Highlights from Shiny In Production (2025)
Published: November 3, 2025.
This October, Jumping Rivers hosted the fourth installment of our conference "Shiny In Production". Here we summarise the talks and workshops that were presented.
Published: November 3, 2025.
This October, Jumping Rivers hosted the fourth installment of our conference "Shiny In Production". Here we summarise the talks and workshops that were presented.
Published: September 25, 2025.
One of our main projects at Jumping Rivers in the last year has been building the Litmusverse, a platform for validation. An important component when assessing the quality of code within a package is the unit tests. In this blog we discuss the main features of the `{testthat}` package, as a very convenient way for testing R code.
Published: August 5, 2025.
Shiny in Production Conference is fast approaching and we wouldn't be able to put it on without the support of our sponsors!
Published: June 24, 2025.
We are pleased to announce the lightning talks for this year's Shiny in Production conference! In this blog post, we've pulled together all of the talk abstracts to give you a full view of what to expect!
Published: June 17, 2025.
We are pleased to announce the full length talks for this year's Shiny in Production conference! In this blog post, we've pulled together all of the talk abstracts to give you a full view of what to expect!
Published: May 8, 2025.
Writing tests is one of the best ways to keep your Python code reliable and reproducible. This post builds on our previous blog about Python testing with pytest, and explores some of the more advanced features it offers. We will show how to make your tests more reproducible, easier to manage and demonstrate how writing simple tests can save you time in the long run.
Published: January 23, 2025.
The fourth instalment of Shiny in Production is back this October, hosted at the Catalyst in Newcastle upon Tyne, with super early bird tickets deadline for Shiny in Production ends on the 31st of January.
Published: September 5, 2024.
Programming is a craft, and in data science we often spend countless hours coding. Software testing can improve the quality of the code you write as a data scientist. Here, we introduce the pytest framework and show how it can be used to test Python functions.