thanks for the suggestions! I'll play with this over the weekend and see
what comes out. :)
on 06/06/2008 06:48 PM Don MacQueen said the following:
In a case like this, if you can possibly work with matrices instead of
data frames, you might get significant speedup.
(More accurately, I have had situations where I obtained speed up by
working with matrices instead of dataframes.)
Even if you have to code character columns as numeric, it can be worth it.
Data frames have overhead that matrices do not. (Here's where profiling
might have given a clue) Granted, there has been recent work in reducing
the overhead associated with dataframes, but I think it's worth a try.
Carrying along extra columns and doing row subsetting, rbinding, etc,
means a lot more things happening in memory.
So, for example, if all of your matching is based just on a few columns,
extract those columns, convert them to a matrix, do all the matching,
and then based on some sort of row index retrieve all of the associated
columns.
-Don
At 2:09 PM -0400 6/5/08, Daniel Folkinshteyn 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.
