At Jumping Rivers we’re all about getting involved in the R community! As such, we host multiple events throughout the year. Read on for information about what we have planned so far for 2023! Conferences SatRdays London 2023 In April 2023 we will be hosting SatRdays at Bush House, London. SatRdays are low cost, not for profit events aimed to attract those who ordinarily wouldn’t be able to attend pricier events, or who can’t usually make it during the week.
If you’re thinking of picking up a new skill in the new year, take a look at our upcoming public training courses! We have plenty of introductory courses coming up, both online and in-person, so you can hit the ground running after the holidays! Whether you want to start from scratch, or improve your skills, Jumping Rivers has a training course for you. Introduction to R Course Level: Foundation
This week, we’ve been reminding ourselves of some of the amazing talks from the Shiny in Production conference in October. The recordings are now up on our YouTube channel, for anyone to view! For a run down of the day and what you can expect from the videos, take a look at our recent Highlights blog. Do you require help building a Shiny app? Would you like someone to take over the maintenance burden?
Data science and data engineering are incredibly cognitively demanding professions. As data professionals, we are required to leverage both our analytical/engineering skills and our interpersonal skills to be effective contributors within our organisations. Based on my personal experience, the field seems to concentrate humans who are detail-oriented, curious, impact-driven and tenacious to a fault. This A-type personality profile, while magical when applied to technical work, could reasonably also count as an occupational hazard.
It has been 6 months since the launch of Diffify, our website for comparing package releases. We are delighted to announce that, in addition to CRAN’s 20,000 R packages, you can now track 1600 popular Python packages! What’s included? The current criteria for a Python package to be included in Diffify are: The package is listed in the top 2000 PyPI packages according to download statistics. The package has had version releases since 1st May 2020.
Matplotlib is one of the longest standing and most comprehensive plotting libraries for Python. It is mostly used for creating static plots and its flexible customisation options make it a great choice for creating publication quality graphs. In this blog post we will look at formatting and colourmap customisation in Matplotlib, and how to set a consistent plotting style throughout a project. Note: If you wish to run the code snippets in this blog yourself you will need:
Introduction As someone who has zero experience using Shiny in R, the recent announcement that the framework had been made available to Python users inspired an opportunity for me to learn a new concept from a different perspective to most of my colleagues. I have been tasked with writing a Python related blog post, and having spent the past few weeks carrying out an analysis of Jumping Rivers’ Twitter data (@jumping_uk), creating a dashboard to display some of my findings and then writing about it seemed like a nice way to cap off my 6-week summer placement at Jumping Rivers.
Here at Jumping Rivers we like to keep our courses up to date so we can bring you training on the latest tools and technologies. To this end, we have recently added two new courses to our listing! Whether you want to start from scratch, or improve your skills, Jumping Rivers has a training course for you. Reporting with Quarto Do you create interactive documents that always need to be updated when the data changes?
Refactoring Russian Doll Code Recently, I’ve been working with an environmental scientist to refactor a large R package. Let’s call her Jane. Jane inherited a mess of code, and had to get it working as quickly as possible. She tidied up it as best as she could in the time, but now that the company depended on it, it needed some attention. We referred to it as her “Russian Doll code” because it had many nested functions, each passing the same giant nested lists back and forth.
Introduction This is part the final part of our three part series Part 1: API as a package: Structure Part 2: API as a package: Logging Part 3: API as a package: Testing (this post) This blog post is a follow on to our API as a package series, which looks to expand on the topic of testing {plumber} API applications within the package structure leveraging {testthat}. As a reminder of the situation, so far we have an R package that defines functions that will be used as endpoints in a {plumber} API application.