File management and projects in R

or, How to keep your computer safe from fire

There’s a famous blog post about workflows in R1 about a talk Jenny Bryan gave that included this slide:

If the first line of your R script is

setwd("C:\Users\jenny\path\that\only\I\have")

I will come into your office and SET YOUR COMPUTER ON FIRE 🔥.

If the first line of your R script is

rm(list = ls())

I will come into your office and SET YOUR COMPUTER ON FIRE 🔥.

I am definitely guilty of this too!

From my first year of learning R:

Ask an AI to write you a script

…and there’s a good chance it has some of these bad habits.

  • These models learned from a decade of blog posts and Stack Overflow answers that open exactly this way

Note

Unless you have given it a full project context (e.g., full directory permission with Claude Code), an AI has no idea what your file structure looks like and how everything should fit together.

Instead: project-oriented workflow

  • R projects provide a structured and organized way to work on projects in R
  • R projects encapsulate all project-related files and settings into a single directory
  • RStudio makes it easy to work with R projects

Note

Posit, the company behind RStudio, has a new IDE called Positron that is designed to work with a project structure. The approach is slightly different than RStudio, but the principles are the same.

R Projects (and related tools) can prevent a lot of accidents!

R Projects

my-project/
├─ my-project.Rproj
├─ README.md
├─ data/
│   ├─ raw/
│   └─ processed/
├─ R/
├─ results/
│   ├─ tables/
│   ├─ figures/
│   └─ output/
└─ docs/

  • An .Rproj file is mostly just a placeholder text file that lives in your project folder

    • It remembers various options, and makes it easy to open a new RStudio session that starts up in the correct working directory. You never need to edit it directly.

  • Otherwise your project acts just like any other folder on your computer

  • You can essentially turn any folder on your computer into an R project, or make a new one via RStudio when you create an R project

Benefits of R Projects

  1. Isolation: Each project has its own workspace, separate from other projects
  2. Reproducibility: Projects ensure that code and data are self-contained and portable
  3. Collaboration: Projects facilitate collaboration by sharing the entire project directory

Always open a project by opening the .Rproj file

You can have multiple projects open at once in different RStudio sessions!

You can also switch between R projects from RStudio

  • Clicking the arrow icon will open it up in a new session and keep your current session open
  • Opening an R project will also open all the files you had open last time (including unsaved “Untitled” files!)

Creating an R Project

  1. Open RStudio and go to File > New Project, or click on the projects button in the upper-right corner of RStudio.
  2. Choose a project location (New Directory, Version Control, Existing Directory).
  3. Specify the project directory (where on your computer you are storing the folder with the project) and create the project.
  4. Choose the project type (e.g., regular project, R package, Shiny app, Quarto website, Bookdown book)

You already have an R project!

In the exercises, we are going to make some more changes to the repo you forked and cloned

  1. Download an .R script and a .csv file from the website
  • We’ll be using some data from the 1979 National Longitudinal Survey of Youth
  1. Find your epi590r-in-class repo in your file browser
  • Create an R folder and a data folder
  • Within the data folder add a raw and a clean folder.
  • Put the .csv file in the data/raw folder and the script in R folder.

File structure goal

epi590r-in-class/
├─ epi590r-in-class.Rproj
├─ README.md
├─ R/
│   └─ clean-data-bad.R
├─ data/
│   ├─ raw/
│   │  └─ nlsy.csv
│   └─ clean/

Exercises, cont.

  1. Return to RStudio. If you closed RStudio, make sure you re-open this project. Look to the filepane to confirm the files are there.

  2. Stage, commit, and push the changes you’ve made.

  3. Try to run the code, line-by-line, in clean-data-bad.R.

  • You will get errors!
  • As you’re running it, try to think of changes you might make

Stop for a settings change!

  1. Tell RStudio to start fresh whenever you start a new session
  1. Close RStudio, then open it up again by opening the epi590r-in-class.Rproj file in your file browser

Exercise: work with files in your R project