R for Data Science

Chapter 6. Workflow: Projects

One day you will need to quit R, go do something else, and return to your analysis the next day. One day you will be working on multiple analyses simultaneously that all use R and you want to keep them separate. One day you will need to bring data from the outside world into R and send numerical results and figures from R back out into the world. To handle these real-life situations, you need to make two decisions:

1. What about your analysis is “real,” i.e., what will you save as your lasting record of what happened?

2. Where does your analysis “live”?

What Is Real?

As a beginning R user, it’s OK to consider your environment (i.e., the objects listed in the environment pane) “real.” However, in the long run, you’ll be much better off if you consider your R scripts as “real.”

With your R scripts (and your data files), you can re-create the environment. It’s much harder to re-create your R scripts from your environment! You’ll either have to retype a lot of code from memory (making mistakes all the way) or you’ll have to carefully mine your R history.

To foster this behavior, I highly recommend that you instruct RStudio not to preserve your workspace between sessions:

This will cause you some short-term pain, because now when you restart RStudio it will not remember the results of the code that you ran last time. But this ...