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
[EMAIL PROTECTED]
+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@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@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.
