For the last few years we’ve offered automatic sponsorship for meet-ups and satRday events. However for obvious COVID related reasons, most (all?) meet-ups have meeting getting together virtually, so the need for extra Pizza money has diminished. As with most organisations, we’ve had to adapted to the new online-first environment. In particular, running primarily online

Don’t we all miss 2019 (blame Covid for the long delay in this post). The days of going to work and seeing your work colleagues face to face - and for some of you, attending one of our on-site training courses! 2019 was a great year for us. Not only have we broken new boundaries,

One of our main roles at Jumping Rivers is to set-up and provide ongoing maintenance to R, Python and RStudio infrastructure. This typically involves ensuring software is up-to-date and making sure everything is running smoothly. The OSS Index developed by Sonatype is a free catalogue of open source components and scanning tools to help developers

Jumping Rivers is a data science consultancy company focused on R and Python. We work across industries and throughout the world. We offer a mixture of training, modelling, and infrastructure support. Jumping Rivers is an RStudio Full Service Certified Partner. Location: We are based in Newcastle upon Tyne. However, since the creation of the company

Continuous integration is an amazing tool when developing R packages. We push a change to the server, and a process is spawned that checks we haven’t done something silly. It protects us from ourselves! However this process can become slow, as typically the CI process starts with a blank virtual machine (VM). If you are

So RStudio Connect has embraced Python and now runs Flask applications! At Jumping Rivers we make a lot of use of R, shiny, and Python for creating visual tools for our clients. Shiny has a lot of nice features, in particular it is very fast for prototyping web applications. Over our morning meeting we discussed

In our recent post about saving R graphics, it became obvious that achieving consistent graphics across platforms or even saving the “correct” graph on a particular OS was challenging. Getting consistent fonts across platforms often failed, and for the default PNG device under Windows, anti-aliasing was also an issue. The conclusion of the post was

R is known for it’s amazing graphics. Not only ggplot2, but also plotly, and the other dozens of packages at the graphics task view. There seems to be a graph for every scenario. However once you’ve created your figure, how do you export it? This post compares standard methods for exporting R plots as PNGs/PDFs

Written by Aoife Curran. The purpose of this post isn't to add new insight into the spread of the coronavirus - there are plenty of experts out there more qualified. Instead, our goal is to highlight how to construct simple, interactive visualisations using live data such as: Getting the tools Folium is a Python library