1 Introduction

BBS Course: Good Software Engineering Practice for R Packages

Daniel, Liming, Shuang, Joe

March 24, 2023

Disclaimer

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

Statistician (Ph.D. in 2013 on 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, chair of the ASA BIOP working group on Software Engineering (SWE WG)
feel free to connect at LinkedIn or Github

Master in Biostatistics from Fudan University
Statistical Programmer in Roche for 4 years, technical engineering lead for chevron team in NEST project
Member of ASA BIOP working group on Software Engineering (SWE WG) mmrm taskforce
Feel free to connect at Github

Ph.D. in Statistics
Postdoc at the University of Oxford for 6 years, Data Scientist at Roche for the last 3 years, technical engineering lead for the NEST SME team.
Multiple open-source packages on Github and CRAN, see this page for details.
Feel free to connect at LinkedIn or Github

Master in Pharmacology from Fudan University
5-year experience in clinical data management, curation and related R tools development
Technical Lead of several data FARIification R packages, co-auther of the Restrict Data Check Shiny App and the Raw Data Compare R Package
Feel free to connect at Github

09:00 - 09:30	Introduction and outline
09:30 - 10:30	R Package Syntax
10:30 - 10:45	Tea 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	Tea Break
15:00 - 15:45	Publication of R Packages
15:45 - 16:30	Shiny Development
16:30 - 17:00	Summary and Discussion

Course website at openpharma.github.io/workshop-r-swe
- all slides
- sources available at github.com/openpharma/workshop-r-swe
- all materials CC-BY 4.0
We have a WeChat channel for this course that you can connect to
- use to ask your peers questions
- will try to monitor during course

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

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

\(\leadsto\) R package

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, CRF 11)
- reusable
- shareable
- easier to document

Creators (initial authors): Daniel Sabanes Bove , Friedrich Pahlke , Kevin Kunzmann
In the current version, changes were done by (later authors): Liming Li , Joe Zhu , Shuang Li
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, which is forked from the original version at github.com/kkmann/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