[R] SLOW split() function

[Dennis Murphy]

I tried this:

library(data.table)
    N <- 1000
    T <- N*10
d <- data.table(gp= rep(1:T, rep(N,T)), val=rnorm(N*T), key = 'gp')
dim(d)
[1] 10000000        2

# On my humble 8Gb system,
> system.time(l <- d[, split(val, gp)])
   user  system elapsed
   4.15    0.09    4.27

I wouldn't be surprised if there were a much faster way to do this
operation in data.table since split() is a data frame operation. This
is about as fast as Jim Holtman's suggestion:

system.time(s <- split(seq_len(nrow(d)), d$gp))
   user  system elapsed
   4.15    0.09    4.29

HTH,
Dennis

On Mon, Oct 10, 2011 at 6:01 PM, ivo welch <ivo.welch at gmail.com> wrote:
> dear R experts:  apologies for all my speed and memory questions.  I
> have a bet with my coauthors that I can make R reasonably efficient
> through R-appropriate programming techniques.  this is not just for
> kicks, but for work.  for benchmarking, my [3 year old] Mac Pro has
> 2.8GHz Xeons, 16GB of RAM, and R 2.13.1.
>
> right now, it seems that 'split()' is why I am losing my bet.  (split
> is an integral component of *apply() and by(), so I need split() to be
> fast.  its resulting list can then be fed, e.g., to mclapply().)  I
> made up an example to illustrate my ills:
>
>    library(data.table)
>    N <- 1000
>    T <- N*10
>    d <- data.table(data.frame( key= rep(1:T, rep(N,T)), val=rnorm(N*T) ))
>    setkey(d, "key"); gc() ## force a garbage collection
>    cat("N=", N, ".  Size of d=", object.size(d)/1024/1024, "MB\n")
>    print(system.time( s<-split(d, d$key) ))
>
> My ordered input data table (or data frame; doesn't make a difference)
> is 114MB in size.  it takes about a second to create.  split() only
> needs to reshape it.  this simple operation takes almost 5 minutes on
> my computer.
>
> with a data set that is larger, this explodes further.
>
> am I doing something wrong?  is there an alternative to split()?
>
> sincerely,
>
> /iaw
>
> ----
> Ivo Welch (ivo.welch at gmail.com)
>
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