R Bootcamp Materials!

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To train new employees at the Wisconsin Department of Public Instruction, I have developed a 2-3 day series of training modules on how to get work done in R. These modules cover everything from setting up and installing R and RStudio to creating reproducible analyses using the knitr package. There are also some experimental modules for introductions to basic computer programming, and a refresher course on statistics. I hope to improve both of these over time. 

I am happy to announce that all of these materials are available online, for free.

The bootcamp covers the following topics:

  1. Introduction to R : History of R, R as a programming language, and features of R.
  2. Getting Data In : How to import data into R, manipulate, and manage multiple data objects. 
  3. Sorting and Reshaping Data :  Long to wide, wide to long, and everything in between!
  4. Cleaning Education Data : Includes material from the Strategic Data Project about how to implement common business rules in processing administrative data. 
  5. Regression and Basic Analytics in R : Using school mean test scores to do OLS regression and regression diagnostics — a real world example. 
  6. Visualizing Data : Harness the power of R’s data visualization packages to make compelling and informative visualizations.
  7. Exporting Your Work : Learn the knitr package, and how to export graphics, and create PDF reports.
  8. Advanced Topics : A potpourri of advanced features in R (by request)
  9. A Statistics Refresher : With interactive examples using shiny 
  10. Programming Principles : Tips and pointers about writing code. (Needs work)

The best part is, all of the materials are available online and free of charge! (Check out the R Bootcamp page). They are constantly evolving. We have done two R Bootcamps so far, and hope to do more. Each time the materials get a little better.

For those not ready for a full 2 to 3 day training, together with a colleague at wor (Justin Meyer of RProgramming.net) we have created a 2-3 hour introduction that is also available on the webpage. 

And, of course, all the materials are online on GitHub. Look for future blog posts on tips for running an R bootcamp and some practical advice. For now, enjoy the materials, and feel free to leave a comment here for feedback, fork the GitHub repo, make a pull request, or take and adopt the materials however you see fit! One parting piece of advice though — don’t wait until day two for the data visualization module — give them the ggplot2 goodness ASAP. 

To leave a comment for the author, please follow the link and comment on their blog: Data, Evidence, and Policy - Jared Knowles.

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