# [R] Find number of elements less than some number: Elegant/fastsolution needed

Douglas Bates bates at stat.wisc.edu
Fri Apr 15 00:22:53 CEST 2011

```My colleague Sunduz Keles once mentioned a similar problem to me.  She
had a large sample from a reference distribution and a test sample
(both real-valued in her case) and she wanted, for each element of the
test sample, the proportion of the reference sample that was less than
the element.  It's a type of empirical p-value calculation.

I forget the exact sizes of the samples (do you remember, Sunduz?) but
they were in the tens of thousands or larger.  Solutions in R tended
to involve comparing every element in the test sample to every element
in the reference sample but, of course, that is unnecessary.  If you
sort both samples then you can start the comparisons for a particular
element of the test sample at the element of the reference sample
where the last comparison failed.

I was able to write a very short C++ function using the Rcpp package
that provided about a 1000-fold increase in speed relative to the best
I could do in R.  I don't have the script on this computer so I will
post it tomorrow when I am back on the computer at the office.

Apologies for cross-posting to the Rcpp-devel list but I am doing so
because this might make a good example of the usefulness of Rcpp and
inline.

On Thu, Apr 14, 2011 at 4:45 PM, William Dunlap <wdunlap at tibco.com> wrote:
>> -----Original Message-----
>> From: r-help-bounces at r-project.org
>> [mailto:r-help-bounces at r-project.org] On Behalf Of Kevin Ummel
>> Sent: Thursday, April 14, 2011 12:35 PM
>> To: r-help at r-project.org
>> Subject: [R] Find number of elements less than some number:
>> Elegant/fastsolution needed
>>
>> Take vector x and a subset y:
>>
>> x=1:10
>>
>> y=c(4,5,7,9)
>>
>> For each value in 'x', I want to know how many elements in
>> 'y' are less than 'x'.
>>
>> An example would be:
>>
>> sapply(x,FUN=function(i) {length(which(y<i))})
>>   0 0 0 0 1 2 2 3 3 4
>>
>> But this solution is far too slow when x and y have lengths
>> in the millions.
>>
>> I'm certain an elegant (and computationally efficient)
>> solution exists, but I'm in the weeds at this point.
>
> If x is sorted findInterval(x, y) would do it for <= but you
> want strict <.  Try
>  f2 <- function(x, y) length(y) - findInterval(-x, rev(-sort(y)))
>  f0 <- function(x, y) sapply(x,FUN=function(i) {length(which(y<i))})
> E.g.,
>  > x <- sort(sample(1e6, size=1e5))
>  > y <- sample(1e6, size=1e4, replace=TRUE)
>  > system.time(r0 <- f0(x,y))
>     user  system elapsed
>    7.900   0.310   8.211
>  > system.time(r2 <- f2(x,y))
>     user  system elapsed
>    0.000   0.000   0.004
>  > identical(r0, r2)
>   TRUE
>
> Bill Dunlap
> Spotfire, TIBCO Software
> wdunlap tibco.com
>
>>
>> Any help is much appreciated.
>>
>> Kevin
>>
>> University of Manchester
>>
>>
>>
>>
>>
>>
>>
>>
>> Take two vectors x and y, where y is a subset of x:
>>
>> x=1:10
>>
>> y=c(2,5,6,9)
>>
>> If y is removed from x, the original x values now have a new
>> placement (index) in the resulting vector (new):
>>
>> new=x[-y]
>>
>> index=1:length(new)
>>
>> The challenge is: How can I *quickly* and *efficiently*
>> deduce the new 'index' value directly from the original 'x'
>> value -- using only 'y' as an input?
>>
>> In practice, I have very large matrices containing the 'x'
>> values, and I need to convert them to the corresponding
>> 'index' if the 'y' values are removed.
>>
>>
>>
>>
>>       [[alternative HTML version deleted]]
>>
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>>
>
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