With the rapid adoption of R in the pharmaceutical industry, the topic of R package validation has become a focal point of discussion. Ensuring that these packages perform in a reproducible and reliable manner is not just important, but essential for maintaining the integrity of data analysis and regulatory compliance. Across the industry, numerous professionals have shared their methodologies for validating open-source R packages, each contributing valuable insights into best practices and common challenges. Despite the wealth of shared knowledge, one area remains relatively unexplored: the validation of R packages within Docker images. Docker, a platform that enables the creation and deployment of applications in isolated containers, offers a powerful tool for ensuring consistency across different computing environments. By encapsulating R packages and their dependencies within Docker images, we can achieve a high level of reproducibility and portability. This talk aims to delve into this niche yet significant topic and provide a guide to tailoring your validation process to leverage Docker for R package validation, enhancing the reproducibility and reliability of computational results in the pharmaceutical industry.
Nicole Jones
Denali
R Package Validation Framework: Docker Integration
With the rapid adoption of R in the pharmaceutical industry, the topic of R package validation has become a focal point of discussion. Ensuring that these packages perform in a reproducible and reliable manner is not just important, but essential for maintaining the integrity of data analysis and regulatory compliance. Across the industry, numerous professionals have shared their methodologies for validating open-source R packages, each contributing valuable insights into best practices and common challenges. Despite the wealth of shared knowledge, one area remains relatively unexplored the validation of R packages within Docker images. Docker, a platform that enables the creation and deployment of applications in isolated containers, offers a powerful tool for ensuring consistency across different computing environments. By encapsulating R packages and their dependencies within Docker images, we can achieve a high level of reproducibility and portability. This talk aims to delve into this niche yet significant topic and provide a guide to tailoring your validation process to leverage Docker for R package validation, enhancing the reproducibility and reliability of computational results in the pharmaceutical industry.