In R, I have 600,000 categorical variables, each of which is classified as "0", "1", or "2".
What I would like to do is collapse "1" and "2" and leave "0" by itself, such that after re-categorizing "0" = "0"; "1" = "1" and "2" = "1". In the end I only want "0" and "1" as categories for each of the variables.
Also, if possible, I would rather not create 600,000 new variables, if I can replace the existing variables with the new values that would be great!
What would be the best way to do this?
There is a function recode in package car (Companion to Applied Regression):
require("car")
recode(x, "c('1','2')='1'; else='0'")
or for your case in plain R:
> x <- factor(sample(c("0","1","2"), 10, replace=TRUE))
> x
[1] 1 1 1 0 1 0 2 0 1 0
Levels: 0 1 2
> factor(pmin(as.numeric(x), 2), labels=c("0","1"))
[1] 1 1 1 0 1 0 1 0 1 0
Levels: 0 1
Update: To recode all categorical columns of a data frame tmp you can use the following
recode_fun <- function(x) factor(pmin(as.numeric(x), 2), labels=c("0","1"))
require("plyr")
catcolwise(recode_fun)(tmp)