“For” calculation is so slow

system.time(d<-10000000*10000001/2)

identical(a,b,d)

Hi

For loops are really, really slow in R. In general, you want to avoid 

them

I strongly disagree. ***Proper*** use of looping is quite convenient and 

reasonably fast.

Consider

system.time( {

+ a=0
+ for (i in 1:10000000) {
+ a <-a+i
+ }
+ a
+ })
   user  system elapsed 
  10.22    0.02   10.28 

system.time(b<-sum(as.numeric(1:10000000)))

   user  system elapsed 
   0.09    0.01    0.11 

identical(a,b)

[1] TRUE

It is usually implementing C habits into R code what makes looping slow. 

The slowest part of a program is usually programming and it is 

mainly by programmer.

Regards
Petr

like the plague. If you absolutely must insist on using them in large,
computationally intense and complex code, consider implementing the 

relevant

parts in C, say, and calling that from R.

Staying within R, you can probably considerably speed up that code by
storing gx and gy as a multi-dimensional arrays. (e.g. for sample 

something like

rawGy = sample( 1:240, 240^2* 241, replace = T)
rawGx = sample( 1:240, 240^2 *241, replace = T)
gx = array(rawGx, dim = c(length(s) - 1, 240,  max(rawGx)+1  ) )
gy = array(rawGy, dim = c(length(s) - 1, 240,  max(rawGy)+1 ) )

 ), in which case, you can easily do the computation without loops by

gxa = (gx[ ,a,1]+ 1)
gya =(gy[ ,a, 1] +1)
uv = gx[cbind(1:(length(s) - 1) , b, gxa)] / gx[cbind(1:(length(s) - 

, a,

gxa)]  - gy[cbind(1:(length(s) - 1) ,b, gya)]/gy[cbind(1:(length(s) - 

,a,

gya)]

or similar, which will be enormously faster (on my computer, there's 

over

30x speed up). With a bit of thought, I'm sure you can also figure out 

how

to let it vectorise in a, as well...

Zhou

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