R help - Sep 2015 - For Loops please help!!

Also, any time you write "for" in R, you pay a steep price in
performance. In
a short, simple loop it may not be noticeable, but in a more challenging
problem it can be a huge issue.

A more efficient way to write your loop would be:
infectrate = 400*1.1^(1:30) # calculation
cbind(1:30,log(infectrate))    # display

-Dan



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This is not true. The steep price has to do with memory use patterns like result
<- c( result, new value ). Vectorization is cleaner, easier to read, and
somewhat faster, but for loops are not the monster that they have a reputation
for being if the memory is allocated before the loop and elements are updated in
the loop. In particular the more complicated the algorithm inside the loop, the
less incremental overhead the for loop will introduce.
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On September 5, 2015 8:31:15 AM PDT, Dan D <ddalthorp at usgs.gov>
wrote:
>Also, any time you write "for" in R, you pay a steep price in
>performance. In
>a short, simple loop it may not be noticeable, but in a more
>challenging
>problem it can be a huge issue.
>
>A more efficient way to write your loop would be:
>infectrate = 400*1.1^(1:30) # calculation
>cbind(1:30,log(infectrate))    # display
>
>-Dan
>
>
>
>--
>View this message in context:
>http://r.789695.n4.nabble.com/For-Loops-please-help-tp4711882p4711885.html
>Sent from the R help mailing list archive at Nabble.com.
>
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>and provide commented, minimal, self-contained, reproducible code.

Yes, the cause is memory use patterns, but the price is steep nonetheless.

E.g.:

rate<-log(400*1.1^(1:30)) # runs about 27x times as fast as the following
(test via 'microbenchmark') 

rate<-numeric(30)
for (i in 1:30){
   rate[i]<-log(400*1.1^i)
}

When manipulating large arrays, the difference can easily be a few seconds
vs. an hour or more. And if many such arrays need to be run, the difference
is between "difficult" and "not feasible". 

-Dan



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