Eli Lilly

Reproducible computation at scale in R with targets

Data science can be slow. A single round of statistical computation can take several minutes, hours, or even days to complete. The targets R package keeps results up to date and reproducible while minimizing the number of expensive tasks that …

Targets and Reproducible Pipelines

Data science can be slow. A single round of statistical computation can take several minutes, hours, or even days to complete. The targets R package keeps results up to date and reproducible while minimizing the number of expensive tasks that …

Creating and reviving Shiny apps with golem

Developing Shiny applications that meet design goals, easily deploy to multiple platforms, and contain easily maintainable components (all while adhering to best practices) is typically a difficult endeavor. Until recently, there has not been a tool …

Machine learning workflow management with drake

Machine learning workflows can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, …

Machine learning workflow management with drake

Developing powerful Shiny applications in an enterprise environment: Best practices and lessons learned

Recent advances in the Shiny ecosystem boost the scale and scope of serious enterprise-wide web applications. More specifically, it is entirely possible to utilize key features of Shiny Server Professional and additional R packages such as shinyjs, …

The drake R package: reproducible data analysis at scale

The drake package is a general-purpose workflow manager for data-driven tasks in R, with applications in the pharmaceutical industry ranging from tailored medicine to clinical trial simulation and beyond. Drake rebuilds intermediate data objects when …