[R] How to transpose it in a fast way?

[David Winsemius]

On Mar 8, 2013, at 9:31 AM, David Winsemius wrote:

> 
> 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") } )
Read 100000000 items
    user   system  elapsed 
 487.100  912.613 1415.228 

> system.time( {MASS::write.matrix( matrix( scan("example.dat", what=character()), 500,500), file="fil.out") } )
Read 250000 items
   user  system elapsed 
  1.184   2.507   3.834 

And so it seems to scale linearly:

> 3.834 * 100000000/250000
[1] 1533.6

> -- 
> 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
>>> 
>>> ______________________________________________
>>> R-help at 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 at 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 at 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