Reproducibility is a critical part of research and analysis. How often have you revisited a project from a year ago, or opened a project on a different computer, and failed to get the same results (or even failed to run any of the code!)?
The R ecosystem makes it easy to create stable and reproducible analyses that anyone (including yourself!) can replicate and re-run in the future. In this talk, you'll learn how to make your work more reproducible using these tools:
- R Markdown
- rrtools and workflowr
- packrat and renv
- Docker and Binder
Though you'll be able to follow along with a free RStudio Cloud account (https://rstudio.cloud), it'll be best if you have R and RStudio installed locally on your computer. You might also want to install Docker on your computer (https://www.docker.com/get-started).
Andrew Heiss (@andrewheiss) is a visiting professor at Brigham Young University and assistant professor of public policy and management at the Andrew Young School of Policy Studies at Georgia State University. He teaches statistics, data visualization, and data science, and tries to do everything possible with R.