Brad Thompson wrote:
In an R program I am working on, I progressively build up several
vectors of integers. I never know how long the vectors will eventually
be, so I can't preallocate them with vector(). If I preallocate all of
the vectors to their maximum size, I will run out of memory. I tried
using c() or append() to build up the vectors, as in the following (tried
on R 2.1.0 on Linux and R 2.2.1 on MacOS 10):
li - vector() # also tried list() and pairlist()
for (i in 0:4) {
li - c(li, i) # also tried with i and li swapped
if (i %% 1 == 0)
system('date')
}
The problem is that based on the times this outputs, it is O(n^2),
matching the straightforward implementation of c() where everything
passed to c() is copied.
I tried extending the vector by assigning to length(li) instead of
using c(), but that also runs in O(n) (so the loop runs in O(n^2)) and
appears to copy the elements.
What I am looking for is an array that can dynamically resized in
amortized constant or log time (like python's list or C++'s
std::vector). I could build up a data structure inside R (the same way
std::vector is built on top of C arrays), but I was hoping someone
might have some advice on a better way to do this.
Does R have a resizeable array type that I'm missing? What do others
generally do in this case?
Thank you,
Brad
Hi, Brad,
You can add length as needed as in:
## create a buffer
li - vector(numeric, 1000)
t1 - proc.time()[3]
for (i in 1:99) {
## add more space if needed
if(i length(li)) length(li) - length(li) + 1000
li[i] - i
if(i %% 1 == 0) {
t2 - proc.time()[3]
cat(sprintf(n = %6d; time = %4.2f\n, i, t2 - t1))
## flush.console() ## Windows only, I think
t1 - t2
}
}
## resize 'li'
length(li) - i
HTH,
--sundar
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