Coding best practices
There are code standards and conventions available depending on the language you are using and sometimes also conventions adopted for specific collaborative projects. These can be quite complex and out of scope if you are writing a code for your analysis, however there are a few things you can do to make your code much more readable and safer from bugs which are quite simple. In the video linked below, kindly provided by DataTAS, the presenter gives some useful tips which can be applied to any language:
"Reproducible research how to write code that is built to last"
It is worth watching the video (the actual presentation is about half of the video ~35 minutes) to understand fully how valuable these tips are and also to get a perspective from someone who went from a science background to a commercial software engineering position.
Below is a list of best practices discussed in the video.
Naming
Use descriptive names for variables and functions
Use consistent naming across the code
Avoid hard-coding values
Initialising variables
Code structure
Indents
Comments
Use functions to organise your code
Don't Repeat Yourself (DRY) code
One statement per line
Write explicit code
Keep your files a reasonable length
Clear flow: try to have only one exit point in a function
Use tests
Style guides
- Python: pep8 Python Enhancement Proposal
- Python reserved keywords
- Julia: style guide
- R style guide
- R reserved keywords