[R] Improving data processing efficiency

[Daniel Folkinshteyn]

Thanks, I'll take a look at Rprof... but I think what i'm missing is 
facility with R idiom to get around the looping, and no amount of 
profiling will help me with that :)

also, full working code is provided in my original post (see toward the 
bottom).

on 06/05/2008 03:43 PM bartjoosen said the following:
> Maybe you should provide a minimal, working code with data, so that we all
> can give it a try.
> In the mean time: take a look at the Rprof function to see where your code
> can be improved.
> 
> Good luck
> 
> Bart
> 
> 
> Daniel Folkinshteyn-2 wrote:
>> 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 =============
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>