Posts

Let’s use a built-in example within R: location of earthquakes off the island of Fiji.

Rmarkdown was a revelation to me when I was first introduced to it in SEAMS (now Arcadis Gen). I’d used Jupyter notebooks before for Python and loved the live lab notebook feel of them. Rmarkdown for R is like this but more polished, more final, more suited to a corporate or public end user. It also has a few tricks up its sleeve.

Ever since a week before lockdown in mid-March, I’ve been holed up in my conservatory working from home. The wild swings in temperature have provided ample motivation to build a temperature probe and live dashboard to track patterns, open windows in good time or cope with the lead time that my pitiful electric heater requires.

I’ve been working with git for quite a while now. I’ve been happily working with git for almost as long. After some in-person training and referring to Happy Git for R, things finally clicked when I found a rhythm to all these strange commands. The git flow I found has helped me not only with what git command I want to use (is it a push? is it a merge?), but also when and in what order to do so.

This post is a draft of an article I wrote on the arcadisgen.com website…

DVLA is not yet on CRAN. To install, simply run the following in R.

When I began learning about how to use Docker I stumbled on an excellent project called Rocker. For anyone with an x86 machine these Rocker images allow them to run R and most of its dependencies in a containerised environment. Plumber APIs, anyone? What about your own Shiny server? Finally, data scientists using R can have the same level of control on dependencies and package versions as Python users have become accustomed to through venv.