Gabor, Charles, Whit -- i've been walking the woods of R alone so far, and i
got to say that your replies to that trivial question are eye-opening
experience for me. Gentlemen, what i am trying to say in a roundabout way is
that i am extremely grateful and that you guys are frigging awesome.
Let me outline the times i am getting for different proposed solutions on
the same machine, same data, same version of R
x<-rnorm(50000); len<-100
1. my naive roll.rank
system.time(x.rank.1<-roll.rank(x,len))
user system elapsed
6.405 0.488 6.94
2. Gabor's zoo
z<-zoo(x)
system.time(rollapply(z,len, function(x) rank(x)[len]))
user system elapsed
6.195 0.361 6.554
3. Charles embed
system.time(x.rank <- rowSums(x[ -(1:(len-1)) ] >= embed(x,len) ))
user system elapsed
0.181 0.055 0.236
4. Whit's fts
dat<-fts(x)
system.time(x.rank<-moving.rank(dat, len))
user system elapsed
0.036 0 0.036
5. Charles suggestion with inline, my crude implementation
sig<-signature(x="numeric", rank="integer", n="integer", len="integer")
code<-"int k=0; for(int i=*len-1; i< *n; i++) {int r=1; for(int j=i-1; j>
i-len;j--) r+=(x[i]>x[j] ?1:0); rank[k++]<-r;}"
fns<-cfunction(sig,code, convention=".C")
system.time( x.rank<-fns(x, numeric(length(x)-len), length(x), len))
user system elapsed
0.011 0 0.011
I guess i could speed it up from time being proportional to length(x)*len
to time proportional to length(x)*log(len) if i use slightly more
intelligent algo, but this works fine for my requirements. Only thing i
really wonder about is why exactly R takes 640 times more than this C code.
It would be immensely enlightening if someone could point to an explanation
of how execution in R works and where and when it slows down like this.
--
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