?cut
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Jeff Newmiller The ..... ..... Go Live...
DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. Live Go...
Live: OO#.. Dead: OO#.. Playing
Research Engineer (Solar/Batteries O.O#. #.O#. with
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Sent from my phone. Please excuse my brevity.
Jonathan Greenberg <j...@illinois.edu> wrote:
>Folks:
>
>Say I have a set of histogram breaks:
>
>breaks=c(1:10,15)
>
># With bin ids:
>
>bin_ids=1:(length(breaks)-1)
>
># and some data (note that some of it falls outside the breaks:
>
>data=runif(min=1,max=20,n=100)
>
>***
>
>What is the MOST EFFICIENT way to "classify" data into the histogram
>bins
>(return the bin_ids) and, say, return NA if the value falls outside of
>the
>bins.
>
>By classify, I mean if the data value is greater than one break, and
>less
>than or equal to the next break, it gets assigned that bin's ID (note
>that
>length(breaks) = length(bin_ids)+1)
>
>Also note that, as per this example, the bins are not necessarily equal
>widths.
>
>I can, of course, cycle through each element of data, and then move
>through
>breaks, stopping when it finds the correct bin, but I feel like there
>is
>probably a faster (and more elegant) approach to this. Thoughts?
>
>--j
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