CodingBestPractices

Coding Best Practices

This class is about developing better coding practices, ranging from structuring code to writing code to debugging code.

Required readings (before class)

• Notes: tex/CodingBestPractices.pdf
• Google's R Style Guide
• Tidyverse Style Guide

To do (before class)

• Make sure you have a development environment that can run R code (e.g. R; RStudio; VS Code).
• Especially if using VS Code, test that it can run R scripts and produce a plot

Class structure

• Matters arising + adapt agenda (15 min)
• Coding exercise as a group (40 min.)
• Break (10 min.)
• Group discussion - assess your current coding practices and goals (~20 min.)
• Pick an issue and (re)start your project's best-practice conventions (remaining time)

Further readings

General best practices

• Wilson et al. 2014 Best practices for scientific computing
• Wilson et al. 2017 Good enough practices for scientific computing
• Balaban et al. 2021 Ten simple rules for quick and dirty scientific computing
• Filazzola et al. 2022 A call for clean code to effectively communicate science

Filenames

• File Naming Guidelines - Minnesota Historical Society

Mathematica

• Mathematica programming practice resources
• Common new user pitfalls

Writing functions in R

• Grolemund & Wikham - Writing functions (r4ds-19)

R Code style

• R-bloggers' best practices

Debugging in R

• Wikham's Advanced R - Debugging tools
• Wikham's Advanced R - Conditions

Workflow

• Ross 2017. Declutter your R workflow with tidy tools
• Browman's Minimal Make

Data

• Browman & Woo 2018. Data Organization in Spreadsheets
• Nowogrodzki 2020. Eleven tips for working with large datasets
• Perkel 2019. Ways to avoid data-storage disasters
• White et al. 2013 Nine simple ways to make it easier to (re)use your data