Within the life sciences industry, Shiny has enabled tremendous innovations to produce web interfaces as frontends to sophisticated analyses, dynamic visualizations, and automation of clinical reporting across drug development. While industry …
Since its first release over eight years ago, the R community has progressively created amazing web-based applications with the Shiny package. In practically every R conference or user meetup, we see amazing examples of how Shiny is changing the …
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 …
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 …
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 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, …
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, …