Thing 3: Economy
Related FAIR Principles: R1
When curating a reproducible file bundle, consider any extraneous parts that can be cut to make the overall bundle simpler to streamline computational reproduction.
Economizing everything means fewer research objects can break while also requiring less care and maintenance over time.
Simplifying and commenting out code are a couple of methods for tackling economization; however, the approach may vary depending on the type of software or methods being used for analysis. During file review and/or curation, here are some questions to consider:
- Can the scripts be simplified by removing redundancies or using loops and functions, for example?
- Are code blocks ordered logically according to the presentation of the results in the publication?
- Is there a master script that groups together all the other scripts? Are there additional scripts outside the master script and if so, are they necessary?
- Are the dependencies to the scripts or code all necessary?
- Are there comments in the code to help understand the computational workflow?
- Are there notebooks?
- Literate programming. (n.d.). Retrieved December 15, 2021, from http://www.literateprogramming.com/
A collection of best practices and guidance for programming, documentation, and code commenting.
- Gillespie, C., & Lovelace, R. (n.d.). Efficient R programming. Retrieved December 15, 2021, from https://csgillespie.github.io/efficientR/
This book covers not only programmer efficiency, but also computational efficiency to write more effective and streamlined code using R.
- Martin, R. C. (Ed.). (2009). Clean code: A handbook of agile software craftsmanship. Prentice Hall. https://enos.itcollege.ee/~jpoial/oop/naited/Clean%20Code.pdf
A guide on writing clean and concise code covering topics such as good vs. bad commenting, slow code, and formatting.
- Gentzkow, Matthew and Jesse M. Shapiro. (2014). Code and data for the social sciences: A practitioner’s guide. University of Chicago. https://faculty.chicagobooth.edu/matthew.gentzkow/research/CodeAndData.pdf, last updated January 2014.
Chapter 6, in particular, discusses the three rules of abstraction when writing code to eliminate redundancy and improve readability of the final product.
As mentioned previously, the approach to economizing code is dependent upon many factors. Here are resources specific to a few disciplines that highlight some best practices:
- Benureau, Fabien C.Y., and Rougier, Nicolas P. (2018). Re-run, repeat, reproduce, reuse, replicate: Transforming code into scientific contributions. Frontiers in Neuroinformatics, 11. https://doi.org/10.3389/fninf.2017.00069.
- Palomino, Jenny, Wasser, Leah, and Joseph, Max. (2021). Earth Lab. Earth Data Analytics. Intro to Earth Data Science. Section 7 - Write Efficient and Clean Code Using Open Source Python. https://www.earthdatascience.org/courses/intro-to-earth-data-science/write-efficient-python-code/
- Battig, W. F. (1962). Parsimony in psychology. Psychological Reports, 11(2), 555–572. https://doi.org/10.2466/pr0.1922.214.171.1245
There are also tools available to assist with cleaning code. For example:
- ROpenSci. (n.d.). A tool for writing cleaner, more transparent code. Retrieved December 15, 2021, from https://docs.ropensci.org/Rclean/