[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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