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R is a "free software environment for statistical computing and graphics."[1]


Install the r package. The installation of external packages within the R environment may require gcc-fortran.


To start a R session, open your terminal and type this command:

$ R
  • Make sure to use a capital R for the command. Note that some shells use the lowercase r command to repeat the last entered command. Once in your R session, the prompt will change to >
  • site refers to system-wide in R Documentation

Run ?Startup to read the documentation about system file configuration, help() for the on-line help,help.start() for the HTML browser interface to help, demo() for some demos and q() to close the session and quit.

When closing the session, you will be prompted : Save workspace Image ?[y/n/c]. The workspace is your current working environment and include any user-defined objects, functions. The saved image is stored in .RData format and will be automatically reloaded the next time R is started. You can manually save the workspace at any time in the session with the save.image(image.RData) command, save as many images as you want (eg : image1.RData, image2.RData). You can load image with the load.image(image.RData) command at any time of your session.

  • Tired of R's verbose startup message ? Then start R with the --quiet command-line option ($ R --quiet). You can add alias R="R --quiet" in one of your Startup files.
  • Running R from the command line will set R's working directory to the current directory. Opening the R GUI will set R's working directory to $HOME, unless explicitly defined in your configuration files (.Renviron or .Rprofile).


Whenever R starts, its configuration is controlled by several files. Please refer to Initialization at Start of an R Session to get a detailed understanding of startup process.


R first loads site and user environment variable files. The name of the site file is controlled by the Environment variables R_ENVIRON if it exists, and defaults to /etc/R/Renviron. The name of the user file is specified by R_ENVIRON_USER. If that is unset, it defaults to .Renviron in the curent working directory or if it exists, and ~/.Renviron otherwise.

Most important variables can be found on Environment Variables R Documentation.

You may disable loading environment files with --no-environ

Lines in Renviron file should be either comment lines starting with # or lines of the form name=value. Here is a very basic .Renviron:

R_HOME_USER = /path/to/your/r/directory
R_PROFILE_USER = ${HOME}/.config/r/.Rprofile
R_LIBS_USER = /path/to/your/r/library
R_HISTFILE = /path/to/your/filename.Rhistory                                             # Do not forget to append the .Rhistory
MYSQL_HOME = /var/lib/mysql


R then loads an Rprofile, which contains R code that is executed. These files are read in the following order of preference (only one file is loaded):

1. A file specified by the environment variable R_PROFILE_USER.

2. .Rprofile in the current working directory.

3. $HOME/.Rprofile.

An .Rprofile can contain arbitrary R code, though best practice suggests that one should not load packages at startup, as this hinders package upgrades and reproducibility.

# The .First function is called after everything else in .Rprofile is executed
.First <- function() {
  # Print a welcome message
  message("Welcome back ", Sys.getenv("USER"),"!\n","working directory is:", getwd())

options(digits = 12)                                          # number of digits to print. Default is 7, max is 15
options(stringsAsFactors = FALSE)                             # Disable default conversion of character strings to factors
options(show.signif.stars = FALSE)                            # Don't show stars indicating statistical significance in model outputs
error <- quote(dump.frames("${R_HOME_USER}/testdump", TRUE))  # post-mortem debugging facilities

You can add more global options to customize your R environment. See this post for more examples of user configurations.


Aspects of the Locale are accessed by the functions Sys.getlocale and Sys.localeconv within the R session. Locales will be the one defined in your system.

Managing R packages

There are many add-on R packages, which can be browsed on The R Website..

Note: Some R packages link to files provided by system packages. These packages will need to be reinstalled when these files are updated.

With pacman

There are some packages available on the AUR with the prefix r-. You can mix and match installing R packages with pacman and through R (below), but if you do so you should let pacman manage system packages (those that reside at /usr/lib/R/library, and let R manage user-installed packages elsewhere (e.g. ~/R/library).

With R

Packages can be installed from within R using the install.packages(c("pkgname")) command. You should use a local library and let pacman manage files that reside under /usr/lib/R/library.

  • install.packages() requires tk to be installed for selecting mirrors. Try installing this package if you see:

Error: .onLoad failed in loadNamespace() for 'tcltk', details (...)

  • Alternatively, you can disable graphical pop-ups like this by running [2]:

> options(menu.graphics=FALSE)

to make this change more permanent add the above line to your Rprofile.

Within your R session, run this command to check that your user library exists and is set correctly:

> Sys.getenv("R_LIBS_USER")
[1] "/path/to/directory/R/packages"

Alternatively, you may install from the command line like so:

$ R CMD INSTALL -l $R_LIBS_USER pkg1 pkg2 ...

Upgrading R packages

Within a R session
> update.packages(ask=FALSE)

Or when you also need to rebuild packages which were built for an older version:

> update.packages(ask=FALSE,checkBuilt=TRUE)

Or when you also need to select a specific mirror (https://cran.r-project.org/mirrors.html) to download the packages from (changing the url as need):

> update.packages(ask=FALSE,checkBuilt=TRUE,repos="https://cran.cnr.berkeley.edu/")
Tip: upgrading packages from your R session can quickly be a pain if you have too many loaded packages at start up. For packages to be upgraded, they cannot be loaded, so do not load packages from your Rprofile.
Within a shell

You can use Rscript, which comes with r to update packages from a shell:

$ Rscript -e "update.packages()"


The Makevars file can be used to set the default make options when installing packages. An example optimized Makevars file is as follow:

CFLAGS=-O3 -Wall -pedantic -march=native -mtune=native -pipe
CXXFLAGS=-O3 -Wall -pedantic -march=native -mtune=native -pipe

Adding a graphical frontend to R

R does not include a point-and-click graphical user interface for statistics or data manipulation. However, third-party user interfaces for R are available, such as R commander and RKWard.

R Commander frontend

R Commander is a popular user interface to R. There is no Arch linux package available to install R commander, but it is an R package so it can be installed easily from within R. R Commander requires tk to be installed.

To install R Commander, run 'R' from the command line. Then type:

> install.packages("Rcmdr", dependencies=TRUE)

This can take some time.

You can then start R Commander from within R using the library command:

> library("Rcmdr")

RKWard frontend

RKWard is an open-source frontend which allows for data import and browsing as well as running common statistical tests and plots. You can install rkwardAUR from AUR.

Editors IDEs and notebooks with R support

Rstudio IDE

RStudio an open-source R IDE. It includes many modern conveniences such as parentheses matching, tab-completion, tool-tip help popups, and a spreadsheet-like data viewer.

Install rstudio-desktop-binAUR (binary version from the Rstudio project website) or rstudio-desktop-gitAUR (development version) from AUR.

The R library path is often configured with the R_LIBS environment variable. RStudio ignores this, so the user must set R_LIBS_USER in ~/.Renviron, as documented above.

Rstudio server

RStudio Server enables you to provide a browser based interface to a version of R running on a remote Linux server.

Install rstudio-server-gitAUR. The two main configuration files are /etc/rstudio/rserver.conf and /etc/rstudio/rsession.conf. They are not created during the install, so you will need to create and edit them. For information about configure options, please refer to rstudio getting started documentation.

To start the server, please enable and start the rstudio-server.service unit file provided with the package.

Emacs Speaks Statistics

Emacs users can interact with R via the emacs-essAUR package.


The nvim-rAUR package allows vim and neovim users to code in R, including editing and rendering of R markdown (Rmd) files, execution of R code in a separate pane, inspection of variables, and integrated help panes.


cantor is a notebook application developed by KDE that includes support for R.

Jupyter notebook

jupyter-notebook is a browser based notebook with support for many programming languages. R support can be added by installing the IRkernel.

Tips and tricks

Optimized packages

The numerical libraries that comes with the R (generic blas, LAPACK) do not have multithreading capabilities. Replacing the reference blas package with an optimized BLAS can produce dramatic speed increases for many common computations in R. See these threads for an overview of the potential speed increases:


openblas can replace the reference blas. If you are using the regular r package from [extra] no further configuration is needed; R is configured to use the system BLAS and will use OpenBLAS once it is installed.

Intel MKL

If your processors are Intel, you can use the Intel math Kernel Library. The MKL, beyond the capabilities of multithreading, also has specific optimizations for Intel processors. Keep in mind that they can potentially interfere with the standard R functionality for parallel processing.

Please first Install the intel-mklAUR package available from AUR, then the r-mklAUR package.

  • if you install the r-mklAUR with R already installed, you will be prompted to remove R. Once r-mkl is installed, please run on R console the following command :

> update.packages(checkBuilt=TRUE)

  • here are elapsed time in sec from computing 15 tests with default GCC build and icc/MKL build: 274.93 sec for GCC build, 21.01 sec for icc/MKL build. See this post for more information.


intel-advisor delivers top application performance with C, C++ and Fortran compilers, libraries and analysis tools.

Install the intel-advisor-xeAUR[broken link: package not found] package.

Set CRAN mirror across R sessions

Instead of having R ask which CRAN mirror to use every time you install or update a package, you can set the mirror in the Rprofile file. https://cloud.r-project.org/ should be a good default for everywhere:

## Set CRAN mirror:
  r <- getOption("repos")
  r["CRAN"] <- "https://cloud.r-project.org/"
  options(repos = r)

See also

  • Official website
  • RSeek A Google Custom Search Engine for R related material.
  • R for Data Science Online version of a CCA licensed book written by Garrett Grolemund and Hadley Wickham from RStudio, 2017.
  • R-bloggers Aggregation site for (English) blogs related to R.
  • /r/Rlanguage on Reddit There are several R related Subreddits, each one provides links to the others.