[R] apply is slower than for loop?

[Duncan Murdoch]

On 09/07/2010 4:11 PM, Gene Leynes wrote:
> I thought the "apply" functions are faster than for loops, but my most
> recent test shows that apply actually takes a significantly longer than a
> for loop.  Am I missing something?
>   

Probably not.  apply() needs to figure out the shape of the results it 
gets from each row in order to put them into the final result matrix.  
You know that in advance, and set up the result to hold them, so your 
calculation would be more efficient.

The *apply functions are designed to be convenient and clear to read, 
not necessarily fast.

Duncan Murdoch

> It doesn't matter much if I do column wise calculations rather than row wise
>
> ## Example of how apply is SLOWER than for loop:
>
> #rm(list=ls())
>
> ## DEFINE VARIABLES
> mu=0.05 ; sigma=0.20 ; dt=.25 ; T=50 ; sims=1e5
> timesteps = T/dt
>
> ## MAKE PHI AND DS
> phi = matrix(rnorm(timesteps*sims), nrow=sims, ncol=timesteps)
> ds = mu*dt + sigma * sqrt(dt) * phi
>
> ## USE APPLY TO CALCULATE ROWWISE CUMULATIVE PRODUCT
> system.time(y1 <- apply(1+ds, 1, cumprod))
> ## UNTRANSFORM Y1, BECAUSE ROW APPLY FLIPS THE MATRIX
> y1=t(y1)
>
> ## USE FOR LOOP TO CALCULATE ROWWISE CUMULATIVE PRODUCT
> y2=matrix(NA,nrow(ds),ncol(ds))
> system.time(
>     for (i in 1:nrow(ds)){
>         y2[i,]<-cumprod(1+ds[i,])
>     }
> )
>
> ## COMPARE RESULTS TO MAKE SURE THEY DID THE SAME THING
> str(y1)
> str(y2)
> all(y1==y2)
>
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