[Rd] Fastest non-overlapping binning mean function out there?
Hervé Pagès
hpages at fhcrc.org
Wed Oct 3 03:11:14 CEST 2012
Hi Henrik,
On 10/02/2012 05:19 PM, Henrik Bengtsson wrote:
> Hi,
>
> I'm looking for a super-duper fast mean/sum binning implementation
> available in R, and before implementing z = binnedMeans(x y) in native
> code myself, does any one know of an existing function/package for
> this? I'm sure it already exists. So, given data (x,y) and B bins
> bx[1] < bx[2] < ... < bx[B] < bx[B+1], I'd like to calculate the
> binned means (or sums) 'z' such that z[1] = mean(x[bx[1] <= x & x <
> bx[2]]), z[2] = mean(x[bx[2] <= x & x < bx[3]]), .... z[B]. Let's
> assume there are no missing values and 'x' and 'bx' is already
> ordered. The length of 'x' is in the order of 10,000-millions. The
> number of elements in each bin vary.
You didn't say if you have a lot of bins or not. If you don't have a lot
of bins (e.g. < 10000), something like
aggregate(x, by=list(bin=findInterval(x, bx)), FUN=mean)
might not be too bad:
> x <- seq(0, 8, by=0.1)
> bx <- c(2, 2.5, 4, 5.8)
> aggregate(x, by=list(bin=findInterval(x, bx)), FUN=mean)
bin x
1 0 0.95
2 1 2.20
3 2 3.20
4 3 4.85
5 4 6.90
I didn't try it on a 10,000-millions-elements vector though (and I've
no idea how I could do this).
H.
>
> Thanks,
>
> Henrik
>
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--
Hervé Pagès
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
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