[R] Improving data processing efficiency

[Daniel Folkinshteyn]

just in case, uploaded it to the server, you can get the zip file i 
mentioned here:
http://astro.temple.edu/~dfolkins/helplistfiles.zip

on 06/06/2008 01:25 PM Daniel Folkinshteyn said the following:
> i thought since the function code (which i provided in full) was pretty 
> short, it would be reasonably easy to just read the code and see what 
> it's doing.
> 
> but ok, so... i am attaching a zip file, with a small sample of the data 
> set (tab delimited), and the function code, in a zip file (posting 
> guidelines claim that "some archive formats" are allowed, i assume zip 
> is one of them...
> 
> would appreciate your comments! :)
> 
> on 06/06/2008 12:05 PM Gabor Grothendieck said the following:
>> Its summarized in the last line to r-help.  Note reproducible and
>> minimal.
>>
>> On Fri, Jun 6, 2008 at 12:03 PM, Daniel Folkinshteyn 
>> <dfolkins at gmail.com> wrote:
>>> i did! what did i miss?
>>>
>>> on 06/06/2008 11:45 AM Gabor Grothendieck said the following:
>>>> Try reading the posting guide before posting.
>>>>
>>>> On Fri, Jun 6, 2008 at 11:12 AM, Daniel Folkinshteyn 
>>>> <dfolkins at gmail.com>
>>>> 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.
>>>>>
>>