I recently released my first R and Python packages. This post contains some thoughts and advice about releasing software packages – particularly for other graduate students.

The question of “should you release a package?” is highly context dependent (e.g. if you are a probabilist the answer is probably no). There are a number of trade-offs to consider. For example, academia does not seem to value software very much. More importantly, there is a large time cost cost to develop software packages that could have been spent writing papers, this includes:

• Coding the basic functionality
• Turning your code into a package someone else can download and use
• Documentation for the code
• Providing data analysis examples
• Maintaining and updating the package
• Responding to user feedback
• Surveying the existing literature to make sure your package provides new functionality

I think academia is starting to value software more than it used to¹. I would argue that, in many cases, releasing code is as important as writing a paper. Some of the benefits to you that come from releasing a software package include:

• Save future you time. Better code new = less headache in the future.
• Fame/glory/prestige for people using your work.
• Help other people solve their problems. If part of your rational for doing research/academia is helping to solve problems then good code might be as (or more) impactful as a paper.
• Software skills are highly valued in industry.
• You might learn new things out of necessity (e.g. computational linear algebra) and/or better understand your own research.

Resources

Programming is typically a small part of the statistics curriculum (and most other scientific disciplines); we don’t think of ourselves as software engineers even though many of us spend a lot of time writing code. Luckily there are many quality, open-source resources that show you how to write better code and release software. Without these resources (particularly the R Packages book) it would have taken me 1-2 orders of magnitude more time to build these packages².

These resources are helpful for creating R/Python packages:

• Hadley Wickham’s R Packages book and devtools were incredibly helpful. If you plan on building an R package read this book.
• This tutorial on a minimal Python package and coockiecutter give helpful templates and instructions to create a Python package.
• Tim Hopper’s talk on releasing code gives a good high level overview of how/why to release code.
• Hosting the package on github gives you a lot of functionality for free (e.g. users can submit feedback via github issues)

These resources helped me become a better programmer:

• Good Enough Practices in Scientific Computing
• Some principles of good programming
• Jeff Leek’s book on How to be a Modern Scientist and an uncountable number of simplystatistics posts.
• Unit testing made the packages a lot less buggy (testthat for R and unittest for Python).
• Reading/borrowing from existing, quality bases including. I found the following helpful:
  • R: ggplot2, tidytext.
  • Python: sklearn, lightning.

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