R - show only levels used in a subset of data frame

Question

I have a rather large data frame with a factor that has a lot of levels (more than 4,000). I have another column in the same data frame that I'm using as a reference, and what I'd like to find is a subset of the levels whenever this reference column is NA.

The first step I'm using is subsetrows <- which(is.na(mydata$reference)) but after that I'm stuck. I want something like levels(mydata[subsetrows,mydata$factor]) but unfortunately, this command shows me all the levels and not just the ones existing in subsetrows. I suppose I could create a new vector outside of my data frame of only my subset rows and then drop any unused levels, but is there any easier/cleaner way to do this, possibly without copying my data outside the data frame?

As an example of what I want returned, if my data frame has factor levels from A to Z, but in my subset only P, R and Y appear, I want something that returns the levels P, R and Y.

maybe this could help stackoverflow.com/questions/1195826/… — NicE
Could you try levels(mydata$factor)[mydata$factor[subsetrows]]? — Marat Talipov
Well, using your previous idea of unique, this turns out to give me the right levels: unique(as.character(mydata$factor[subsetrows])) — Alium Britt
In fact, as.character.factor is a wrap-up for levels(x)[x] — Marat Talipov
@thelatemail, I don't think so: subsetting mydata$factor[subsetrows] returns a subset of factors, i.e. numeric indices that serve as a shortcut for characters stored in levels, that is guaranteed to be within the length of levels(x) and is guaranteed to match the proper level. In fact, as I noted before, as.character(mydata$factor[subsetrows]) calls as.character.factor, whose definition is function(x) levels(x)[x]. Thus, my solution and the one proposed by Alium (which I actually like more because of its compactness) are essentially the same thing — Marat Talipov

davechilders davechilders · Accepted Answer · 2015-02-17T21:45:50

You can certainly accomplish this with base functions. But my personal preference is to use dplyr with chained operations such as this:

library(dplyr)

d %>%
  filter(is.na(ref)) %>%
  select(field) %>%
  distinct()

data

d <- data.frame(
  field = c("A", "B", "C", "A", "B", "C"),
  ref = c(NA, "a", "b", NA, "c", NA)
  )

R - show only levels used in a subset of data frame

2 Answers