r

We love supporting the community around the open source tools that we use on a daily basis. In the past, Jumping Rivers has helped useR user groups and SatRdays events to happen by enabling frictionless sponsorship for European groups. We believe that it is our duty to help grow the community that helps us. With that in mind, it is our honour to announce that we are proudly sponsoring a new season of events hosted by WhyR.

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 training courses. We’ve always ran some, online events, but now we’re running in excess of ten days of training per month on topics ranging from R, Python, Git, Shiny and Stan.

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, we have recruited new full-time staff which have furthermore contributed to the glowing success of the company.

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 identify vulnerabilities, understand risk, and keep their software safe. The {oysteR} package is an R interface to the OSS Index that allows users to scan their installed R packages.

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 we have encouraged remote working. Half of the team are remote (Leeds, Lancaster, Edinburgh). To make remote working a possibility, you need a) a good internet connection and b) within a few hours of (train) travel to London or Edinburgh.

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 using R, then the current most popular CI pipeline is Travis CI, but there’s also Jenkins, GitHub Actions, GitLab CI, Circle CI and a few others.

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 the fact that flask will soon be coming to RStudio products and wondered how easy it would be to recreate one of the simple shiny examples as a flask application.

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 to use grDevices::cairo_pdf() for saving PDF graphics or grDevices::png(..., type = "cairo_png") for PNGs or alternatively the new {ragg} package.

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 across different OSs. As R has excellent cross-platform capabilities, we may expect this to follow through to exporting graphics.

Faster package installation Every few weeks or so, a tweet pops up asking about how to speed up package installation in R Depending on the luck of twitter, the author may get a few suggestions. The bigger picture is that package installation time is starting to become more of an issue for a number of reasons. For example, packages are getting larger and more complex (tidyverse and friends), so installation just takes longer.