What is the most efficient way to split a combined factor column into two factor columns in an r data.table?

Question

I have a large data.table (9 M lines) with two columns: fcombined and value fcombined is a factor, but its actually the result of interacting two factors. The question now is what is the most efficient way to split up the one factor column in two again? I have already come up with a solution that works ok, but maybe there is more straight forward way that i have missed. The working example is:

library(stringr)
f1=1:20
f2=1:20
g=expand.grid(f1,f2)
combinedfactor=as.factor(paste(g$Var1,g$Var2,sep="_"))
largedata=1:10^6
DT=data.table(fcombined=combinedfactor,value=largedata)


splitfactorcol=function(res,colname,splitby="_",namesofnewcols){#the nr. of cols retained is length(namesofnewcols)
  helptable=data.table(.factid=seq_along(levels(res[[colname]])) ,str_split_fixed(levels(res[[colname]]),splitby,length(namesofnewcols)))
  setnames(helptable,colnames(helptable),c(".factid",namesofnewcols))
  setkey(helptable,.factid)
  res$.factid=unclass(res[[colname]])
  setkey(res,.factid)
  m=merge(res,helptable)
  m$.factid=NULL
  m
}
splitfactorcol(DT,"fcombined",splitby="_",c("f1","f2"))

Answer 1

I think this does the trick and is about 5x faster.

setkey(DT, fcombined)
DT[DT[, data.table(fcombined = levels(fcombined),
                   do.call(rbind, strsplit(levels(fcombined), "_")))]]

I split the levels and then simply merged that result back into the original data.table.

Btw, in my tests strsplit was about 2x faster (for this task) than the stringr function.