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Learn Vectorization (Vectorize)

3 messages · Alaios, Henrique Dallazuanna, Bert Gunter

Alaios
# 
Greetings Friends,
I would be grateful if you can help me undestand how to make my R code more efficiently.

I have read in R intoductory tutorial that a for loop is not used so ofter (and is not maybe not that efficient) compared to other languages.

So I am trying to build understanding how to get the equivalent of a for loop using more R-oriented thinking.

If I got it right one way to do that in R is Vectorize. So I have writen a small snippet of code with two nested for loops. I would be grateful if you can help me find the equivalent of this code using Vectorize (or any other R thinking)

My code takes as input a n*m matrix and prints the x,y coordinates where a cells starts and ends.



remap <- function (sr){
# Input this funcion takes as arguments
# sr: map
  startpos<- -1 # 
  endpos<- +1 # 
  stepx<- (endpos - (startpos)) / nrow(sr)
  stepy<- (endpos - (startpos)) / ncol(sr)

  for (i in seq(from=-1,to=1,by=stepx) ) { 
    for (j in seq(from=-1,to=1,by=stepx) ){
       cat(' \n',i,j)
    }
   
  }
}
sr<-matrix(data=seq(from=1,to=9),nrow=3,ncol=3,byrow=TRUE)
remap(sr)

Regards
Alex
Bert Gunter
# 
Inline below.

-- Bert
On Tue, Jan 25, 2011 at 7:48 AM, Henrique Dallazuanna <wwwhsd at gmail.com> wrote:
-- You got it wrong. Vectorize() is just another (disguised) way of
doing R level looping, via apply functions.It offers no efficiency
advantage over explicit for loops, though it may make for cleaner
programming, which IS a great advantage in the bigger scheme of
things, I admit. But thatwasn't your question.

The basic idea of vectorization in R is to use **built-in** commands
that act on whole objects through their internal (usually C) code.
These **will** be much faster than looping in R.

So for example to generate 100000 random numbers one can do:
user  system elapsed
   0.02    0.00    0.02


## OR
+                       y <- numeric(1e5)
+                       for(i in 1:(1e5))y[i] <- rnorm(1)
+                       })
   user  system elapsed
   0.86    0.01    0.87


The former is vectorized, producing all 100000 randoms at a go and
runs 40 times faster than the latter, which is not, instead producing
one random number at a time (and requiring the overhead of a separate
call for each, I think).

Please read an Intro to R for more. V&R's MASS also has some helpful
remarks on vectorization. I'm sure that other texts (e.g. Dalgaard's)
are worth consulting on this, too, but I haven't read them.

-




 So I have writen a