This is Andrew Marder’s adaptation of Karl Broman’s Tools for Reproducible Research.
A minimal standard for data analysis and other scientific computations is that they be reproducible: that the code and data are assembled in a way so that another group can re-create all of the results (e.g., the figures in a paper). The importance of such reproducibility is now widely recognized, but it is still not as widely practiced as it should be, in large part because many computational scientists (and particularly statisticians) have not fully adopted the required tools for reproducible research.
In this course, we will discuss general principles for reproducible research but will focus primarily on the use of relevant tools (particularly Make, Git, and R Markdown), with the goal that the students leave the course ready and willing to ensure that all aspects of their computational research (software, data analyses, papers, presentations, posters) are reproducible.
Prerequisite: Some knowledge of R.
The source for this website is on GitHub.
Christopher Gandrud’s Reproducible Research with R and RStudio
Yihui Xie’s Dynamic Documents with R and knitr
By the end of the course, each student will have designed and completed a small project:
rmarkdown to make a vignette.testthat to include a unit test.R CMD check.