The solution is to read the 'R Data Import/Export Manual' and make use
of
connections or databases.
What you want to do is very easy in RODBC, for example, but can be done
with scan() easily provided you keep a connection open.
On Fri, 6 Apr 2007, Yuchen Luo wrote:
> Hi, my friends.
>
> When a data file is large, loading the whole file into the memory all
> together is not feasible. A feasible way is to read one row, process it,
> store the result, and read the next row.
It makes a lot more sense to process say 1000 rows at a time.
> In Fortran, by default, the 'read' command reads one line of a
file, which
> is convenient, and when the same 'read' command is executed the
next time,
> the next row of the same file will be read.
>
> I tried to replicate such row-by-row reading in R.I use scan( ) to do so
> with the "skip= xxx " option. It takes only seconds when the
number of the
> rows is within 1000. However, it takes hours to read 10000 rows. I think it
> is because every time R reads, it needs to start from the first row of the
> file and count xxx rows to find the row it needs to read. Therefore, it
> takes more time for R to locate the row it needs to read.
Yes, R does tend to do what you tell it to ....
> Is there a solution to this problem?
>
> Your help will be highly appreciated!
> Best Wishes
> Yuchen Luo
>
> [[alternative HTML version deleted]]
>
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--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595