Leave the code cleaner than you found it.
– R.C. Martin in Clean Code
The R language has become very popular among scientists and analysts because it enables the rapid development of software and empowers scientific investigation. However, regardless of the language used, data analysis is usually complicated. Because of various project complexities and time constraints, analytical software often reflects these challenges. “What did I measure? What analyses are relevant to the study? Do I need to transform the data? What’s the function for the analysis I want to run?” Although many researchers see the value in learning to write software, the time investment for learning a programming language alone is still exceedingly high for many, let alone also learning software best practices. The downside to the rapid spread of data science is that learning to create good software takes a back-seat to just writing code that will get the job “done” leading to issues with transparency and software that is highly unstable (i.e. buggy and not reproducible).
This is a companion discussion topic for the original entry at https://ropensci.org/blog/2020/04/21/rclean/