Resources for learning R

This was a no-code approach to data literacy, but several people have asked for some resources for learning R. Here I’ll share a few of my favorite things. One thing to know right off the bat is that R has several different-looking but equally valid “syntaxes”

Tidyverse syntax uses packages like ggplot2 and dplyr, and needs tidy data. The authors of the tidyverse have a “tidy tools manifesto” that explains their design philosophy. A lot of the resources I link here will be tidyverse-forward. If you are doing your own data analysis work in R, or teaching courses focused on data analysis, this is what I recommend. My colleagues and I have a paper explaining why: An educator’s perspective of the tidyverse
Formula syntax is used by modeling functions like lm() as well as packages like mosaic, designed for teaching introductory statistics in a very consistent way. If you are teaching intro stat, this is the syntax I recommend you teach. It does not have support for data wrangling/tidying, so for big data analysis projects you have to venture outside the syntax. If you want to learn more about this syntax, read my pre-print Teaching modeling in introductory statistics: A comparison of formula and tidyverse syntaxes
Base R syntax is the syntax in the base of the programming language R. R packages are written in base R. It is characterized by the use of $ and [ , ] operators. If you want to write R packages or have your students write them (unlikely!) teach this syntax. Many “old school” people start with this syntax because they believe it is foundational. It’s not! Don’t be fooled.

I have a keynote talk I gave at useR! (the international R users conference) titled Speaking R about how to vocalize R code in different syntaxes while you’re teaching it.

R and RStudio

R is the programming language, which you need in order to code in R! It is free and open source. You could code in R from the commandline or from the default R Graphical User Interface (GUI) but…

RStudio is the industry-standard Integrated Development Environment (IDE), which makes it easier to code in R. It is free and open source. Some companies buy an enterprise version, which allows them more support but not necessarily more features.

Downloading R and RStudio

In order to use R and RStudio, you need access to them! There is a cloud version that allows you a few hours of compute time per month for free, and is pretty cheap after that. If you just want to give R a quick try, this can be a good way to start. I have used this with intro classes where I don’t want students to get bogged down in software installs. In upper level classes where we use it more heavily, I ask students to install it locally.

If you really want to learn R, I suggest installing it locally.

There are several steps to this process, so I made a YouTube video to walk students through it. There are three main steps:

Download and install R
Download and install RStudio Desktop
Install whatever packages you need to do your work. I’ve made a big list of potential packages, which you may or may not need all of. To install them, run this entire piece of code:

install.packages(c("tidyverse", "babynames", "broom", "coefplot", "cowplot", "devtools", "drat", "fueleconomy", "fivethirtyeight", "formatR", "gapminder", "GGally", "ggforce", "ggraph", "ggrepel", "ggridges", "graphlayouts", "gridExtra", "here", "hexbin", "interplot", "janitor", "margins", "mgcv", "maps", "mapproj", "nycflights13", "RColorBrewer", "rmarkdown", "sf", "skimr", "usethis", "viridis", "viridisLite"))

I’m happy to help troubleshoot if you run into any issues!

Learning R

If you want a self-paced way to learn R, DataQuest is the website I recommend. It has R content as well as other programming languages like Python. It does cost money
Their competitor that I do not recommend is called DataCamp. Here’s an overview: Don’t use DataCamp. There is so much more media coverage. (Oh, and the CEO has been quietly reinstated!)
If you want a free way to learn R, the website learnR4free has (you guessed it!) free resources for learning R. They have materials in English, Turkish, and Spanish.
- one that I know is good: R Bootcamp
The book R for data science (free online) is a common resource for learning R. There is also a supportive online community called the R for data science online community that you can join for free. They have a Slack group, and I have heard there is almost always a group of people working their way through the r4ds book together and asking questions online.

Community

One of the things that makes R a popular programming language is our great community. I’ve already mentioned the R for data science online community, which is an online community. There are also lots of in-person communities for R:

R Ladies is a group for women and gender minorities who use R. Most major cities have a chapter. For example, there is a New Orleans chapter!
Usually there is also a general R users group in each city. I don’t see one in New Orleans, but I do see a Tulanians Who Enjoy R Coding (TWERC) group.

Resources

As you learn, you will have questions! Here are some resources to help you answer them.

The Posit cheatsheets are overviews of common functions you might need, including illustrations to help you understand what they do. Some useful cheatsheets:
- Data visualization with ggplot2
- Data wrangling with dplyr
- there are also “contributed” cheatsheets cheatsheets made by people other than the company itself.
- My syntax comparison cheatsheet is one
On the subject of cheatsheets, I have two “Enough R for Intro Stats” cheatsheets, which are given to students in my intro stats courses STAT 220:
- Formula syntax (source code, Rmd)
- Tidyverse syntax (source code, Rmd)
Sometimes you will have a question you need answered. If you search online, a lot of good answers are available on StackOverflow. However, StackOverflow is not a place for beginners to post questions (their policies are not beginner-friendly– you can’t re-ask a question that’s been asked before!). Instead, the place to ask questions is the Posit forum. This forum encourages people to re-ask questions, because they acknowledge we’re all learning!

Books

Again, the book R for data science is very commonly used, for a reason. It’s a great intro to the tidyverse, and you can read it free online
R Graphics Cookbook will help you make your ggplots beautiful. It’s also free online, or you can buy a paper book.
Modern Data Science with R is a data science textbook that uses the tidyverse. Again, free online or you can buy the book.
There’s also Statistical Inference via Data Science again free online (are you sensing a theme?).
Text Mining with R, ditto.
Spatial Data Science With Applications in R, yep, also free.
If you want a recommendation for a book with a particular application, just ask.

Teaching R

Again, you might want to read me and my colleague’s piece An educator’s perspective of the tidyverse
Mine Cetinkaya-Rundel (the first author on that piece) has a website Data Science in a Box, which has lots of materials for teaching data science. It also has her design philosophy, which includes “Start with cake”

Videos

Let’s say you’re not sick of my voice after a full week of it. I have a ton of YouTube videos with content related to learning R. For example,

full semester of intro statistics labs taught with the tidyverse, and accompanying documents
full semester of intro statistics labs taught with formula syntax, and accompanying documents

Writing reproducible reports

Another powerful thing about R is the tooling around writing reproducible reports. Here are some resources related to that:

Quarto websites. Quarto is a fancy version of Markdown that lets you include code (R, Python, or other languages) and create “literate programming” documents.
- My STAT336 website was made with Quarto
- That paper on the tidyverse was written in RMarkdown, the previous generation of what is now Quarto.
- My paper on teaching different R syntaxes was also written in RMarkdown
To do this sort of stuff, you would need the rticles package