This post features original artwork submitted by various artists for United Nations Global Call Out To Creatives - help stop the spread of COVID-19.
In March, Desirée De Leon and I did a webinar called “Sharing on Short Notice: How to Get Your Teaching Materials Online with R Markdown." Since then, we’ve watched as many devoted educators have worked overtime to overhaul their teaching materials into remote-friendlier formats on very short notice.
While these new remote learning resources were created first and foremost to replace in-person courses, some instructors went the extra mile to openly share them for everyone. This may include class schedules/readings, slides, videotaped lectures and screencasts, and sometimes even RStudio Cloud projects, making it possible for us non-students to follow along from the comfort of our own homes. This post is a roundup of some of these new open learning resources.
But first, a disclaimer. Most people I know have less free time now, not more. Even if you do technically have free time you could devote to learning, you may not have the cognitive energy to learn anything new. If you fall into either of these two camps, bookmark this post for the future, and then promptly close this browser tab. Please do not let this post make you feel guilty or stressed. Learning will only stick if you feel good while doing it.
Having said that, there are also folks who were caught by this quarantine at a time when they needed to learn new skills to either get a job or get a better job. If you fall into this camp, then you are in luck, because the resources below were created by some of the most engaging and thoughtful educators in data science. And because they were all created relatively recently, you can also be sure that the content is current.
Finally, please keep in mind that these resources are provided as a courtesy by the instructor—if you do decide to use them and have questions or get stuck, ask for help on a dedicated help forum like RStudio Community; do not contact course instructors directly either in private or public (i.e., by tagging them on twitter).
Ready for R
This course is created and taught by Dr. Ted Laderas, an Assistant Professor at Oregon Health and Science University and an RStudio certified trainer. In Ted’s own words:
“This course is meant to be a gentle introduction to using R/Rstudio in your daily work. It aims to teach useful skills (visualization, data loading, data filtering and manipulation, simple statistics) that students can immediately use in their work. No prerequisites or previous experience required. It is not meant to be a substitute for a full programming course or a full course in statistics.”
For external folks, Ted also started a mailing list that will let you know when materials are updated: https://ready4r.netlify.app/mailing/. To compliment these materials, you may also want to check out Ted’s self-paced R-Bootcamp course, created with Jessica Minnier: https://r-bootcamp.netlify.com/.
Robust tools
This course is created and taught by Dr. Danielle Navarro, an Associate Professor at the University of New South Wales and an RStudio certified trainer. Danielle is also a co-author on the work-in-progress 3rd edition of the book “ggplot2: elegant graphics for data analysis” and recently has started sharing her original generative art created with R. This course is unique because it teaches many of the tools that researchers (and really all data scientists) need, but that too often we assume you’ll learn simply through use alone. In Danielle’s own words:
“ Data science with R: A robust toolkit for psychological research covers an introduction to R programming, modern data visualisation and data wrangling, how to structure your projects, version control and how to write professional documents in R. The course is designed for novices, and no preexisting familiarity with these tools and no programming background is assumed.”
Each section in this course features links to an HTML slide deck, YouTube video, and an RStudio Cloud project.
Danielle also has an online resource called “Learning Statistics with R”, originally designed to teach intro statistics to psychology undergrads.
Advanced Statistical Computing with R
These are the materials for a Spring 2020 undergraduate course offered by California Polytechnic State University professors Drs. Kelly Bodwin (an RStudio certified trainer) and Hunter Glanz. In their own words:
“Advanced techniques for efficient use of computers to perform statistical computations and to analyze large amounts of data. Includes version control systems; tools supporting reproducibility; functional programming; randomization and bootstrapping; dynamic data visualizations; and R package development.”
Follow along with their learners using this course schedule.
Week-by-week, Kelly and Hunter provide a curated sequence of readings, videos, and small practice activities with careful time estimates so you know what you are signing up for.
Each week also features a lab assignment and extra challenges so you can practice if you have the time. There is also a public Discord channel so you can learn with others.
Data Visualization
Created by Professor Andrew Heiss at Georgia State University, also an RStudio certified trainer, this website is an asynchronous online course — with hours of video, interactive code, code examples, and all sorts of neat stuff! From Andrew’s course description:
“Use R, ggplot2, and the principles of graphic design to create beautiful and truthful visualizations of data”
All materials are open source and creative commons licensed, so anyone can adapt and improve it later.
A personal aside: this beautiful website is built using the Hugo Academic theme with the R blogdown package. You can see the source code for the website on GitHub. Andrew’s website is another great example of a course website that was shared on short notice!
Get Started with tidymodels
We recently launched the new tidymodels.org website. If you don’t know what that word means, it is a collection of packages for modeling and machine learning using tidyverse principles. One of the most popular resources on the new website is the Get Started section. It features five articles, starting with how to create a model and ending with a beginning-to-end modeling case study. While there are no interactive exercises (yet!), you can copy/paste the code to work through the materials locally. Be on the lookout for integrations with RStudio Cloud and learnr tutorials that will work within the IDE after our summer intern, Ezgi Karaesman, works her education magic 💫
Supervised Machine Learning Case Studies with R
This self-paced course is newly updated to use the tidymodels framework for predictive modeling, brought to you by Julia Silge. Julia is a member of the tidymodels software engineering team, an RStudio certified instructor, and prolific data science blogger (don’t miss her #TidyTuesday posts with video screencasts!). When I first started my own machine learning journey, Julia’s course was a welcome break for me from predicting home prices in Ames, Iowa. Julia has a talent for explaining complicated things in simple terms, and the whole course is a breath of fresh air if you’ve been trying to learn machine learning by wading through stuffy textbooks. I was lucky enough to get a preview of this revised version, and wholeheartedly recommend—it is a great interactive compliment to tidymodels.org.
Applied Machine Learning
Scikit-learn developer and Columbia University Associate Research Scientist Andreas Mueller has shared his 2020 Applied Machine Learning course. This includes a schedule with links to all his slides (including presenters notes!), and 21 videotaped lectures in a YouTube playlist. Obviously, this is not an R course; it is in Python 🐍. Regardless, any data scientist can benefit from these materials, and it may give you R lovers an excuse to test out using Python and the reticulate package.
R-Hub for Package Developers
Maëlle Salmon, a research software engineer with ROpenSci and member of the RLadies Global team, has been diligently cataloging a treasure trove of knowledge for package developers to ease all steps of the R package development process. This project has been funded by the R Consortium.
Check out her work on the R-Hub documentation site: https://docs.r-hub.io/
And related series of blog posts here: https://blog.r-hub.io/
Remote RLadies Meetups
Finally, if you are craving some socially distant social interactions with your learning, many RLadies meetups have gone online. You can see all the future events anywhere in the world here: https://www.meetup.com/pro/rladies/
I hope these resources are useful if you are able to learn now, or even if you are just daydreaming about a time in the future when you’ll be able to get lost in learning something new again. Hang in there.
