[R] size limitations in R

[Gabor Grothendieck]

SAS was developed many years ago when computers were far
less powerful so its heritage is that it is very efficient and its unlikely
that R or other modern software will match SAS in that respect.

The development version of the sqldf R package provides an interface
which simplifies the use of the R package RSQLite which in turn is an
interface to the sqlite database.  The development version of
sqldf supports RSQLite's ability to read a file directly to sqlite without
going through R and then reading it from there or reading a subset of it
from there into R.  See example 6 on the sqldf home page:

http://code.google.com/p/sqldf/

On 8/31/07, Fabiano Vergari <fab.vergari at googlemail.com> wrote:
> I am a SAS user currently evaluating R as a possible addition or even
> replacement for SAS. The difficulty I have come across
>
> straight away is R's apparent difficulty in handling relatively large data
> files. Whilst I would not expect it to handle
>
> datasets with millions of records, I still really need to be able to work
> with dataset with 100,000+ records and 100+
>
> variables. Yet, when reading a .csv file with 180,000 records and about 200
> variables, the software virtually ground to a
>
> halt (I stopped it after 1 hour). Are there guidelines or maybe a
> limitations document anywhere that helps me assess the size
>
> of file that R, generally, or specific routines will handle? Also, mindful
> of the fact that I am am an R novice, are there
> guidelines to make efficient use of R in terms of data handling?
>
> Many thanks in advance for your help.
>
> Regards,
> Fabiano Vergari
> fab.vergari at googlemail.com
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at stat.math.ethz.ch 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.
>