Re: [R] Improving data processing efficiency

From: Daniel Folkinshteyn <dfolkins_at_gmail.com>
Date: Fri, 06 Jun 2008 11:12:32 -0400

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 =============
>



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