Re: [R] Improving data processing efficiency

From: Daniel Folkinshteyn <dfolkins_at_gmail.com>
Date: Fri, 06 Jun 2008 13:29:56 -0400

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. Received on Fri 06 Jun 2008 - 17:36:18 GMT

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.2.0, at Fri 06 Jun 2008 - 19:30:39 GMT.

Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.

list of date sections of archive