How many comments do you have in your code? Cleaning up nasty code to make easier to follow (CC044)

If you read code you wrote a year ago, what are the odds that you will be able to descipher what you were trying to do? In today's episode Pat demonstrates how he desciphers old smelly code and talks about commenting practices that can help to avoid these problems. How many comments should an R script contain? What makes for a good variable or function name? Watch today's episode to find out. This episode is part of a larger arc of episodes investigating the sensitivity and specificity of amplicon sequence variants (ASVs), also known as exact sequence variants (ESVs). ASVs are growing in popularity for analyzing microbial communities using 16S rRNA gene sequences. Pat demonstrates these concepts by live coding at the command line interface using RStudio, GitHub Flow, and make. 0:00 Introduction 4:05 Getting set up 9:30 Commenting a block of code 31:31 Rendering the markdown document 33:36 Closing the issue 35:15 Conclusion The accompanying blog post contains the exercises and solutions can be found at http://www.riffomonas.org/code_club/2020-11-02-commenting-code