How to transpose it in a fast way?

On Mar 8, 2013, at 6:01 AM, Jan van der Laan wrote:

You could use the fact that scan reads the data rowwise, and the fact that arrays are stored columnwise:

# generate a small example dataset
exampl <- array(letters[1:25], dim=c(5,5))
write.table(exampl, file="example.dat", row.names=FALSE. col.names=FALSE,
  sep="\t", quote=FALSE)

This might avoid creation of some of the intermediate copies:

MASS::write.matrix( matrix( scan("example.dat", what=character()), 5,5), file="fil.out")

I tested it up to a 5000 x 5000 file:

exampl <- array(letters[1:25], dim=c(5000,5000))
MASS::write.matrix( matrix( scan("example.dat", what=character()), 5000,5000), file="fil.out")

Read 25000000 items

Not sure of the exact timing. Probably 5-10 minutes. The exampl-object takes 200,001,400 bytes. and did not noticeably stress my machine. Most of my RAM remains untouched. I'm going out on errands and will run timing on a 10K x 10K test case within a system.time() enclosure. Scan did report successfully reading 100000000 items fairly promptly.

system.time( {MASS::write.matrix( matrix( scan("example.dat", what=character()), 10000,10000), file="fil.out") } )

system.time( {MASS::write.matrix( matrix( scan("example.dat", what=character()), 500,500), file="fil.out") } )

3.834 * 100000000/250000

-- 
David.

# and read...
d <- scan("example.dat", what=character())
d <- array(d, dim=c(5,5))

t(exampl) == d


Although this is probably faster, it doesn't help with the large size. You could used the n option of scan to read chunks/blocks and feed those to, for example, an ff array (which you ideally have preallocated).

HTH,

Jan




peter dalgaard <pdalgd at gmail.com> schreef:

On Mar 7, 2013, at 01:18 , Yao He wrote:

Dear all:

I have a big data file of 60000 columns and 60000 rows like that:

AA AC AA AA .......AT
CC CC CT CT.......TC
..........................
.........................

I want to transpose it and the output is a new like that
AA CC ............
AC CC............
AA CT.............
AA CT.........
....................
....................
AT TC.............

The keypoint is  I can't read it into R by read.table() because the
data is too large,so I try that:
c<-file("silygenotype.txt","r")
geno_t<-list()
repeat{
line<-readLines(c,n=1)
if (length(line)==0)break  #end of file
line<-unlist(strsplit(line,"\t"))
geno_t<-cbind(geno_t,line)
}
write.table(geno_t,"xxx.txt")

It works but it is too slow ,how to optimize it???

As others have pointed out, that's a lot of data!

You seem to have the right idea: If you read the columns line by line there is nothing to transpose. A couple of points, though:

- The cbind() is a potential performance hit since it copies the list every time around. geno_t <- vector("list", 60000) and then
geno_t[[i]] <- <etc>

- You might use scan() instead of readLines, strsplit

- Perhaps consider the data type as you seem to be reading strings with 16 possible values (I suspect that R already optimizes string storage to make this point moot, though.)

--
Peter Dalgaard, Professor
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com

David Winsemius
Alameda, CA, USA