Testing Packages

Reproducible and Trustworthy Workflows for Data Science

Welcome
Introduction
- 1 How do reproducible and trustworthy workflows impact data science?
- 2 Introduction to the Bash Shell
Version Control with Git
Collaborating on GitHub
Project Organization
- 14 Filenames and data science project organization, Integrated development environments
Virtual Environments
Containers
Data Validation
Scripts and Reports
- 29 Non-interactive scripts
- 30 Reproducible reports: Quarto
Pipelines
- 31 Data analysis pipelines with scripts
- 32 Data analysis pipelines with GNU Make
Software Testing for Data Science
Packaging
Testing Packages
- 45 Package testing: Python (pytest)
- 46 Package coverage: Python
Continuous Integration
Package Documentation
Continuous Deployment and Publishing
Licenses and Copyright
- 62 Copyright and licenses
Wrap Up
- 63 Workflows for reproducible and trustworthy data science wrap-up
Appendix
Archive

Testing Packages

Once our functions live in a package, we test the package itself and measure how much of its code our tests reach.

44 Packaging: R and Python tool philosophies

45 Package testing: Python (pytest)