There are many ways to utilize fiery: It can be the engine behind a local running web-app, it can serve a dynamic site running on your own server, or it can serve a more demanding service by running in the cloud. In this post I’m going to focus on ...

As I announced last friday on Twitter I have moved Data Imaginist to Blogdown from Jekyll. This is going to be a short post about why I did it. There’s not going to be much tutorial about this; the migration is as painless as can be expected and besides, ...

I’m very pleased to announce that lime has been released on CRAN. lime is an implementation of the model prediction explanation framework described in “Why Should I Trust You?”: Explaining the Predictions of Any Classifier paper. The authors of the paper have developed a Python implementation and while the ...

routr is now available on CRAN, and I couldn’t be happier. It’s release marks the completion of an idea that stretches back longer than my attempts to bring network visualization and ggplot2 together (see this post for ref). While my PhD was still concerned with proteomics a began ...

Some time ago (last year actually ?) I had a blast developing a feature for ggforce which had been on my mind for far to long than its limited utility warranted. The idea was to showcase the new facetting extension powers I’d added to ggplot2 by making a facetting function ...

I’m pleased to announce that fiery has been updated to version 1.0 and is now available on CRAN. As the version bump suggests, this is a rather major update to the package, fixing and improving upon the framework based on my experience with it, as well as introducing a number ...

I’m very happy to announce that reqres has been released on CRAN. reqres is a new (in R context) approach to working with HTTP messages, that is, the requests you send to a server and the respons it returns. The uunderlying mechanics of a web server is seldom something ...

Recently a blog post made its rounds on the internet describing how it is possible to speed up plot creation in ggplot2 by first creating a blank canvas and then later adding the plot elements on top of it. The main takeaway plot is reproduced below: The blog post is ...