# R

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- Intel C++

R is a "free software environment for statistical computing and graphics (http://www.r-project.org/)."

## Installation

### Basic package

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

### Initial configuration

Please refer to Initialization at Start of an R Session to get a detailed understanding of startup process. The home directory of the `R` installation is `/usr/lib/R`. Base packages are found in `/usr/lib/R/library/base` and site configuration files in `/etc/R/`. 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.

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

    $ R

Note:

- Use `Shift+u` for the command (some shells use the `r` letter 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.

Tip:

- Tired of R's verbose startup message ? Then start `R` with the `--quiet` command-line option.

`$ R --quiet` You can `alias R ="R --quiet"` in one of your Startup files

- Unless explicitly defined somewhere in your configuration files, `R` will start in your $HOME directory. If you want to start in a specific directory. first time you create the directory do this:

    $ R
    > setwd("path/to/your/directory")
    > q()
    Save workspace image? [y/n/c]: y

`R` will create a .RData image file of your current environment. Then, when double-clicking this file, `R` will automatically change its working directory to the file's directory.

- colorize console output:

    > download.file("http://www.lepem.ufc.br/jaa/vimr/colorout_1.1-1.tar.gz", destfile = "colorout_1.1-1.tar.gz")
    > install.packages("colorout_1.1-1.tar.gz", type = "source", repos = NULL)

### Variables

`R` can be confusing when it comes to environment variables, as they are large and duplicated following the site or user sides. There are two sorts of files used in startup: environment files, defined by `$R_ENVIRON` and profile files, defined by `$R_PROFILE`.

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

#### R_ENVIRON

At startup, `R` search at early stage for site and user `.Renviron` files to process for setting environment variables. The site file is located in `/etc/R`, and generated by configure.

The name of the user file can be specified by the `R_ENVIRON_USER` environment variable. If you do not specify any file, `R` will automatically read `.Renviron` in your home directory if there is one. In case you want to use another emplacement for this file, append this line `export R_ENVIRON_USER ="path/to/.Renviron"` in one of your startup files. This is the place to set all kind of environment variables using the R syntax.

#### R_PROFILE

Then `R` searches for the site-wilde `Rprofile.site` defined by the `R_PROFILE` environment variable. This file does not exist after a fresh installation. Finally, `R` seraches for user `R_PROFILE_USER`. if unset, a file called `.Rprofile` is searched for in the current directory, returned by the `R` command `> getwd()` or in the user's home directory. This is the place to put all your custom `R` code.

## Installing R packages

There are many add-on `R` packages, which can be browsed on The R Website.. They can be installed from within `R` using the `install.packages(c("pkgname"))` command. `R` can install its packages locally as per user local settings or system wide.

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"

Installation within your `R` session is the safest way and will not conflict with the pacman package management, but there is another method to install packages. Run the following command in your terminal:

    $ 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)

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 need to be not loaded. Best practice is to start a new clean R session this way: `R --vanilla` then run the upgrade.

#### Within a shell

First, Install littlerAUR from the Arch User Repository. This package allows direct execution of R commands and can be seen as a scripting front-end. Then, visit dirk.eddelbuettel webiste about littler. Below is the script to run packages updates:

    #!/usr/bin/env r
    #
    # a simple example to update packages in /usr/local/lib/R/site-library
    # parameters are easily adjustable

    repos <- "http://cran.rstudio.com"

    lib.loc <- Sys.getenv("R_LIBS_USER")

    update.packages(repos=repos, ask=FALSE, lib.loc=lib.loc)

Put this script in your favorite place and make it executable. Of course, feel free to modify the repo URL and the library location.

Warning: when using zsh, r is a builtin command. You will have to either use an explicit path `/usr/bin/r` or create an alias under a different name.

#### Automatically after R upgrades

R packages may not be compatible between R versions. You may want to automatically upgrade them and rebuild them if necessary when R upgrades. This can be accomplished with a pacman hook:

    /etc/pacman.d/hooks/r-upgrade.hook

    [Trigger]
    Operation = Upgrade
    Type = Package
    Target = r

    [Action]
    Description = Updates R packages and rebuilds them if necessary after R upgrade
    # Depends is optional if this should depend on another package
    Depends =
    When = PostTransaction
    Exec = Rscript -e 'update.packages(checkBuilt = TRUE, ask = FALSE)'

## Configuration files

The two user configuration files you want in your home folder are `.Renviron` and `Rprofile.r`. If you want to keep your `$HOME` directory as clean as possible, a good practice will be to make the `~/.config/r` directory, put the `Rprofile.r` file at the root of the directory and append all your `R` code in this file.

### .Renviron

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

    .Renviron

    R_HOME_USER = /path/to/your/r/directory
    R_PROFILE_USER = ${HOME}/.config/r/Rprofile.r
    R_LIBS_USER = /path/to/your/r/library
    R_HISTFILE = /path/to/your/filename.Rhistory                                             # Do not forget to append the .Rhistory
    R_DEFAULT_PACKAGES = 'datasets,utils,grDevices,graphics,stats,methods,rJava,colorout'    # load some default packages at start up
    MYSQL_HOME = /var/lib/mysql

### Rprofile.r

For convenient reasons, you can put a specific `Rprofile.r` in each of your usual working directories. One facility would be to dedicate one directory per project, with its specific profile. When `R` will change to the working directory, it will then read the `Rprofile.r` file inside it.

Here is a very short list of useful options and code:

    Rprofile.r

    setwd("path/to/startup/directory")                                                                   # define a start up working directory
    .First <- function(){
    #welcome message
    message("Welcome back ", Sys.getenv("USER"),"!\n","working directory is:", getwd())
    }
    options(prompt = paste(paste (Sys.info () [c ("user", "nodename")], collapse = "@"),"[R] "))         # customize your R prompt with username and hostname in this format: user@hostname [R]
    options(digits = 12)                                                                                 # number of digits to print. Default is 7, max is 15
    options(stringsAsFactors = FALSE)
    options(show.signif.stars = FALSE)
    error = quote(dump.frames("${R_HOME_USER}/testdump", TRUE))                                          # post-mortem debugiging facilities

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

## 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.

### Nvim-R

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

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.

## 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. However the available optimized BLAS packages all have drawbacks such as requiring a commercial license or potentially interfering with the standard R functionality for parallel processing. If you really need faster linear algebra in R you may consider the following options.

### OpenBLAS

openblasAUR can be installed from the AUR, replacing the reference blas from extra. 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, it is strongly advised to use the Intel math Kernel Library. The MKL, beyond the capabilities of multithreading, also has specific optimizations for Intel processors, with performance far superior to traditional libraries.

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

Note:

- 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-xe

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.

## See also

- RSeek A search engine for R related material.

Categories:

- Mathematics and science
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