Your Guide to Quality Data Science Products

Delve into the benefits of Nextflow in computational biology workflow management, its integration with RShiny, and its significance in bioinformatics.

Discover how functional programming boosts reproducibility, simplifies testing, and delivers value in pharmaceutical research and data-driven industries.

Want to train highly-accurate deep learning models in R? Look no further than FastAI in R and follow this article to train a model.

Posit and Databricks have joined forces to revolutionize data science, explore what this means for the R community, data scientists and data engineers.

Unveil the capabilities of Shiny Express, a new way of writing Shiny apps that combines the robustness of Shiny with the flexibility of Streamlit.

Everybody uses dplyr for their data processing pipelines - but is it the fastest option? Read our overview of R data processing frameworks.

The pharma and life sciences dilemma: Stick with SAS or move to R? Explore key insights in our detailed review.

Maximize your Shiny app's performance with our guide on memory benchmarking and optimization.

Uncover the capabilities of Teal, Roche's open-source pharma framework with insights from Paweł Rucki on its growth, user dynamics, and industry impact.

Discover the best R/Shiny library for your data visualisation needs. Compare Rhino, Golem, and Leprechaun in our in-depth analysis to find the right fit for your project.

Your Shiny app is amazing, but can others run it? This R Shiny Docker guide will make sure your apps are reproducible and portable.

Discover how to use the new reactable.extras release to create stunning Shiny data tables with custom inputs, server-side processing, and more.