Sherri -
Here's one way:
nms = c('rs123','rs157','rs132')
lowf = function(one,two){
both = paste(pop[[one]],pop[[two]],sep='')
tt = table(both)
lowfreq = names(tt)[which.min(tt)]
ifelse(both == lowfreq,1,0)
}
res = mapply(lowf,nms,paste(nms,'.1',sep=''),SIMPLIFY=FALSE)
names(res) = paste(names(res),'_new',sep='')
pop = data.frame(pop,res)
It doesn't deal with the case of ties with regard to the frequency
of the SNP value, but it should be easy to modify if that's
an issue.
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Sat, 27 Feb 2010, Sherri Rose wrote:
*Combining 2 columns into 1 column many times in a very large dataset*
The clumsy solutions I am working on are not going to be very fast if I can
get them to work and the true dataset is ~1500 X 45000 so they need to be
efficient. I've searched the R help files and the archives for this list and
have some possible workable solutions for 2) and 3) but not my question 1).
However, I include 2) and 3) in case anyone has recommendations that would
be efficient.
Here is a toy example of the data structure:
pop = data.frame(status = rbinom(n, 1, .42), sex = rbinom(n, 1, .5),
age = round(rnorm(n, mean=40, 10)), disType = rbinom(n, 1, .2),
rs123=c(1,3,1,3,3,1,1,1,3,1), rs123.1=rep(1, n),
rs157=c(2,4,2,2,2,4,4,4,2,2),
rs157.1=c(4,4,4,2,4,4,4,4,2,2), rs132=c(4,4,4,4,4,4,4,4,2,2),
rs132.1=c(4,4,4,4,4,4,4,4,4,4))
Thus, there are a few columns of basic demographic info and then the rest of
the columns are biallelic SNP info. Ex: rs123 is allele 1 of rs123 and
rs123.1 is the second allele of rs123.
1) I need to merge all the biallelic SNP data that is currently in 2 columns
into 1 column, so, for example: rs123 and rs123.1 into one column (but
within the dataset):
11
31
11
31
31
11
11
11
31
11
2) I need to identify the least frequent SNP value (in the above example it
is 31).
3) I need to replace the least frequent SNP value with 1 and the other(s)
with 0.
Thank you for any assistance,
-S.R.
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