R is a great language to use interactively; however, that does mean many users don't get experience of using it as a language in which to do programming—that is, for automating analyses and saving the user's time and efforts when it comes to repeating stuff. In this chapter, we'll take a look at some techniques for doing just that—in particular, we'll look at how to integrate base R objects into tidyverse workflows, extend Bioconductor classes to suit our own needs, and use literate programming and notebook-style coding to keep expressive and readable records of our work.

The following recipes will be covered in this chapter:

• Making base R objects tidy
• Using nested dataframes
• Writing functions for use in mutate
• Working programmatically with Bioconductor classes
• Developing reusable workflows and reports
• Making use of the apply family of functions