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 1 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
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