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| === R === | === R === |
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| The "R" statistical/programming package is installed on the clusters. The greatest number of add-on packages are installed in the 4.0.2 version. Also included are slightly newer 4.1.0 and R 4.2.2 and slightly modified Microsoft R Open 4.0.2, plus older saved versions. See [[https://www.analyttica.com/microsoft-r-or-open-source-r-which-suits-you-the-best/|R vs. Microsoft R]]. | The "R" statistical/programming package is installed on the clusters. The greatest number of add-on packages are installed in the 4.0.2 version. Also included are older saved versions, newer 4.1.0 and R 4.2.2 and slightly modified Microsoft R Open 4.0.2 [[https://web.archive.org/web/20200808203121/https://analyttica.com/microsoft-r-or-open-source-r-which-suits-you-the-best/]]. |
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| <code> | <code> |
| module load gcc/9.3.1 mkl/19.0.5 R/4.2.2 | module load gcc/9.3.1 mkl/19.0.5 R/4.2.2 |
| </code> | </code> |
| "gcc" module may be omitted unless you are adding R packages (see below). Substitute R/4.1.0 or MR/4.0.2 as desired for those versions. You can also add rstudio if you want the gui. This is most easily run through a portal. | "gcc" module may be omitted unless you are adding R packages (see below). Substitute R/4.1.0 or MR/4.0.2 as desired for those versions. You can also add rstudio if you want the gui. This is best run through a portal (via interactive-apps Desktop or Rstudio GUI). |
| <code> | <code> |
| module load gcc/9.3.1 mkl/19.0.5 R/4.2.2 rstudio | module load gcc/9.3.1 mkl/19.0.5 R/4.2.2 rstudio |
| If you need a new R package, you can ask hpc-support to add it to the central installation, or you can add your own packages as demonstrated below for the package "broman". R is not as prone to conflicts as Python, so most packages can be added to the central installation without issues. Another way to create a custom installation is using [[python|conda]], which would be better if you see conflicts with standard ''R''. It will, however, install many thousands of files in your home. | If you need a new R package, you can ask hpc-support to add it to the central installation, or you can add your own packages as demonstrated below for the package "broman". R is not as prone to conflicts as Python, so most packages can be added to the central installation without issues. Another way to create a custom installation is using [[python|conda]], which would be better if you see conflicts with standard ''R''. It will, however, install many thousands of files in your home. |
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| For this simple installation, before starting, create files ~/.Rprofile and ~/.Renviron as shown, plus other variables you may want to set. See [[https://rstats.wtf/r-startup.html|rstats.wtf]]. Some redundant text output has been omitted. | For this simple installation, before starting, create files ~/.Rprofile and ~/.Renviron as shown, plus the R library directories (changing full version 4.2.2 to 4.2 in this example) and any of the other R environment variables you may want to set [[https://rstats.wtf/r-startup.html|rstats.wtf]]. |
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| The files ~/.Rprofile and ~/.Renviron should look like this. Also create ``mkdir -p`` the R library directories as shown with the major version of R that you are using (changing 4.2.2 to 4.2 in this case). | |
| <code> | <code> |
| pinnacle-l4:feynman:$ cat .Rprofile | pinnacle-l4:feynman:$ cat .Rprofile |
| </code> | </code> |
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| With those files in place you can install any R package yourself. Your packages will be installed into | With those files in place you can install any R package yourself. Some redundant text output below has been omitted. |
| a directory in your /home directory. In this example '/scrfs/storage/feynman/home/R/x86_64-pc-linux-gnu/4.2': | |
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| <code> | <code> |
