[R] Managing Data [Summary]
Magill, Brett
MagillB at usa.redcross.org
Mon Feb 25 15:53:03 CET 2002
A while ago I asked about data management approaches in R. I was asked by
several people to post a summary of the responses and alas I am just getting
around to doing it. I received several comments, both on and off list.
Below is a compilation of these responses. Hope this is as helpful to
others as it was to me.
Best,
Brett
1. Frank Harrell recommended a section, "Operating in S", from his text
which can be found at:
http://hesweb1.med.virginia.edu/biostat/s/doc/splus.pdf
2. Jon Baron says that he does not use the files R stores at all, instead
following the procedures outlined in the beginning of his text:
http://www.psych.upenn.edu/~baron/rpsych.htm
This approach involves "...involves keeping raw data in separate files, each
with a name (supposedly a useful mnemonic), such as cba1.data, and scripts
in other separate files, such as cba1.R." A script is written to read,
transform, manipulate, and analyze data.
Jim Lemon uses a similar approach but adds "Just to forestall any confusion,
I noticed that [Jon Baron] didn't mention using read.table() or scan() in
the *.R file of commands to read in the data each time the script is run.
That way you never trash your data, and any data edits you do outside of R
are reflected in the next run of the script."
3. David Brahm offers another approach, a package he developed called g.data
available at:
http://cran.us.r-project.org/src/contrib/PACKAGES.html#g.data
David says that this approach will be most intuitive to S-plus users. The
package "...manages data in R in a manner very similar to the way S-Plus
does. You assign 'permanent' objects to a particular position in your
search path (usually pos=2), then save all objects in that position with a
call to g.data.save()."
Brett A. Magill, Evaluation Associate
Research and Product Development
Health, Safety and Community Services
American Red Cross
magillb at usa.redcross.org
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