Obligatory ad: If you happen to be on Ubuntu, consider #r2u for your #rstats needs.
All the packages from your screenshot (and all their dependencies, for a total of 118 deb packages) install in 25 (!!) seconds.
#rstats #automate_and_accelerate
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Hi @eddelbuettel I hadn't time yet to say how wonderful #r2u is. Now that April arrives, It would be nice to get a frozen version of it for the last days of the R 4.2.x before 4.3.0 (thinking about rocker and reproducible workflows).
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Go for it. Get some storage, get some bandwidth, and experiment with point-in-time overlay snapshots. I think @uri_sohn has some latent interest too (but is busy getting `groundhig` away from MRAN).
I will continue to focus on the apt-based repository.
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Yes, @eddelbuettel I will look at my university if they provide some space and bandwidth to do that.
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My #r2u got very lucky in that someone just plain wonderful from my college helped with a (small, as we do not keep history) VM, and ample bandwith is of course a given for @IllinoisStat so it reflects well on us. Your chance to build on top with a related service!
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@eddelbuettel Could you provide an approximate value for the space required on a server for r2u ? Also, an idea of current bandwidth used (should be much lower for "frozen distros", most probably).
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Disk space for hosting is surprisingly tiny: it is currently only 35gb (as we delete packages that are superceeded by a new version). Bandwidth should be tiny compared to what is offered otherwise, this part of campus also hosts a main CTAN (T, not R) mirror.
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@eddelbuettel OK, thanks. I think I have space on a server for that. Will experiment next week.
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The main "problem" as I see it is that we dispatch to apt, so we have to construct 'dated' index files for it -- instead of for R. Not insurmountable, but AFAIK not done before at least in the R context.
Mar 8, 2023 · 2:38 PM UTC
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