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