| 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 |
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
openstatswareincluding Bayesian MMRM workstream - Feel free to connect
Ryan
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
House-keeping
- Course website at openpharma.github.io/workshop-r-swe-md
- all slides
- sources available at github.com/openpharma/workshop-r-swe-md
- all materials CC-BY 4.0
- We offer a gitter chat channel
- use to ask your peers questions
- will try to monitor during course
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
- Encapsulate behavior (functions)
- Avoid global state/variables
- Adopt consistent coding style
- Document well
- Add test cases
- Version your code
- 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
- Creators (initial authors): Daniel Sabanes Bove , Friedrich Pahlke , Kevin Kunzmann
- In the current version, changes were done by (later authors): Andrew Bean , Biju (Ryan) Wang
- This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
- The source files are hosted at github.com/openpharma/workshop-r-swe-md, which is forked from the original version at github.com/openpharma/workshop-r-swe.
- Important: To use this work you must provide the name of the creators (initial authors), a link to the material, a link to the license, and indicate if changes were made
1 Introduction openstatsware Course: Good Software Engineering Practice for R Packages Andrew, Ryan, Daniel September 26, 2023