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How to skip re-installing CRAN packages when updating R?
2 messages · Sean Zhang, Brian Ripley
Please do study the posting guide: you have not told us your platform and it does matter. For Windows and Mac OS X see the appropriate FAQ.
On Wed, 24 Dec 2008, Sean Zhang wrote:
Dear R-helpers: I am new to R and would like to seek your expert opinion on installation tip. Many thanks in advance. I want to update my R to the newest version and wonder the following two questions: Question 1: How can I install R and its contributed packages in a way so when updating R in the future, I do NOT need to re-install contributed packages used by R of last version.
Use a separate library directory. You will need to re-install for updates to dot-0 releases (e.g. 2.9.0) but update.packages(checkBuilt=TRUE) does that for you.
Question 2: Is it an ok-practice to just install all the CRAN packages (i.e., install.packages(available.packages()[,1]) ). Does someone do so?
Not a good idea as you already have some, the recommended packages. install.packages(new.packages()) would be better. And yes, quite a few sites do things like that, but do bear in mind the costs on the CRAN servers (we keep a local mirror and install from that).
The reason I ask the second question is that if installing all available packages does Not consume too much time (say less than 2 hours), too much computer resource (I have big harddrive, so harddrive is probably not a concern. I guess computing speed will not be affected but not sure...) then, I do not need to bother Question 1 and will just install all available packages when updating R.
It will take more than 2 hours for a source install except for a parallel install on a very fast machine. For example, the Windows build takes 8 CPU hours (http://cran.r-project.org/bin/windows/contrib/checkSummaryWin.html) and I think my x86_64 Linux server takes about 3 elapsed hours and 3GB for the installed packages. (There is no built-in support for parallel installs, although some of us have written private versions.)
Many Thanks in advance. Merry Christmas! -Sean [[alternative HTML version deleted]]
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Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595