On Sun, 31 Dec 2006, Farrel Buchinsky wrote: > I have hundreds of humans who have undergone SNP genotyping at hundreds of > loci. Some have even undergone the procedure twice or thrice (kind of an > internal control). > > So obviously I need to find those replications, and confirm that the results > are the same. If there is discordance then I need to address it.
Why not use duplicated() ? For a data.frame with 200 rows of which about 50 are duplicates and 201 columns finding the (non) duplicates takes little time on my year old AMD 64 running Windows XP: > my.dat <- data.frame(ID=rep(1:100, sample(1:3,100,repl=T))) > snp.dat <- lapply(1:200,function(x) 0:1 ) > snp.frame <- as.data.frame(do.call(cbind,snp.dat)) > my.dat <- cbind( my.dat,snp.frame[sample(nrow(my.dat))%%2+1,]) > system.time( table(duplicated(my.dat)) ) [1] 0.03 0.00 0.03 NA NA > Finding the non-duplicated rows for which there is at least one replication: > system.time( which( (!duplicated(my.dat)) & (my.dat$ID %in% > names(which(table(my.dat$ID)>1)) ) )) [1] 0.05 0.00 0.05 NA NA > > > I tried to use the aggregate function > > nr.attempts > <-aggregate(RawSeq$GENOTYPE_ID,list(sample=RawSeq$SAMPLE_ID,assay=RawSeq$ASSAY_ID),length) > This was simply to figure out how many times the same piece of information > had been obtained. I ran out of patience. It took beyond forever and tapply > did not perform much better. The reshape package did not help - it implied > one was out of luck if the data was not numeric. All of my data is character > or factor. > > Instead I used RODBC > > sqlSave(channel,RawSeq) > to push the table into a Microsoft Access database > Then a sql query, courtesy of the Microsoft Access Query Wizard a la design > mode. > > SELECT RawSeq.SAMPLE_ID, RawSeq.ASSAY_ID, Min(RawSeq.GENOTYPE_ID) AS > MinOfGENOTYPE_ID, Max(RawSeq.GENOTYPE_ID) AS MaxOfGENOTYPE_ID, Count( > RawSeq.rownames) AS CountOfrownames > FROM RawSeq > WHERE (((RawSeq.GENOTYPE_ID)<>"")) > GROUP BY RawSeq.SAMPLE_ID, RawSeq.ASSAY_ID > ORDER BY Count(RawSeq.rownames) DESC; > > This way I could easily use the minimum and maximum values to see if they > were discordant. > Microsoft Access handled it with aplomb. I plan to use RODBC to bring the > result of the SQL query back into R. > > This is the first time I have seen Microsoft Access outpace R. > Is my observation correct or am I missing something. I would much rather > perform all data manipulation and analyses in R. > > > > -- > Farrel Buchinsky > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > Charles C. Berry (858) 534-2098 Dept of Family/Preventive Medicine E mailto:[EMAIL PROTECTED] UC San Diego http://biostat.ucsd.edu/~cberry/ La Jolla, San Diego 92093-0717 ______________________________________________ R-help@stat.math.ethz.ch 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.