You don’t need a PhD in statistics or years of coding experience to learn R. Anyone can learn the most powerful tool for data analysis and visualization.
R lets you wrangle, analyze, and visualize data quickly, efficiently, and beautifully.
Oh, and it’s free.
The R for the Rest of Us online courses are designed to guide you through every step on your R journey. All courses combine combine video lessons with exercises for you to practice what you’re learning. You also get access to carefully curated resources so that you can learn more about any topic that piques your interest. And, if you need help along the way, you can post questions and get answers.
From "what's R?" to "I love R!" in three courses. Start here when you're just starting out.
Fundamentals of R
The Fundamentals of R course covers RMarkdown, which makes it possible to go from data import to final report in one tool. It also offers a solid foundation in data wrangling, analysis, and visualization. After completing the course, you will be able to run analyses and produce beautiful reports, all without ever leaving R.
Going Deeper with R
Once you’ve got the fundamentals of R down, you can do a lot with R. But what happens when:
Your data isn’t in the right format to plot with
You get messy data and don’t know how to deal with it
You want to produce presentations from RMarkdown documents, not just reports
These are just a few of the questions that Going Deeper with R is designed to answer.
Getting Started With R
One of the main obstacles that many new R users face is simply getting started. This free course is designed to get new users, no matter what’s holding you back, up and running quickly. It takes you step-by-step, helping you download exactly what you need to get started.
Courses on particular topics taught by my talented friends in the R community.
Data Cleaning with R
Data cleaning skills are essential to your success. Fortunately, R has some great packages to help with data cleaning. Unfortunately, knowing what these packages are and how to use them is not straightforward. This course will help you learn how to take messy data and quickly clean it.
Inferential Statistics with R
R is unique among programming languages because it is designed for statistics. Unlike other general purpose languages that can work with data (e.g. Python), data analysis is at the core of what R does.
Given this, many people are disappointed when they struggle to figure out how to do inferential statistics with R. They say, "I know how to do a t-test in SPSS, I just want to do the same thing in R!" Sound familiar? If so, this course is designed for you.
Mapping with R
In recent years, R has become a fully-fledged tool for doing geospatial work. With R, you can make choropleth maps, hexbin maps, you name it. Any type of map you can think of, you can make it in R.
Don’t waste your time and your money learning to make maps in other tools. Get started today and you’ll be making maps in R in no time.
Package Development with R
Learning to make your own functions is a huge step on your R journey. But how do you reuse functions across projects? The answer is to make your own R package.
This course will show you how to make your own package and help make you more efficient than you've ever been before..
The Glamour of Graphics
Have you ever looked at a beautiful chart and thought: wow, I wish I could make something like that! It might seem like some people have an innate design sensibility that you don’t. It’s easy to think, “I’m a data person, not a designer. I’m never gonna be able to make beautiful data viz.”
But here’s the secret: anyone can make beautiful data viz.
Will Chase is a journalist at Axios who has worked for years to hone the craft of designing data viz. In this course, which builds on Will’s 2020 talk of the same name, he’ll give you a peek behind the curtain showing you
Using Git and GitHub with R
I’ve taught a lot of people to use R. Once they get the basics down, one of the first questions they ask is: how do I share my code with others? Should they email it? But then, if they need to change the code, they have to send it again. Should they put it on Dropbox? But if multiple people work on a file on Dropbox, all sorts of problems ensue.
What to do? The answer is Git and GitHub.
Using Git and GitHub allows you to share code, ensuring everyone has the most up-to-date code and allowing multiple people to work on the code at the same time.
Reproducibility for the Rest of Us
Ever had to deal with R code that worked in the past, but doesn't work today? The problem could be that your code is not reproducible. In this course, you'll learn to write code with good formatting and naming practices that can be easily read by humans and computers. You'll learn how to document your code so that collaborators and future you can understand it. And you'll learn about literate programming using Quarto to write reports that can be reproduced at any point.