[R] large dataset
tlumley at u.washington.edu
Mon Mar 29 22:12:26 CEST 2010
On Sun, 28 Mar 2010, kMan wrote:
>> This was *very* useful for me when I dealt with a 1.5Gb text file
> Two hours is a *very* long time to transfer a csv file to a db. The author
> of the linked article has not documented how to use scan() arguments
> appropriately for the task. I take particular issue with the authors
> statement that "R is said to be slow, memory hungry and only capable of
> handling small datasets," indicating he/she has crummy informants and not
> challenged the notion him/herself.
I believe that *I* am the author of the particular statement you take issue with (although not the of the rest of the page).
However, when I wrote it, it continued:
"R (and S) are accused of being slow, memory-hungry, and able to handle only small data sets.
This is completely true.
Fortunately, computers are fast and have lots of memory. Data sets with a few tens of thousands of observations can be handled in 256Mb of memory, and quite large data sets with 1Gb of memory. Workstations with 32Gb or more to handle millions of observations are still expensive (but in a few years Moore's Law should catch up).
Tools for interfacing R with databases allow very large data sets, but this isn't transparent to the user."
I think this is a perfectly reasonable summary and has been (with appropriate changes to the memory numbers) for the nearly ten years I've been saying it.
Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle
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