Importing Data with Python
Published: July 17, 2025
Importing data is a key step in the data science workflow. How you import a dataset has consequences for how you work with that data throughout a project.
Russ Hyde
Russ has previously worked in molecular biology and bioinformatics. He holds a PhD in Molecular Physiology and MSc in Mathematics. Russ is an author of several CRAN packages and mentor on the R-for-data-science community.
Advanced Testing in Python
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.
The Jumping Rivers Dashboard Gallery
Published: April 15, 2025
At Jumping Rivers we love data dashboards and are delighted to announce the release of a gallery to showcase our application-development skills.
What's new in R 4.5.0
Published: April 10, 2025
Here we summarise some of the more interesting changes that have been introduced in R 4.5.0.
Music in R
Published: December 19, 2024
Here, we’ll talk about music in R, what packages are available, how to represent musical notation, and what people are actually doing with music data in R.
Highlights from Shiny in Production (2024)
Published: October 17, 2024
The 9th and 10th of October saw the third Shiny in Production conference hosted by Jumping Rivers. Here we share the highlights from our fabulous talks and workshops.
What's new in R 4.4.0
Published: April 25, 2024
R 4.4.0 introduces some cool features (one of which is experimental) and makes one of our favourite {rlang} operators available in base R.
What's new in R 4.3.0
Published: April 20, 2023
R v4.3.0 is released on April 21st, 2023, and continuing a recent theme, R is getting stricter with your code. If you don't know your `&` from your `&&` you may be in for a bumpy ride. Here, we discuss the changes to the logical comparison operators and a few other new features.
UI-driven testing for Shiny: Part 3
Published: January 19, 2023
It pays to extract out some helper functions and / or classes from your test cases. By doing so, your tests will look simpler, the behaviour that you are testing will be more explicit, and you'll have less code to maintain.
UI-driven testing for Shiny: Part 2
Published: January 12, 2023
Here, we will write a simple shiny app (as an R package) and show how to generate tests for this app using {shinytest2}.
UI-driven testing for Shiny: Part 1
Published: January 5, 2023
Automated testing is an essential part of any production-quality software project. Much of the focus in the R world is on testing the individual components of a project, but for those working with {shiny} there are great tools that can test your application as if a user was interacting with it. In this blog series, we focus on {shinytest2}.
WHO/Europe: Offload Shiny's Workload
Published: June 21, 2022
The great strength of Shiny is that it simplifies the production of data-focused web applications, making it relatively easy to present data to users / clients in an interactive way. However, data can be big and data-processing can be complex, time-consuming and memory-hungry. In this post we demonstrate how we tackled this issue in a recent project.
New features in R 4.1.0
Published: May 17, 2021
A new R release (v 4.1.0) is due for release on 18th May 2021. This version brings in a few exciting features, such as anonymous functions and the native pipe. Here, we summarise these and other notable changes.
