[Rd] [BioC] enabling reproducible research & R package management & install.package.version & BiocLite
MEC at stowers.org
Tue Mar 5 15:36:12 CET 2013
.>>> * where do the dragons lurk
.>> webs of interconnected dynamically loaded libraries, identical versions of
.>> R compiled with different BLAS/LAPACK options, etc. Go with the VM if you
.>> really, truly, want this level of exact reproducibility.
.> Sounds like the best bet -- maybe tools like vagrant might be useful here:
.> ... or maybe they're overkill?
.> Haven't really checked it out myself too much, my impression is that
.> these tools (vagrant, chef, puppet) are built to handle such cases.
.> I'd imagine you'd probably need a location where you can grab the
.> precise (versioned) packages for the things you are specifying, but
.Right...and this is a bit tricky, because we don't keep old versions
.around in our BioC software repositories. They are available through
.Subversion but with the sometimes additional overhead of setting up
So, even if I wanted to go where dragons lurked, it would not be possible to cobble a version of biocLite that installed specific versions of software.
Thus, I might rather consider an approach that at 'publish' time tarzips up a copy of the R package dependencies based on a config file defined from sessionInfo and caches it in the project directory.
Then when/if the project is revisited (and found to produce differnt results under current R enviRonment), I can "simply" install an old R (oops, I guess I'd have to build it), and then un-tarzip the dependencies into the projects own R/Library which I would put on .libpaths.
(My virtual machine advocating colleagues are snickering now, I am sure......)
Thanks for all your thoughts and advices....
.> Steve Lianoglou
.> Graduate Student: Computational Systems Biology
.> | Memorial Sloan-Kettering Cancer Center
.> | Weill Medical College of Cornell University
.> Contact Info: http://cbio.mskcc.org/~lianos/contact
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