> [*] I recall a student fitting a GLM with about 30 predictors to 1.5m
> records: at the time (ca R 2.14) it did not fit in 4GB but did in 8GB.
You can easily run out of memory when a few of the variables are
factors, each with many levels, and the user looks for interactions
between them. This
On 16/09/2014 13:56, peter dalgaard wrote:
Not sure trolling was intended here.
Anyways:
Yes, there are ways of working with very large datasets in R, using databases
or otherwise. Check the CRAN task views.
SAS will for _some_ purposes be able to avoid overflowing RAM by using
sequential fi
Hundreds of thousands of records usually fit into memory fine.
Hadley
On Tue, Sep 16, 2014 at 12:40 PM, Barry King wrote:
> Is there a way to get around R’s memory-bound limitation by interfacing
> with a Hadoop database or should I look at products like SAS or JMP to work
> with data that has h
Not sure trolling was intended here.
Anyways:
Yes, there are ways of working with very large datasets in R, using databases
or otherwise. Check the CRAN task views.
SAS will for _some_ purposes be able to avoid overflowing RAM by using
sequential file access. The biglm package is an example o
If you need to start your question with a false dichotomy, by all means choose
the option you seem to have already chosen and stop trolling us.
If you actually want an answer here, try Googling on the topic first (is "R
hadoop" so un-obvious?) and then phrase a specific question so someone has a
On Tue, Sep 16, 2014 at 6:40 AM, Barry King wrote:
> Is there a way to get around R’s memory-bound limitation by interfacing
> with a Hadoop database or should I look at products like SAS or JMP to work
> with data that has hundreds of thousands of records? Any help is
> appreciated.
> __
Is there a way to get around R’s memory-bound limitation by interfacing
with a Hadoop database or should I look at products like SAS or JMP to work
with data that has hundreds of thousands of records? Any help is
appreciated.
--
__
*Barry E. King, Ph.D.*
Analytics Modeler
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