Best way to coerce numerical data to a predetermined histogram bin?
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Sent from my phone. Please excuse my brevity.
Jonathan Greenberg <jgrn at 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