Module 1.3: Downloading R

Author
Affiliation

Alex Cardazzi

Old Dominion University

All materials can be found at alexcardazzi.github.io.

Why use code?

You might be thinking, “why can’t we use Excel?” It’s not that Excel is bad – it’s that R is better. Using code allows for reproducibility, customization, and automation.

Perhaps the best argument for writing code over just using Excel is one about fixed vs marginal cost. Learning how to use Excel requires a much lower fixed cost than learning how to code. Everyone has seen Excel, data are nicely formatted into cells, and you can point-and-click to generate nearly everything. Alternatively, programming is a language, and learning a language is slow and potentially painful. However, once you have working code written, you never have to write it again! In other words, the future marginal cost of coding is much, much lower.

Why use R?

Your next question, however, might be, “why R? My comp-sci friends use Python, C++, and SQL.”

There are a few arguments I will provide: First, R is becoming one of the most popular languages for working with data. R is uniquely suited for for statistical analysis and visualization, which you will see as we go through the course. Second, R is an open-source language. This means that it is free, and it is developed by its users. This allows for rapid development of cutting edge statistical methods, providing a significant advantage over other languages. Finally, R is the language that I know best and the one I am most qualified to help you learn

Some Basics of R

The following might seem abstract at the moment, but hopefully will become more concrete and clear as time goes on.

  • Everything in R is an object with a name. Your dataset(s) will be an object (or objects), and you will assign a name to it (them).
  • Functions are how you do things (e.g., calculate statistics) to/with objects.
  • Functions come built-in (called base R) or can be loaded from libraries.
    • You can also write your own functions! In fact, libraries are just sets of functions written by other people.
  • You can have multiple datasets loaded into R at once
    • The Excel analogue would be having multiple sheets.

Now, let’s download R and RStudio. If you run into trouble, feel free to jump ahead to the next sub-module. There, you will download Claude Code, which can help you download R and RStudio as well.

Download R

First, we have to download R via cran.r-project.org. This is the actual language, and what you should think of as the “brain” of R.

  • This website should look like it was built in the 1990s or early 2000s. This sometimes makes students skeptical, but fear not.
  • Make sure to choose the correct operating system and latest version of R.

Downloading RStudio

Second, we need to download RStudio via posit.co. If the previous download is the brain, you should think of RStudio as the body. We will only ever interact with R through RStudio in this course. Once again, make sure you select the correct operating system for your machine.

Posit used to be known as RStudio, but has since changed names to generalize themselves. RStudio (the product) continues to be developed and maintained by Posit. Think of this like Meta or Anthropic (the company) running Facebook or Claude (the product).

Exploring RStudio

Screenshot of RStudio (on Windows).

Screenshot of RStudio (on Windows)

When you open RStudio for the first time, you will see four panels like in the above image. It is likely that your version of RStudio has a white background with blue or black text. If you would like to change this, go to “Tools > Global Options… > Appearance > Editor theme”. I like a darker theme to make it easier on my eyes if I am looking at the screen for long periods of time.

The four panels shown above are as follows:

  • Top Left: Source – This is where you will write the R code you want to save. In other words, this is where you write and save your work, usually called R scripts or Quarto Markdown files (.R or .qmd).
  • Bottom Left: Console – When you execute (or run) code, you will usually see output here. This is also a place you can write code you do not want to be part of your final script. If you were a painter, the Source panel would be your canvas and the Console would be your palette. Also note the Terminal tab here. You can interact with Claude Code here once it is set up.
  • Top Right: Environment – Here is where we will be able to see all the objects (data, etc.) that we are working with in the moment. To clear your environment, paste the code rm(list = ls()) into your Console, and hit Enter.
  • Bottom Right: Output – This is mostly where you will see plots you have generated or files you have rendered, but can also see files on your computer, packages you have installed, and “Help” for certain functions.

Screenshot of RStudio with panel labels.

Screenshot of RStudio with panel labels.

Code Tips

Before we start writing code, here are some important tips and tricks that will make your life (our lives) easier.

  • The hardest part about coding is learning how to Google and/or prompt your AI. You read that correctly. The best programmers are the best Googlers/prompters. There is a wealth of knowledge online, and knowing how to sift through it all is truly a skill.
  • Write yourself comments. You can do this by writing # before you type something. This will help you remember what your code does after you have been away from it for a long time. Sometimes, re-reading (decyphering) uncommented code is harder than re-writing it from scratch.
  • Your code probably won’t work the first time. Your code probably won’t work the first few times. However, when trying to fix something, only change one thing at a time.
  • Give objects informative names. It is easier to understand code when things are named “country_gdp” or “yearly_unemployment” rather than “gdp2” or “x”.

As a final tip, and this one is important, we are going to change some default settings to RStudio.

  • Click on “Tools > Global Options… > General”
  • Uncheck “Restore .RData into workspace at startup”
  • Change “Save workspace to .RData in exit:” to Never

It might seem like these auto-saving features are a good idea, but trust me: you will be much better off without it. Do not skip this part!

R’s Data Types

R has a few different data types:

  • Numbers: You can type a number into R and R will know its value.1
  • Boolean: This data type is made up of TRUE and FALSE values. Think of this like binary values (0 and 1). Here is a picture of George Boole.
  • Characters: This datatype is reserved for text. Sometimes characters are called strings, but they are always found inside quotation marks. In R, you can use " or '.
  • Factors: Factors are a weird mix of characters and numbers. Perhaps the best way to think of them is as a categorical variable. In this course, we will generally avoid the use of Factors.

Evaluation

To execute / evaluate / run code in R, there a few different ways to do it. The easiest way is to highlight whatever you are interested in running, and typing ctrl (Cmd on Mac) + enter. You can also place your cursor on the line of code you’d like to run, and use the same keys to run that specific line. There is also a button on the top right of the Source panel that says “Run”, which will do the same thing.

WebR

Before moving on to explore basic operations, I want to mention something you’ll see embedded throughout this course. I will be exhibiting code in each module in static code blocks. Many times, these code blocks, sometimes called code chunks, might generate output, plots, both, or nothing. Unfortunately, these code blocks are, for all intents and purposes, set in stone. In other words, besides collapsing/expanding them, you cannot really interact or experiment with them. This probably stifles student curiosity, since you’ll probably want to tweak things as you’re going through the notes.

To address this, I have included WebR chunks into each module’s notes. These chunks will look a bit different from the static chunks, and I encourage you to interact with them! You can write, alter, and execute code inside each chunk, and each WebR chunk will “remember” what you’ve run in other chunks. Go ahead and explore a bit with the chunks below:

While the WebR chunks can “talk” to one another, and the static chunks can talk to one another, there is no communication between the two types of chunks.

Code 
# This is a static chunk
# Notice how you cannot modify what's written here.

Basic Operations

Once we have a handle on data types, we can begin to perform operations on data. For numeric values, we can use simple arithmetic operations such as addition (+), subtraction (-), multiplication (*), and division (/).

Most, if not all, of the code blocks (and output) in this course will be collapsable. Click on them to hide/display the code (or output).

Code 
# Example Comment.  Get ready to math.
5 + 5
10 / 3
4 + 3 * 100 # Another comment. Something about PEMDAS.
(4 + 3) * 100 # Something else about PEMDAS.
Output 
[1] 10
[1] 3.333333
[1] 304
[1] 700

Next, play around with some of this in WebR:

When we have boolean values instead of numbers, we need to use logical operators: And (&), Or (|), Not (!)

  • “And” and “Or” take two boolean values and combine them to into a single boolean.
    • “And” returns TRUE only when both values are TRUE.
      • Example: The sky is blue (TRUE) & the grass is green (TRUE) results in TRUE
      • Example: The sky is green (FALSE) & the grass is green (TRUE) results in FALSE
    • “Or” returns TRUE only when both values are not FALSE (or at least one is TRUE).
      • Example: The sky is blue (TRUE) | the grass is green (TRUE) results in TRUE
      • Example: The sky is green (FALSE) | the grass is green (TRUE) results in TRUE
  • “Not” negates a single boolean value.
  • It may feel a bit clunky, but logical operators can be thought of a lot like English.
Code 
TRUE & FALSE
TRUE | TRUE
TRUE & !FALSE
Output 
[1] FALSE
[1] TRUE
[1] TRUE

Try some of these with WebR. Un-comment ones you want to try by deleting the hashtag. Re-comment them by adding the hashtag. You can also try various other options.

Some other logical operations to note are ==, <, >, >=, and <=. These are logical operations applied to numerical values, and you will likely be much more familiar with these.

Code 
5 < 3
5 > 3
5 > 3 & 4 > 3
5 > 3 | 4 > 5
Output 
[1] FALSE
[1] TRUE
[1] TRUE
[1] TRUE

These logical operations are incredibly important, because we will use these to subset (or filter) our data. For example, you may want to subset your data to look at rows of only Males under the age of 25. This will look something like Gender == "Male" & Age < 25.

We will not discuss operations for characters and factors until later in the course, but there may be times where you will want to convert data from one type to another. To convert from a number or boolean to character, you can use as.character(). To go from text to numeric, you can use as.numeric().

Code 
as.character(5)
as.numeric("5")
as.logical("FALSE")
as.numeric("Five")
Output 
[1] "5"
[1] 5
[1] FALSE
[1] NA

Notice how the final line produces an NA value. Seeing an NA value is the same as seeing R shrug its shoulders. It is not smart enough to know that "Five" is 5, so it returns a “missing” value. NA values can mess up a lot of things in R. For example, what is the average of this collection of numbers: 2, 4, NA, 8? R will return NA when asked, because it isn’t sure how to think about the NA here. Should you remove the missing value? Replace it with zero? It is not always clear. A helpful function, therefore, is is.na(). This returns a boolean equal to TRUE when the input is NA.

Code 
is.na(as.numeric("5"))
is.na(as.numeric("Five"))
Output 
[1] FALSE
[1] TRUE

As a final note, if you run the above in RStudio, you might get output saying Warning: NAs introduced by coercion. This is R giving you a heads up about what I just mentioned. Sometimes, this warning is expected, but other times it’s a good signal to check your data/code!

Footnotes

  1. This is trivial, I know.↩︎