The 5 Essential Habits of Reproducible R Code
The 5 Essential Habits of Reproducible R Code, you know how frustrating it can be to work on a project only to have others struggle to replicate your results.
Whether you’re collaborating with colleagues or sharing your work with the world, reproducibility is key.
The 5 Essential Habits of Reproducible R Code
In this article, we’ll explore the 5 essential habits of reproducible R code, ensuring that your work is transparent, easy to share, and can be replicated by others.
Habit 1: Organize Your Work with Project-Oriented Workflows
Before you start coding, it’s essential to set up a solid project structure.
In R, this means using RStudio Projects, which keeps all your files in one directory.
A well-organized project folder should include:
data/: Raw and cleaned dataR/: R scripts or functionsoutput/: Plots, tables, and resultsreports/: R Markdown reports or presentationsREADME.md: Project overview.Rproj: RStudio project file
By using RStudio Projects, your project paths become relative, making your code portable across different machines.
For example, use the here package to refer to project files like so:
library(here)
data <- read.csv(here("data", "dataset.csv"))Habit 2: Document Your Workflow
Clear documentation is the foundation of reproducible code.
Use comments throughout your code to explain what each section does.
When you create functions, document their purpose, inputs, and outputs using the roxygen2 package:
#' Calculate the mean of a numeric vector
#' @param x A numeric vector
#' @return Mean of the vector
mean_calc <- function(x) {
mean(x, na.rm = TRUE)
}Don’t forget to include a README.md file in your project, explaining the project’s purpose, data used, and how to run the code.
This will help others understand your work quickly and set up the environment for running the code.
Habit 3: Use Version Control
Version control is essential for keeping track of changes in your code and collaborating with others.
Git is the most widely used version control system, and it integrates seamlessly with R.
Use Git to commit changes regularly with clear messages about what each change does.
For example:
git add analysis.R # Add the file you changed
git commit -m "Cleaned data and added summary stats" # Save the change with a short messageVersion control also makes it easy to revert to previous versions of your code if something goes wrong.
Habit 4: Set a Consistent Environment
Setting up a consistent environment ensures your code runs the same way on any computer. In R, you can use the renv package to manage your project’s environment.
It tracks the versions of the packages you use and helps others set up the same environment.
To set up renv, run:
install.packages("renv")
renv::init() # Initialize a new environmentThis will create a renv.lock file that lists the exact versions of the packages being used.
Anyone else working on the project can restore the same environment by running:
renv::restore() # Restore the environmentHabit 5: Set a Seed for Randomness
When your code involves random processes, such as random sampling or model initialization, the results can change each time you run it.
To avoid this, set a seed for the random number generator using set.seed() in R:
set.seed(123) # Set the seed
random_sample <- sample(1:100, 10) # Randomly sample 10 numbersBy following these 5 essential habits of reproducible R code, you’ll make your work more reliable, easier to share, and more accessible to others.
Whether you’re working on a personal project or collaborating with colleagues, these habits will save you time, reduce errors, and ensure the integrity of your work in the long term.
