Events | A list of R conferences and meetings.

In this blog I’ll share real-world lessons from deploying Posit Workbench on Azure Kubernetes Service (AKS), including practical debugging tips, common pitfalls, and the importance of looking beyond the cluster when issues arise.

Creating clarity with your data

For quite some time, AI had kept it's grubby little hands out of the music production world. Now, a good percentage of the plugins (a plugin is a piece of software you can "plug in" to an audio track to add effects or generate audio) I see are advertised as "using AI". From reverb removers (yes, that's right, you can now remove the reverb from an audio recording), to EQ analysers. Today we'll focus on stem separation.

Shiny in Production Conference is fast approaching and we wouldn't be able to put it on without the support of our sponsors!

In this blog we are creating an animated map of the gapminder data using {ggplot2} and {gganimate}. In the process we will cover some of the common pitfalls when working with spatial data and how to get round them!

This is part two of our two part series Part 1: The importance of web accessibility standards Part 2: Accessibility in R applications: {shiny} (this post) Web applications that are Web Content Accessibility Guidelines (WCAG) compliant are becoming an increasingly prominent part of my role as a data scientist as the importance of ensuring that data products are available to all takes a more central focus. This is particularly true in the case of building solutions for public sector organisations in the UK as they are under a legal obligation to meet certain accessibility requirements.

Do you use R? Would you like to play a part in sustaining it? Find out about the R Dev Day that is returning as a satellite event to Shiny in Production 2025. This post will answer questions you may have, such as: “Do I need to be an R guru to participate?”, “What will I be expected to do?”, and “Is there a cost to attend?”. Hopefully by the end, you’ll be motivated to sign up!

Jupyter notebooks are a popular tool for data scientists using Python. They allow us to mix together plain text (formatted as Markdown) with Python code. In this post, we will show you how to produce reproducible PDF and HTML reports from a Jupyter notebook using Quarto.

Importing data is a key step in the data science workflow. Here we compare data import for two key Python data-frame libraries - Polars and Pandas.

As data scientists we often need to communicate conclusions drawn from data. Additionally, as more data is collected, our reports invariably need updating. This is where automated reporting tools such as Quarto come in! In this blog post we will look at how Quarto allows us to weave together text and Python code to generate reproducible reports. What is Quarto? Quarto is a technical publishing system built on Pandoc. By combining code with plain text, it allows you to create reports that can easily be updated when the data changes. For example, imagine you have to report on the profits of a company each month. With Quarto, you can create your report with any key figures and charts, then with just the click of a button update it each month with new data. You can also create content in a variety of formats, from articles and scientific papers to websites and presentations, in HTML, PDF, MS Word and more.

How often do bugs get fixed? Does the package use source control? Is the package a solo or a group effort? These questions aid our understanding about the long-term viability of a package, and how "risky" it is.

Events | A list of R conferences and meetings.

Code quality is what typically comes to mind when talking about "good packages". Does that package pass standard checks? What is the Unit test coverage? How many dependencies does the package have? This post discusses how we use code quality when determining the package litmus score.

Python package managers are essential tools that help developers install, manage, and update external libraries or packages used in Python projects. These packages can contain reusable code, modules, and functions developed by other programmers, making it easier for developers to build applications without reinventing the wheel.

Are you ready to expand your knowledge in R, Python, Shiny, and Posit while becoming a more valuable asset to your team? Jumping Rivers is here to help you do just that with our free monthly webinar series designed for data professionals at all levels. These 55-minute sessions are easy to join online and packed with practical insights to help you sharpen your skills, tackle real-world challenges, and stay ahead in the fast-evolving data landscape. Whether you’re looking to improve your coding, learn best practices for deploying apps, or dive into machine learning, there’s something here for you.