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.
Published: October 1, 2026.
Python 3.15 is released in October 2026. The imports system has introduced lazy loading, and code profiling has been made simpler. Here we summarise these and some other changes introduced in Python 3.15.
Published: July 30, 2026.
In this blog post we outline some reasons why it is valuable to learn how to use the command line as a data scientist.
Published: June 25, 2026.
Jumping Rivers recently hosted the conference "AI In Production 2026" where experts across software engineering and data science discussed how to actually use AI in software products and data projects. The talks also touched on security, regulatory matters, AI usage across different sectors and how AI has impacted the process of software development. Here we summarise the conference talks. "AI In Production 2027" will be held 10-11 June 2027.
Published: April 16, 2026.
Here we summarise some of the more interesting changes in the April 2026 release of R 4.6.0.
Published: March 19, 2026.
Teams may need a way to reliably generate datasets and data products from newly-collected raw data that is robust to the evolution of both the team and the raw data. This is where 'Reproducible Analytical Pipelines' (RAPs) can help.
Published: November 6, 2025.
Here we compare the syntax of two Python libraries, Pandas and Polars for standard data-manipulation tasks.
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: October 16, 2025.
Python 3.14 was released in October. Here we summarise some of the more interesting changes and some trends in Python development and data-science over the past year.
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.
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: 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.
Published: April 10, 2025.
Here we summarise some of the more interesting changes that have been introduced in R 4.5.0.
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.
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.
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.
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.
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.
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}.
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}.
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.
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.
