[R] Improving data processing efficiency
Daniel Folkinshteyn
dfolkins at gmail.com
Sat Jun 7 00:10:25 CEST 2008
Hmm... ok... so i ran the code twice - once with a preallocated result,
assigning rows to it, and once with a nrow=0 result, rbinding rows to
it, for the first 20 quarters. There was no speedup. In fact, running
with a preallocated result matrix was slower than rbinding to the matrix:
for preallocated matrix:
Time difference of 1.577779 mins
for rbinding:
Time difference of 1.498628 mins
(the time difference only counts from the start of the loop til the end,
so the time to allocate the empty matrix was /not/ included in the time
count).
So, it appears that rbinding a matrix is not the bottleneck. (That it
was actually faster than assigning rows could have been a random anomaly
(e.g. some other process eating a bit of cpu during the run?), or not -
at any rate, it doesn't make an /appreciable/ difference.
Any other suggestions? :)
on 06/06/2008 02:03 PM Patrick Burns said the following:
> That is going to be situation dependent, but if you
> have a reasonable upper bound, then that will be
> much easier and not far from optimal.
>
> If you pick the possibly too small route, then increasing
> the size in largish junks is much better than adding
> a row at a time.
>
> Pat
>
> Daniel Folkinshteyn wrote:
>> thanks for the tip! i'll try that and see how big of a difference that
>> makes... if i am not sure what exactly the size will be, am i better
>> off making it larger, and then later stripping off the blank rows, or
>> making it smaller, and appending the missing rows?
>>
>> on 06/06/2008 11:44 AM Patrick Burns said the following:
>>> One thing that is likely to speed the code significantly
>>> is if you create 'result' to be its final size and then
>>> subscript into it. Something like:
>>>
>>> result[i, ] <- bestpeer
>>>
>>> (though I'm not sure if 'i' is the proper index).
>>>
>>> Patrick Burns
>>> patrick at burns-stat.com
>>> +44 (0)20 8525 0696
>>> http://www.burns-stat.com
>>> (home of S Poetry and "A Guide for the Unwilling S User")
>>>
>>> Daniel Folkinshteyn wrote:
>>>> Anybody have any thoughts on this? Please? :)
>>>>
>>>> on 06/05/2008 02:09 PM Daniel Folkinshteyn said the following:
>>>>> Hi everyone!
>>>>>
>>>>> I have a question about data processing efficiency.
>>>>>
>>>>> My data are as follows: I have a data set on quarterly
>>>>> institutional ownership of equities; some of them have had recent
>>>>> IPOs, some have not (I have a binary flag set). The total dataset
>>>>> size is 700k+ rows.
>>>>>
>>>>> My goal is this: For every quarter since issue for each IPO, I need
>>>>> to find a "matched" firm in the same industry, and close in market
>>>>> cap. So, e.g., for firm X, which had an IPO, i need to find a
>>>>> matched non-issuing firm in quarter 1 since IPO, then a (possibly
>>>>> different) non-issuing firm in quarter 2 since IPO, etc. Repeat for
>>>>> each issuing firm (there are about 8300 of these).
>>>>>
>>>>> Thus it seems to me that I need to be doing a lot of data selection
>>>>> and subsetting, and looping (yikes!), but the result appears to be
>>>>> highly inefficient and takes ages (well, many hours). What I am
>>>>> doing, in pseudocode, is this:
>>>>>
>>>>> 1. for each quarter of data, getting out all the IPOs and all the
>>>>> eligible non-issuing firms.
>>>>> 2. for each IPO in a quarter, grab all the non-issuers in the same
>>>>> industry, sort them by size, and finally grab a matching firm
>>>>> closest in size (the exact procedure is to grab the closest bigger
>>>>> firm if one exists, and just the biggest available if all are smaller)
>>>>> 3. assign the matched firm-observation the same "quarters since
>>>>> issue" as the IPO being matched
>>>>> 4. rbind them all into the "matching" dataset.
>>>>>
>>>>> The function I currently have is pasted below, for your reference.
>>>>> Is there any way to make it produce the same result but much
>>>>> faster? Specifically, I am guessing eliminating some loops would be
>>>>> very good, but I don't see how, since I need to do some fancy
>>>>> footwork for each IPO in each quarter to find the matching firm.
>>>>> I'll be doing a few things similar to this, so it's somewhat
>>>>> important to up the efficiency of this. Maybe some of you R-fu
>>>>> masters can clue me in? :)
>>>>>
>>>>> I would appreciate any help, tips, tricks, tweaks, you name it! :)
>>>>>
>>>>> ========== my function below ===========
>>>>>
>>>>> fcn_create_nonissuing_match_by_quarterssinceissue =
>>>>> function(tfdata, quarters_since_issue=40) {
>>>>>
>>>>> result = matrix(nrow=0, ncol=ncol(tfdata)) # rbind for matrix
>>>>> is cheaper, so typecast the result to matrix
>>>>>
>>>>> colnames = names(tfdata)
>>>>>
>>>>> quarterends = sort(unique(tfdata$DATE))
>>>>>
>>>>> for (aquarter in quarterends) {
>>>>> tfdata_quarter = tfdata[tfdata$DATE == aquarter, ]
>>>>>
>>>>> tfdata_quarter_fitting_nonissuers = tfdata_quarter[
>>>>> (tfdata_quarter$Quarters.Since.Latest.Issue > quarters_since_issue)
>>>>> & (tfdata_quarter$IPO.Flag == 0), ]
>>>>> tfdata_quarter_ipoissuers = tfdata_quarter[
>>>>> tfdata_quarter$IPO.Flag == 1, ]
>>>>>
>>>>> for (i in 1:nrow(tfdata_quarter_ipoissuers)) {
>>>>> arow = tfdata_quarter_ipoissuers[i,]
>>>>> industrypeers = tfdata_quarter_fitting_nonissuers[
>>>>> tfdata_quarter_fitting_nonissuers$HSICIG == arow$HSICIG, ]
>>>>> industrypeers = industrypeers[
>>>>> order(industrypeers$Market.Cap.13f), ]
>>>>> if ( nrow(industrypeers) > 0 ) {
>>>>> if (
>>>>> nrow(industrypeers[industrypeers$Market.Cap.13f >=
>>>>> arow$Market.Cap.13f, ]) > 0 ) {
>>>>> bestpeer =
>>>>> industrypeers[industrypeers$Market.Cap.13f >= arow$Market.Cap.13f,
>>>>> ][1,]
>>>>> }
>>>>> else {
>>>>> bestpeer = industrypeers[nrow(industrypeers),]
>>>>> }
>>>>> bestpeer$Quarters.Since.IPO.Issue =
>>>>> arow$Quarters.Since.IPO.Issue
>>>>>
>>>>> #tfdata_quarter$Match.Dummy.By.Quarter[tfdata_quarter$PERMNO ==
>>>>> bestpeer$PERMNO] = 1
>>>>> result = rbind(result, as.matrix(bestpeer))
>>>>> }
>>>>> }
>>>>> #result = rbind(result, tfdata_quarter)
>>>>> print (aquarter)
>>>>> }
>>>>>
>>>>> result = as.data.frame(result)
>>>>> names(result) = colnames
>>>>> return(result)
>>>>>
>>>>> }
>>>>>
>>>>> ========= end of my function =============
>>>>>
>>>>
>>>> ______________________________________________
>>>> R-help at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>
>>>>
>>>
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
More information about the R-help
mailing list