B Great R Resources

B.1 Key takeaways

  • If this set of resources doesn’t fit your learning style, there are lots of others that might
  • In our class, we are working almost entirely in the “tidyverse”, and in R Markdown documents. Some of the resources you’ll find elsewhere might not.

B.2 Coursework in R

Other professors and universities are also trying to teach R in journalism and other classes. Here are a few that have particularly rich materials for their students:

  • Jesse Lecy’s “Introduction to data science for the social sector,” an ASU course that teaches R in the context of non-profit management. His site, https://ds4ps.org/cpp-526-fall-2019/ has a custom-made textbook that covers much of the same material as us.

  • Matt Waite at the University of Nebraska has been teach R in the context of a sports journalism class and a data journalism. His textbooks are all freely available on Github. One minor difference that might trip you up: We’re using R Markdown, and he’s using R Notebooks. They’re very similar, but the “knit” option isn’t there when you use Notebooks – only a “Preview”. You’ll know what that means when we get moving.

  • Christian McDonald at the University of Texas has also created a textbook for his data reporting class, which walks through the same data set all the way through.

  • Peter Aldhous’s “R for Data Journalism”. Peter uses an R script instead of a markdown document. To do that, you create a new R document instead of a markdown, and run code by pressing CTL-Enter next to the lines that you want to run. It won’t be formatted, but it takes away one of the first sets of confusion .

B.3 Instructional screencasts and videos

  • Mary Jo Webster’s R for journalism videos

  • Datacamp has a free introduction to its R class that includes a lot of what we’re doing, called “Transforming data with dplyr”

  • Andrew Ba Tran’s R for Journalists course, originall created for a MOOC offered by the Knight Center for the Americas. Previous Cronkite students say that it goes a little fast for someone with no experience in other lanugages, but it has most of what you’d need.

  • Ben Stenhaug, a Data Science for Social Good fellow at Stanford, has put together some screencasts and exercises called teachR on starting out in R and the tidyverse.

  • “Tidy Tuesday” screencasts from David Robinson, an example using fivethirtyeight.com data on education and salaries. These are very long – up to an hour – but they walk through what it’s really like to take a dataset from scratch and try to find something interesting. I disagree with one thing he does though – I believe you should read the documentation FIRST, not after you’ve done a lot of guessing about what columns mean.

  • Sharon Machliss’ “Do More with R” YouTube series. These are fairly advanced single-topic, short videos.