Motivation: during each iteration, my code needs to collect tabular data (and
use it only during that iteration), but the rows of data may vary. I thought I
would speed it up by preinitializing the matrix that collects the data with
zeros to what I know to be the maximum number of rows. I was surprised by what
I found...
# set up (not the puzzling part)
x<-matrix(runif(20),nrow=4); y<-matrix(0,nrow=12,ncol=5); foo<-c();
# this is what surprises me... what the?
> system.time(for(i in 1:100000){n<-sample(1:4,1);y[1:n,]<-x[1:n,];});
user system elapsed
1.510 0.000 1.514
> system.time(for(i in 1:100000){n<-sample(1:4,1);foo<-x[1:n,];});
user system elapsed
1.090 0.000 1.085
These results are very repeatable. So, if I'm interpreting them correctly,
dynamically allocating 'foo' each time to whatever the current output size is
runs faster than writing to a subset of a preallocated 'y'? How is that
possible?
And, more generally, I'm sure other people have encountered this type of
situation. Am I reinventing the wheel? Is there a best practice for storing
temporary loop-specific data?
Thanks.
PS: By the way, though I cannot write to foo[,] because the size is different
each time, I tried writing to foo[] and the runtime was worse than either of
the above examples.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
