Education Committee


Introduction to R

Part 0: Installing R and RStudio

By Arun Ramamurthy


Introduction

In this mini-tutorial, you will learn how to install R (a statistical programming language) and RStudio (an integrated development environment (IDE) for R programming). This tutorial is the prerequisite for r1, SAAS’s crash course on R for data science and statistics.


What is R?

Before we begin installing R, what is R anyway?

The first thing to notice about R is that it’s open-source - that is, R is free to use and anyone can see how it was made! The free and open nature of R led to the formation of the R community, a constantly-expanding network of individuals who contribute ideas and code to R and all of its packages, free-of-charge. But more on what R is actually used for…


R is a programming language.

Just like Python, R is a programming language. Its syntax (aka specific language structure) is a little different than Python’s, but a lot of it is identical! For example, in both languages, printing a string to the console is done via the same command: 

print("Hello SAAS!") ## Printing in Python
## Hello SAAS!
print("Hello SAAS!") ## Printing in R
## [1] "Hello SAAS!"

R is the name of both the language for computational tasks as well as the runtime environment that actually reads commands and scripts written in the R language to execute tasks. (Note: A command is generally used to describe an interactive style of programming, where you enter commands line-by-line in a console. An ordered list of commands comprises a script, which can be run automatically by the R program as well.)


R is a tool for statistics.

In contrast to Python, R is not a general programming language. Rather, R was developed for the use of data scientists and statisticians specifically. Because of this, many statistical tasks are very easily provided by even the base R libraries. For example, manipulating data and creating and evaluating linear models are readily available in R by default via the data.frame() and lm() commands respectively.

To do such tasks in Python, packages such as pandas and scikit-learn must be installed first. Of course, if you have already installed Anaconda in py0 of the SAAS Crash Course curriculum, you have these packages installed already.

(Note: A package (very similar to a library or module) is an addition to a base program that gives it more commands and functionality. For example, the ggplot2 package is a package for R that allows you to create high-quality graphs and visualizations.)

Because R was made specifically with statistics in mind, it is a premier language for statistical computing, data visualization, and machine learning. R makes data science incredibly quick to write and simple to understand.


R is a data visualization suite.

In addition to running statistical computations, R is very useful for graphing data as well. Especially within the context of RStudio, R (in conjuction with the ggplot2 package) can be used to make sophisticated displays of data, useful in both exploratory data analysis (EDA) and presentation of research findings.


What is RStudio?

By itself, R is a powerful programming language. However, its basic use is command-line only. In order to make writing R easier for statisticians, a group of statisticians based in Boston developed RStudio, an IDE for R programming. An IDE (integrated development environment) is a program designed to facilitate programming in a particular language. RStudio is a desktop (or server) application that eliminates the need for a CLI (as it comes packaged in RStudio itself) and includes a notebook panel for writing and running scripts. But this just scratches the surface of RStudio’s features…


RStudio is for joining various components in R.

RStudio is your one-stop shop for programming in R, reading R scripts and data, looking at help manuals, interactively running through tutorials and notebooks, and even making presentations like this one!

Just to name a few other features of RStudio, here are some of the more popular ones: - Managing packages - Displaying help and documentation - Cloud computing - Viewing and exporting visualizations - Publishing reports, presentations, and websites - Animated and interactive graphs - Writing in Python, C, Ruby, Bash, and more - Writing LaTeX


RStudio is for beautifying outputs.

As mentioned above, this workshop was made entirely in RStudio with the help of the knitr package (included by default). You can make professional research reports in the form of PDFs, webpages like this one (which can even include interactive graphs and code), and slide presentations.

Additionally, RStudio was created to interface well with ggplot2 and other datavis packages, allowing you to save plots as files on your computer, with options to specify resolution and picture size.


RStudio is for getting help quickly.

At anytime while writing an R Markdown or R Notebook file, you can use the console to test functions and read the associated documentation.

For example, if you’re ever curious about what a function (e.g. lm) does, simply type e.g. ?lm into the R interactive console to the lower-left of the application.


RStudio is for sharing open workflows.

Did you know? RStudio is compatible with Git (and Github)! Simply opening up a Git repository in RStudio allows you to add, commit, push, and pull all within RStudio itself - no need to open up your Terminal to do it from there!

In addition, because RStudio’s outputs are fully readable by any other computer (even computers that don’t have R or RStudio installed), RStudio facilitates in the sharing of code and statistical reports.


RStudio Desktop vs RStudio Server

Of note, there are actually two versions of RStudio. The first is RStudio Desktop, which will be covered by this tutorial. RStudio Desktop will run as a local application on your computer. The second version of RStudio, RStudio Server, will run as a locally-hosted server. If you were to link up that server to the Internet, you could access your RStudio server as a webpage, and run R scripts “on the cloud”. For most users, RStudio Desktop is more than enough for now.


What is CRAN?

You may have noticed some of the pages referred to in this guide contain links related to CRAN. CRAN (The Comprehensive R Archive Network) is a network of servers that each hold an identical mirror (synchronized download link) to various R packages, and the base R program itself. Universities and labs around the world maintain these servers, ensuring they are all up-to-date at all times. For example, Berkeley hosts its own server for R downloads, located at cran.cnr.berkeley.edu.


Installing R

For Mac

  1. Visit the R downloads page for Mac and follow the link to the latest version of base R (as of the writing of this workshop, R 3.4.3).
  2. Run the downloaded .pkg installer and follow the installation steps. For most users, the default specifications will do just fine.
  3. After you are finished, test your R installation by running R in your Bash console. Note: in contrast to most CLI commands, R must be uppercase.
  • Once R is running, try commands like 2+3 or log(8, base = 2)
  • Quit R by entering q() (there is no need to save the workspace image)

For Windows

  1. Visit the R downloads page for Windows and follow the link to the latest version of base R (as of the writing of this workshop, R 3.4.3).
  2. Run the downloaded .exe installer and follow the installation steps. For most users, the default specifications will do just fine.
  3. After you are finished, test your R installation by running R in your Bash console. Note: in contrast to most CLI commands, R must be uppercase.
  • Once R is running, try commands like 2+3 or log(8, base = 2)
  • Quit R by entering q() (there is no need to save the workspace image)

    • For Linux

    1. Visit the R downloads page for Linux and follow the link to your particular Linux distro family.
    2. Follow the steps listed in the README document (which, depending on the family you use, may appear as the HTML page) to install R onto your computer. For most users, the default specifications will do just fine.
    3. After you are finished, test your R installation by running R in your Bash console. Note: in contrast to most CLI commands, R must be uppercase.
    • Once R is running, try commands like 2+3 or log(8, base = 2)
    • Quit R by entering q() (there is no need to save the workspace image)

    Installing RStudio

    For Mac

    1. Visit the RStudio Desktop download page and follow the link to the latest version of RStudio (as of the writing of this workshop, RStudio 1.1.423).
    2. Run the downloaded .dmg installer and follow the installation steps. For most users, the default specifications will do just fine.
    3. After you are finished find the RStudio application by pressing CMD-SPACE and entering “RStudio” into the search bar. You should be able to run RStudio without any errors.

    For Windows

    1. Visit the RStudio Desktop download page and follow the link to the latest version of RStudio (as of the writing of this workshop, RStudio 1.1.423).
    2. Run the downloaded .exe installer and follow the installation steps. For most users, the default specifications will do just fine.
    3. After you are finished find the RStudio application by pressing WINDOWS and entering “RStudio” into the search bar. You should be able to run RStudio without any errors.

    For Linux

    1. Visit the RStudio Desktop download page and follow the link to the latest version of RStudio (as of the writing of this workshop, RStudio 1.1.423) for your Linux distro.
    2. Run the installer using the standard Bash command for your distro and follow the installation steps. For most users, the default specifications will do just fine.
    3. After you are finished, you should be able to run RStudio on your machine without any errors.


    Conclusion

    Today, we learned about R, RStudio, and CRAN. To summarize, R is a statistical programming language and runtime environment hosted by the CRAN mirros. RStudio is a (desktop) program used to write in R effectively, and includes a multitude of features to enhance the R programming experience. We then learned how to install R and RStudio from Berkeley’s CRAN mirror.

    Stay tuned for next week’s workshop on basic R programming!


    Additional Reading