
dummies.var1 <- dummyVars(~ dat1$factor.var1 -1, data = dat1)
})
Thanks!

Answer 1

You can do the following that will create a new df, trsf, but you could always reassign back to the original df:
library(caret)
customers <- data.frame(
id=c(10,20,30,40,50),
gender=c('male','female','female','male','female'),
mood=c('happy','sad','happy','sad','happy'),
outcome=c(1,1,0,0,0))
# dummify the data
dmy <- dummyVars(" ~ .", data = customers)
trsf <- data.frame(predict(dmy, newdata = customers))
print(trsf)
See more here

Answer 2

The real answer is .... Don't do that. It's almost never necessary.

Answer 3

You could do something like this:
# Example data
df = data.frame(x = rep(LETTERS, each = 3), y = rnorm(78))
df = cbind(df, model.matrix(~df$x - 1))
However, as pointed out by #user30257 it is hard to see why you want to do it. In general, modeling tools in R don't need dummy vars, but deal with factors directly.

Answer 4

Creating dummy variables can be very important in feature selection, which it sounds like the original poster was doing.
For instance, suppose you have a feature that contains duplicated information (i.e., one of its levels corresponds to something measured elsewhere). You can determine this is the case very simply by comparing the dummy variables for these features using a variety of dissimilarity measures.
My preference is to use:
sparse.model.matrix and
cBind