On Sun, 28 Mar 2010, kMan wrote:
This was *very* useful for me when I dealt with a 1.5Gb text file
http://www.csc.fi/sivut/atcsc/arkisto/atcsc3_2007/ohjelmistot_html/R_and_la
rge_data/
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.
Ahem.
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
Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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