Efficient method to subset drop rows with NA values in R

Question

Background Before running a stepwise model selection, I need to remove missing values for any of my model terms. With quite a few terms in my model, there are t

Answer 1

Edit: I completely glossed over subset, the built in function that is made for sub-setting things:

my.df <- subset(my.df, 
  !(is.na(termA) |
    is.na(termB) |
    is.na(termC) )
  )

---

I tend to use with() for things like this. Don't use attach, you're bound to cut yourself.

my.df <- my.df[with(my.df, {
  !(is.na(termA) |
    is.na(termB) |
    is.na(termC) )
}), ]

But if you often do this, you might also want a helper function, is_any()

is_any <- function(x){
  !is.na(x)
}

If you end up doing a lot of this sort of thing, using SQL is often going to be a nicer interaction with subsets of data. dplyr may also prove useful.