Background

Before we start exploring some of R’s basic functionality, I’m going to set the stage a little on what R and R studio are, why we are using these tools in particular, and what we will need to know before we dig in.

What is R?

At it’s core, R is a programming language. There’s a lot to say about this from a computer science perspective, but, for our purposes, you can just think of R as a language with a very particular structure that’s designed to tell our computers what to do.

There are all sorts of different programming languages out there, and they all offer certain benefits or cater to particular computing needs. Unlike some general purpose languages like C, C++, or Python, R is relatively specialized, and this is part of what makes it so useful for us. R is designed with statistical computing as a primary motivator, and now—roughly 30 years into its tenure—stands as one of the most widely adopted resources for statistical data science in the social sciences and beyond.

Though there can be a bit of a learning curve when getting used to R, we will focus on exactly the things that we need and build ourselves up slowly. Once you get used to it, R will allow you to perform incredibly complex statistical procedures with relative ease, and it can even help us with other related tasks like visualizing and presenting our analyses.

What is R Studio?

We are going to pair R with the software R Studio, which is what’s known as an Integrated Development Environment (IDE). Programming languages can be leveraged in a number of different ways. You could run R commands entirely from a Windows command line or Mac terminal. But that would probably not be very ideal for us—not to mention sort of ethereal and frustrating for those of without any programming experience. IDEs provide user-friendly interfaces for working with programming languages, so that we can easily manage our code, quickly generate and view the output of our analyses, and generally keep track of what we are doing with R. There are lots of other IDEs out there, but R Studio is an ideal balance of ease and power, so it will serve as our IDE of choice.

The best way to think about R Studio’s relationship to R is by framing it as an analogy with a desktop computer. R Studio is to R as a monitor is to a computer. R is the thing that’s doing all the heavy lifting computationally, and R Studio is the thing that allows us to view and interact with R in a way that’s simple and straightforward.

Why R?

Ultimately, R is just one of several different options we could have gone with for a course like this. STATA, SPSS, Python, and even MS Excel are used with regularity for quantitative analysis in academia and industry. However, R has a few advantages that make it well-suited for us.

While software like Excel, SPSS, and STATA arguably have more accessible, user-friendly interfaces, the upper limit of their capabilities is far lower than R. On the other end of the spectrum, Python is a little overkill for our purposes. While it has some excellent resources for data science, it’s also used for a wider variety of programming tasks related to web- and software development. I once heard a computational sociologist joke that using Python for something that can be done in R is like using a nuke when all you need is a hammer. While R is not quite as expansive as Python, it’s specialization in statistical computing provides a helpful balance for us when it comes to our goals for the course.

Another big perk of R is that it is completely free. This is true for Python as well, but SPSS, STATA, and Excel all require the purchase of a license, and some of them can get very pricey. You have access to all of these programs as an NC State student, but you will be able to use R regardless of whether you are on an NC State computer or even enrolled in the university at all. Relatedly, R is completely open-source—you can view it’s source code, and even modify it for your own purposes. This makes R incredibly customizable, and this open-source culture has brought about a dedicated community of researchers and data scientists who regularly contribute new functional add-ons to R.

Lastly, R is quite marketable as a technical skill. Especially for those who want to go on to do research of any kind, experience with R will likely be seen as a plus. I’ve been on the lookout for various research, teaching, and industry jobs as I get ready to enter the job market, and I see calls for R as a required or preferenced skill all the time.

In sum, R provides us with the ideal balance of computing power and feasibility while helping keep our pockets full and giving us some skills that translate well beyond the course.

Acquiring R and R Studio

All of the CHASS computers (and likely most NC State computers) come with R and R Studio, so you do not need to download them, but you may find it convenient to work on R assignments using your own personal device, so I’ve provided some instructions below.

Note that you will want to install R first,

Downloading R

You can download R from the Comprehensive R Archive Network (CRAN).

When you click that link, you will arrive at CRAN’s homepage. Navigate to the sidebar on the left, find ‘Download’ near the top, and then click ‘CRAN’. This will take you to the ‘mirrors’ page. Mirrors are just different host locations for downloading the R installation files. This allows you to maximize download speed by choosing a nearby server, so scroll down to ‘USA’ and choose one of those (I usually opt for the Durham, NC mirror).

Unless you are very experienced with computers, you should download one of the options listed as ‘pre-compiled binary distributions’. These will typically be the first options listed. Don’t even worry about what that means if you’re not familiar. Just choose the one that reflects your operating system (there are options for Windows, Mac, and Linux) That should download an R installer, and you can follow the directions to complete a default installation.

Downloading R Studio

R Studio is a little more straightforward to download. Just navigate to its homepage, scroll down a little, and you will find a big button that says ‘Download R Studio for [your operating system]’. Go ahead and run the installer with the default settings.