Is there a way to efficiently read large datasets directly into a matrix
byrow? I know data.frame, but for large datasets it doesn't efficiently
work, also if I increase the cons memory.
R --nsize 1000k --vsize 90M
...> x<-read.table("pendler.luft.txt")
Error: cons memory (1000000 cells) exhausted
See "help(Memory)" on how to increase the number of cons
cells.>
Also the following is problematic:
R --nsize 1000k --vsize 90M
...> x<-scan("pendler.luft.txt",skip=1)
Read 3164832 items> x<-matrix(x,nrow=3164832/6,ncol=6,byrow=T)
Error: heap memory (92160 Kb) exhausted [needed 24725 Kb more]
See "help(Memory)" on how to increase the heap
size.>
The following works but is not very elegant I think
> x<-matrix(NA,nrow=6,ncol=3164832/6)
> x[,]<-scan("pendler.luft.txt",skip=1)
Read 3164832 items> x<-t(x)
> x[1,]
[1] 10101 10405 10349 3945 89 0>
Is there a better way to do that? How can I avoid copying of such large
objects? E.g., does x<-t(x) copy x or not?
Thanks,
Adrian
--
Adrian Trapletti, Vienna University of Economics and Business
Administration, Augasse 2-6, A-1090 Vienna, Austria
Phone: ++43 1 31336 4561, Fax: ++43 1 31336 708,
Email: adrian.trapletti at wu-wien.ac.at
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