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.
--
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 <pda...@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....@cbs.dk Priv: pda...@gmail.com
>>
>> ______________________________________________
>> 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.
David Winsemius
Alameda, CA, USA
______________________________________________
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.
