1 Introduction

openstatsware Course: Good Software Engineering Practice for R Packages

Andrew, Ryan, Daniel

September 26, 2023

Disclaimer




Any opinions expressed in this presentation and on the following slides are solely those of the presenter and not necessarily those of their employers.

Andrew

  • Ph.D. in Statistics from Ohio State: nonparametric Bayes for heavy-tailed data
  • Statistician at Novartis for 6 years: first Early Development Oncology, now Advanced Exploratory Analytics
  • Open-source contributions include R package OncoBayes2 (Bayesian model-based dose escalation)
  • Member of openstatsware including Bayesian MMRM workstream
  • Feel free to connect

Ryan

  • Ph.D. in Statistics from University of Connecticut, graduated in 2021
  • Senior biostatistician at Johnson & Johnson since 2021
  • Member of openstatsware
  • Feel free to connect

Daniel

  • Ph.D. in Statistics from University of Zurich, Bayesian Model Selection
  • Biostatistician at Roche for 5 years, Data Scientist at Google for 2 years, Statistical Software Engineer at Roche for the last 3 years
  • Multiple R packages on CRAN and Bioconductor, co-wrote book on Likelihood and Bayesian Inference, co-chair of openstatsware
  • Feel free to connect

openstatsware

  • Since: 19 August 2022 - just celebrated our 1 year birthday!
  • Where: American Statistical Association (ASA) Biopharmaceutical Section (BIOP)
  • Who: Currently 38 statisticians from 28 organizations
  • Old name: ASA BIOP Software Engineering Working Group (SWE WG)
  • What: Engineer packages and spread best practices

What you will learn today

  • Understand the basic structure of an R package
  • Create your own R
  • Learn about & apply professional development workflow
  • Learn & apply fundamentals of quality control for R
  • Get crash-course in version control to stay organized
  • Try out modern collaboration techniques on GitHub.com
  • Learn how to make an R available to others
  • Get a starting point for sustainable Shiny app development

Program outline

09:00 - 09:30 Introduction and outline
09:30 - 10:30 R Package Syntax
10:30 - 10:45 Coffee Break
10:45 - 11:45 Software Engineering Workflow
11:45 - 12:45 Lunch Break
12:45 - 13:45 Package Quality
13:45 - 14:45 Collaboration via GitHub
14:45 - 15:00 Afternoon Break
15:00 - 15:45 Publication of R Packages
15:45 - 16:45 Shiny Development
16:45 - 17:00 Summary

House-keeping

What you will need

  • Github.com (free) account
  • Recommended: posit.cloud
    • Free tier sufficient
    • Comes with everything installed
    • Alternative: local R development environment with
      • git
      • Rtools/R/Rstudio IDE
  • Curiosity 🦝
  • Positive attitude 😄

Enter menti.com: 4813 9580

What do we mean by GSWEP4R*?

  • Applying concept of GxP to SWE with R
  • Improve quality of R code/packages, particularly in regulated enviroments but not limited to!
  • Not a universal standard; we share our perspectives
  • Collection of best practices
  • Do not reinvent the wheel: learn from IT/open source space

Why care about GSWEP4R?

  • Move to / integration of R in pharma is clear trend
  • R is a powerful yet complex ecosystem
    • Core component: R packages
    • Mature analysts: users & contributors
    • Deep understanding crucial, even to just assess quality
  • Analyses increasingly require complex scripts/programs
    \(\leadsto\) line between programming and data analysis blurs
  • Value: de-risking use of R and efficiency gains

Start small - from script to package

  1. Encapsulate behavior (functions)
  2. Avoid global state/variables
  3. Adopt consistent coding style
  4. Document well
  5. Add test cases
  6. Version your code
  7. Share as ‘bundle’

\(\leadsto\) R package

The R package ecosystem - huge success

GxP + R =

  • Core infrastructure packages only through industry
  • Quality, burden sharing: open-source pharmaverse and others
  • Open methodological packages can de-risk innovative methods
  • R packages make (statistical/methodological) code
    • testable (with documented evidence thereof, CFR Part 11)
    • reusable
    • shareable
    • easier to document

Question, Comments?

License information

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