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).
Read more about Speeding up your Continuous Integration Builds
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.
Read more about Recreating a Shiny App with Flask
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.
Read more about Setting the Graphics Device in a RMarkdown Document
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.
Read more about Saving R Graphics across OSs
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:
Read more about Mapping the Spread of COVID-19 with Python
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.
Read more about Faster R package installation
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