R for Data Science

Chapter 24. R Markdown Workflow

Earlier, we discussed a basic workflow for capturing your R code where you work interactively in the console, then capture what works in the script editor. R Markdown brings together the console and the script editor, blurring the lines between interactive exploration and long-term code capture. You can rapidly iterate within a chunk, editing and re-executing with Cmd/Ctrl-Shift-Enter. When you’re happy, you move on and start a new chunk.

R Markdown is also important because it so tightly integrates prose and code. This makes it a great analysis notebook because it lets you develop code and record your thoughts. An analysis notebook shares many of the same goals as a classic lab notebook in the physical sciences. It:

• Records what you did and why you did it. Regardless of how great your memory is, if you don’t record what you do, there will come a time when you have forgotten important details. Write them down so you don’t forget!

• Supports rigorous thinking. You are more likely to come up with a strong analysis if you record your thoughts as you go, and continue to reflect on them. This also saves you time when you eventually write up your analysis to share with others.

• Helps others understand your work. It is rare to do data analysis by yourself, and you’ll often be working as part of a team. A lab notebook helps you share not only what you’ve done, but why you did it with your colleagues or lab mates.

Much of the good advice about ...