R Coding Practices

This article lays out some best practices for coding using R. Though it is possible to use R without it, the RStudio integrated development environment makes its use easier and is the standard among R users. There is not a single set of best practices and the guidelines below are suggestions that can and should be adapted the each project's needs, as well as users' preferences

Read First

• RStudio
• Comments
• Objects names

Package installation

R packages are collections of functions, data, and documentation that extend the functionality of base R. They are essential for everything from data cleaning to statistical modeling and visualization.

Installing CRAN Packages

CRAN (The Comprehensive R Archive Network) is the primary repository for R packages.

Here's an example to install a package from CRAN:

install.packages("tidyverse")

To install multiple packages:

install.packages(c("tidyverse", "haven", "data.table"))

To load the packages installed:

library(tidyverse)

Comments and script structure

Running code that returns the right result is only the first half of the job. The other half is making sure your code is easy to follow, test, and reuse. This helps teams catch mistakes, audit decisions, and collaborate more effectively. Poorly structured code increases the risk of errors.

1. Use header comments to introduce each script:

##################################################
# Script: 01_clean_data.R
# Purpose: Clean raw baseline data
# Author: First Last
# Date: 2025-05-30
# Inputs: data/raw/baseline.csv
# Outputs: data/clean/baseline_clean.rds
##################################################

2. Use section headers to structure code within each script

# Load packages -------------------------------------------------------

# Import data ---------------------------------------------------------

# Clean variables -----------------------------------------------------

# Save outputs --------------------------------------------------------

Good syntax makes it easy to understand what the code is doing and why. You should:

• Use clear, expressive names for variables and objects (e.g., baseline_data instead of bd).
• Avoid deeply nested code and one-liners that sacrifice clarity.
• Write logic in small chunks—long chains of operations should be broken down or commented carefully.

Additional Resources

• R-bloggers post on best practices
• DIME Analytics, World Bank shiny training

Related DIME Analytics Trainings

• Training session recording on Introduction to R Shiny
• Training session recording on Big data workflows with R data.table