R is an open-source programming language that specializes in statistical computing and graphics. Supported by the R Foundation for Statistical Computing, it is widely used for developing statistical software and performing data analysis. An increasingly popular and extensible language with an active community, R offers many user-generated packages for specific areas of study, which makes it applicable to many fields.
In this tutorial, we will install R and show how to add packages from the official Comprehensive R Archive Network (CRAN).
To follow along with this tutorial, you will need a Debian 9 server with:
- at least 1GB of RAM
- a non-root user with
To learn how to achieve this setup, follow our Debian 9 initial server setup guide.
Once these prerequisites are in place, you’re ready to begin.
Step 1 — Installing Dependencies
Because R is a fast-moving project, the latest stable version isn’t always available from Debian’s repositories, so we’ll need to add the external repository maintained by CRAN. In order to do this, we’ll need to install some dependencies for the Debian 9 cloud image.
To perform network operations that manage and download certificates, we need to install
dirmngr so that we can add the external repository.
- sudo apt install dirmngr –install-recommends
To add a PPA reference to Debian, we’ll need to use the
add-apt-repository command. For installations where this command may not available, you can add this utility to your system by installing
- sudo apt install software-properties-common
Finally, to ensure that we have HTTPS support for secure protocols, we’ll install the following tool:
- sudo apt install apt-transport-https
With these dependencies in place, we’re ready to install R.
Step 2 — Installing R
For the most recent version of R, we’ll be installing from the CRAN repositories.
Note: CRAN maintains the repositories within their network, but not all external repositories are reliable. Be sure to install only from trusted sources.
Let’s first add the relevant GPG key.
- sudo apt-key adv –keyserver keys.gnupg.net –recv-key ‘E19F5F87128899B192B1A2C2AD5F960A256A04AF’
When we run the command, we’ll receive the following output:
Executing: /tmp/apt-key-gpghome.k3UoM7WQGq/gpg.1.sh --keyserver keys.gnupg.net --recv-key E19F5F87128899B192B1A2C2AD5F960A256A04AF gpg: key AD5F960A256A04AF: public key "Johannes Ranke (Wissenschaftlicher Berater) <email@example.com>" imported gpg: Total number processed: 1 gpg: imported: 1
Once we have the trusted key, we can add the repository. Note that if you’re not using Debian 9 (Stretch), you can look at the supported R Project Debian branches, named for each release.
- sudo add-apt-repository ‘deb https://cloud.r-project.org/bin/linux/debian stretch-cran35/’
Now, we’ll need to run
update after this in order to include package manifests from the new repository.
- sudo apt update
Among the output should be a line similar to the following:
Among the output that displays, you should identify lines similar to the following:
... Get:6 https://cloud.r-project.org/bin/linux/debian stretch-cran35/ InRelease [4,371 B] Get:7 https://cloud.r-project.org/bin/linux/debian stretch-cran35/ Packages [50.1 kB] ...
If the lines above appear in the output from the
update command, we’ve successfully added the repository. We can be sure we won’t accidentally install an older version.
At this point, we’re ready to install R with the following command.
- sudo apt install r-base
If prompted to confirm installation, press
y to continue.
As of the time of writing, the latest stable version of R from CRAN is 3.5.1, which is displayed when you start R.
Since we’re planning to install an example package for every user on the system, we’ll start R as root so that the libraries will be available to all users automatically. Alternatively, if you run the
R command without
sudo, a personal library can be set up for your user.
- sudo -i R
R version 3.5.1 (2018-07-02) -- "Feather Spray" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) ... Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. >
This confirms that we’ve successfully installed R and entered its interactive shell.
Step 3 — Installing R Packages from CRAN
Part of R’s strength is its available abundance of add-on packages. For demonstration purposes, we’ll install
txtplot, a library that outputs ASCII graphs that include scatterplot, line plot, density plot, acf and bar charts:
Note: The following output shows where the package will be installed.
... Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) . . .
This site-wide path is available because we ran R as root. This is the correct location to make the package available to all users.
When the installation is complete, we can load
If there are no error messages, the library has successfully loaded. Let’s put it in action now with an example which demonstrates a basic plotting function with axis labels. The example data, supplied by R’s
datasets package, contains the speed of cars and the distance required to stop based on data from the 1920s:
- txtplot(cars[,1], cars[,2], xlab = ‘speed’, ylab = ‘distance’)
+----+-----------+------------+-----------+-----------+--+ 120 + * + | | d 100 + * + i | * * | s 80 + * * + t | * * * * | a 60 + * * * * * + n | * * * * * | c 40 + * * * * * * * + e | * * * * * * * | 20 + * * * * * + | * * * | 0 +----+-----------+------------+-----------+-----------+--+ 5 10 15 20 25 speed
If you are interested to learn more about
help(txtplot) from within the R interpreter.
Any precompiled package can be installed from CRAN with
install.packages(). To learn more about what’s available, you can find a listing of official packages organized by name via the Available CRAN Packages By Name list.
To exit R, you can type
q(). Unless you want to save the workspace image, you can press
With R successfully installed on your server, you may be interested in this guide on installing the RStudio Server to bring an IDE to the server-based deployment you just completed. You can also learn how to set up a Shiny server to convert your R code into interactive web pages.
For more information on how to install R packages by leveraging different tools, you can read about how to install directly from GitHub, BitBucket or other locations. This will allow you to take advantage of the very latest work from the active community.