Even if you attended RStudio’s pre-conference two-day training last month, you could only attend one workshop—and there were more than half a dozen. Now, though, many materials including slides and R code are available free online. Here’s how to get them.
Most of the code and slides have been . If you don’t have git version control set up on your system, you can download a zipped file of any repository. But git and GitHub do make it easier and more elegant. Check out episode 33 of Do More with R below if you’d like to learn about git and GitHub in RStudio:
Tidy time series and forecasting in R
Instructor Rob J. Hyndman, professor statistics at Monash University, literally — not to mention the R forecast package. I was torn between attending this one and the machine-learning workshop I ended up taking. Happily, even though it’s not quite as good as being in a classroom in person, the written materials and code are online.
The GitHub repository is at and his Forecasting Principles and Practice textbook is free online at .
Modern geospatial data analysis in R
“You will learn to read, manipulate, and visualize spatial data and you’ll be introduced to functionality that will have you saying, ‘I didn’t know you could do that in R!’” touts this workshop’s overview. This is another one I wish I could have attended.
This class featured the sf, tmap, mapview, raster, and dplyr packages.
with instructions on how to download the rest.
Workshop leader Zev Ross said he posted both and a .
on how to download and install the with exercises and solutions.
Machine learning in R
There were two workshops on machine learning this year: an introduction to the still-evolving tidymodels machine learning package ecosystem and a more advanced session with Max Kuhn, creator of the well-known caret package.
Introduction to machine learning with the tidyverse
This workshop has its own website where you can download slides, exercises, and solutions from Alison Hill’s sessions: . There is also a .
Applied machine learning in R
Max Kuhn’s session has a website at . Toward the top there are links to see parts 1 through 6 separately. There is also a .
which includes a number of R Markdown notebooks with code and explanations as well as links to slides and data. This was taught by Brad Boehmke, director of data science at 84.51°.
Text mining with tidy data principles
Julia Silge, co-author of , led this workshop. Her slides are at (Day 1) and (Day 2). The GitHub repo at includes slides and R Markdown documents with code.
Big data analysis in R
This workshop, taught by RStudio engineer James Blair, focused on using dplyr with data.table, databases, and Spark for large-scale data. It also used the vroom, dtplyr, and DBI packages.
The GitHub repo at includes an intro, slides, and workbook directory with R Markdown documents. The workshop exercises and code are also available as on online book at .
Shiny from start to finish
If you’ve wanted to learn the Shiny R interactive web framework — or if you’ve worked with it but wanted to up your game — Macalester College professor Danny Kaplan’s Shiny workshop GitHub repository features slides and project code. You can also clone the project with a free RStudio Cloud account at .
In addition to the , there is a that is definitely worth a visit.
R Markdown and interactive dashboards
This two-day workshop by Yihui Xie (creator of numerous R packages including knitr and DT and the co-author of Shiny, R Markdown, and leaflet) and RStudio education director Carl Howe was aimed at helping attendees create powerful interactive documents and dashboards.
The objectives, according to the workshop description, included the following:
- The full capabilities of R Markdown
- How to parameterize and publish reports from R Markdown
- How to create interactive dashboards using htmlwidgets and Shiny
The workshop GitHub repo at includes a materials directory with slides, exercises, cheat sheets, and more.
What they forgot to teach you about R
It sounds like an introductory workshop, but this was actually “designed for experienced R and RStudio users who want to (re)design their R lifestyle,” according to the session overview. “You’ll learn holistic workflows that address the most common sources of friction in data analysis. We’ll work on project-oriented workflows, version control for data science (Git/GitHub), and how to plan for collaboration, communication, and iteration (including R Markdown).” Instructors Kara Woo, Jenny Bryan, and Jim Hester are all well-known in the tidyverse world.
Find the GitHub repository at and “the one true URL that links to everything!” at .
Building tidy tools
Taught by Charlotte Wickham and Hadley Wickham, this workshop was aimed at “those who have embraced the tidyverse and now want to expand it to meet their own needs,” according to the workshop overview. It discusses API design, functional programming tools, the basics of object design in Amazon S3, and the tidy eval system for non-standard evaluation.
There is a with slides, R Markdown documents, and more.
A practical introduction to data visualization with ggplot2
This workshop covered “basic principles behind effective data visualizations” as well as learning how to build good graphics with ggplot2. It was taught by Duke University professor Kieran Healy, author of . The workshop repo is at .
My organization’s first R package
If you’re interested in creating packages at your workplace for “easier data access, shared functions for data transformation and analysis, and a common look and feel for reporting,” you may want to check out this workshop materials by software engineer Rich Iannone and R developer and Ph.D. student Malcolm Barrett.
You can find the GitHub repo at .
Workshops for R beginners
was, not surprisingly, a workshop aimed at power Excel users who want to start incorporating R into their workflow.
And , taught by Hadley Wickham and Amelia McNamara, was a “two-day, hands-on workshop designed for people who are brand new to R and RStudio.”