Speed up the loop operation in R

Question

I have a big performance problem in R. I wrote a function that iterates over a data.frame object. It simply adds a new column to a data.frame and a

Answer 1

I dislike rewriting code... Also of course ifelse and lapply are better options but sometimes it is difficult to make that fit.

Frequently I use data.frames as one would use lists such as df$var[i]

Here is a made up example:

nrow=function(x){ ##required as I use nrow at times.
  if(class(x)=='list') {
    length(x[[names(x)[1]]])
  }else{
    base::nrow(x)
  }
}

system.time({
  d=data.frame(seq=1:10000,r=rnorm(10000))
  d$foo=d$r
  d$seq=1:5
  mark=NA
  for(i in 1:nrow(d)){
    if(d$seq[i]==1) mark=d$r[i]
    d$foo[i]=mark
  }
})

system.time({
  d=data.frame(seq=1:10000,r=rnorm(10000))
  d$foo=d$r
  d$seq=1:5
  d=as.list(d) #become a list
  mark=NA
  for(i in 1:nrow(d)){
    if(d$seq[i]==1) mark=d$r[i]
    d$foo[i]=mark
  }
  d=as.data.frame(d) #revert back to data.frame
})

data.frame version:

   user  system elapsed 
   0.53    0.00    0.53

list version:

   user  system elapsed 
   0.04    0.00    0.03 

17x times faster to use a list of vectors than a data.frame.

Any comments on why internally data.frames are so slow in this regard? One would think they operate like lists...

For even faster code do this class(d)='list' instead of d=as.list(d) and class(d)='data.frame'

system.time({
  d=data.frame(seq=1:10000,r=rnorm(10000))
  d$foo=d$r
  d$seq=1:5
  class(d)='list'
  mark=NA
  for(i in 1:nrow(d)){
    if(d$seq[i]==1) mark=d$r[i]
    d$foo[i]=mark
  }
  class(d)='data.frame'
})
head(d)