This last chapter, which was uncharacteristically light on theory, may be one of the most important chapters in the whole book. In order to be a productive data analyst using R, you simply must be acquainted with the tools and workflows of professional R programmers.
The first topic we touched on was the link between best practices and reproducibility, and why reproducibility is an integral part of a productive and sane analyst's workflow. Next, we discussed the basics of R scripting, and how to run completed scripts all at once. We saw how RStudio - R's best IDE - can help us while we write these scripts by providing a mechanism to execute code, line-by-line, as we write it. To really cement your understanding of R scripting, we saw an example R script that illustrated clean design and adherence to best practices (informative variable names, readable layout, myriad informative comments, and so on).
Then, you learned of a few ways that you can organize multi-file analysis projects...