Data Science. TileDB. Open Source. Quant Research. R. C++. Debian. Linux. Adjunct Clinical Professor, University of Illinois. Lots of coffee. And some running.
It's a bit of a 'deja vu' -- while I don't have VMs that small (I just use Docker, being on Linux) I answered that same question a few times at StackOverflow etc.
That said, do look into r2u. It's honestly rather good (and I switched all my CI etc to it).
a) That is an out-of-memory error because your VM is too small for C++ compilation
b) solution one: do not compile: sudo apt install r-cran-lme4
c) solution two, much better: look into r2u:
eddelbuettel.github.io/r2u/#rstats
Thrilled to be speaking about #r2u to the #YEGRUG. This should also leave time for demos of the (simple!) setup and its use. I will present remotely (as I have attended remotely) so feel free to join the group. #rstats
More about #r2u as always at eddelbuettel.github.io/r2u/
Yes, look into 'Repository Secrets' in the GitHub Actions documentation. It's pretty straightforward -- for example I use one here and pass it to an environment variable.
github.com/eddelbuettel/rpus…
You do not need a loop: Many operations -- including regular expression or time management (calling `file.info()` first) are vectorized. I have some (only half-)joking slides on 'R for system administration' for that very reason. #Rstats is good at this too!
Most sincere gratulations -- this was well earned as it is a genuinely excellent and brilliant piece of work you (and the rest of the team) did there. Thanks again as well for stepping by and telling my STAT 447 students about this and other work: youtube.com/watch?v=1TgEl5OZ…
Another daily #r2u update post -- many new #Rstats packages mid-week because CRAN doesn't sleep. And #r2u brings this ready to use, with full dependecies, as pre-made binaries for focal and jammy via `apt` or `install.packages()`. And now via a well-connected Internet2 mirror!
Big #r2u news: a mirror! Announced during the @ChicagoRusers talk I just gave.
Use r2u.stat.illinois.edu/ubuntu… and take advantage of Intenet2 speeds, and campus reliability to bring your CRAN Ubuntu binaries to #RStats for @Ubuntu. #r2u documentation will be updated tomorrow.
Yes but worth stressing that #r2u works equallly well beyond @Docker:
- your server or desktop or laptop
- your cloud instance at AWS, GCS, Azure
- your WSL2 instance on Windows (!!)
"Runs everywhere @Ubuntu (on x86_64) runs"
#rstats
duckdb bends the truth a little by claiming zero dependencies. They are vendored in -- so no *external* dependencies is a little "truthier".
#Rstats installation can be easy, fast, reliable, complete: #r2u demo below given tomorrow's talk. 5 seconds on Ubuntu 22.04.
An _abstraction_ that unfies access to file, or network, system command, or ... You think about _having a connection_, then read data (txt, csv, ...) from it. How to use a particular connection varies.
> con <- pipe("ls -1 /etc/R")
> etcfile <- readLines(con)
> close(con)
The `r2u` talk is happening in three days in Chicago at the R User Group meeting. Updated #r2u download chart below as a tease. Not yet 'billions and billions served' but hey 400k aint too shabby for four months. Details at eddelbuettel.github.io/r2u/
Delighted to talk about `r2u` next Thursday at the Chicago R Users Group. As a teaser, the cumulative downloads of Ubuntu focal or jammy binaries. `r2u` covers all of CRAN, with full dependencies.
See eddelbuettel.github.io/r2u/ for more on this new #Rstats tool.
It's not simple as managing toolchains, libraries, versions, ... consistently is difficult.
My suggestion: remove that layer, offer fully integrated binaries where we can -- see eddelbuettel.github.io/r2u/ and its demo and try it (via the gitpod link or docker) #rstats#r2u