r - Idiom for ifelse-style recoding for multiple categories

Question

I run across this often enough that I figure there has to be a good idiom for it. Suppose I have a data.frame with a bunch of attributes, including "product." I also have a key which translates products to brand + size. Product codes 1-3 are Tylenol, 4-6 are Advil, 7-9 are Bayer, 10-12 are Generic.

What's the fastest (in terms of human time) way to code this up?

I tend to use nested ifelse's if there are 3 or fewer categories, and type out the data table and merge it in if there are more than 3. Any better ideas? Stata has a recode command that is pretty nifty for this sort of thing, although I believe it promotes data-code intermixing a little too much.

dat <- structure(list(product = c(11L, 11L, 9L, 9L, 6L, 1L, 11L, 5L, 
7L, 11L, 5L, 11L, 4L, 3L, 10L, 7L, 10L, 5L, 9L, 8L)), .Names = "product", row.names = c(NA, 
-20L), class = "data.frame")

Answer 1

You could convert your variable to a factor and change its levels by levels<- function. In one command it could be like:

`levels<-`(
    factor(dat$product),
    list(Tylenol=1:3, Advil=4:6, Bayer=7:9, Generic=10:12)
)

In steps:

brands <- factor(dat$product)
levels(brands) <- list(Tylenol=1:3, Advil=4:6, Bayer=7:9, Generic=10:12)