Publications by Data Imaginist
Beneath the canvas
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 in generally well reasoned and I love how it moves from a curious observation t...
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Introducing reqres
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 that R users comes into contact with, indeed the most popular way of using R...
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When A Fire Starts to Burn – Fiery 1.0 released
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 of breaking changes. Below, I will go through the major points of the...
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I made a 3D movie with ggplot2 once – here’s how I did it
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 that created a stereoscopic pair of plots that would simulate 3D. To pro...
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Introducing routr – Routing of HTTP and WebSocket in R
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 developing GUI’s based on shiny for managing different parts...
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Announcing lime – Explaining the predictions of black-box models
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 two implementations share many ove...
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Moving to Blogdown
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, other more qualified people have already made great tutorials about the matter. A little ov...
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Setting fire to deployment: Heroku
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 one way of achieving the latter, namely by deploying a fiery app to Heroku. What’s Heroku anywa...
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Looking Back on 2017
Say what you will about 2017 – it sure made an impact. In the broader view I’m pretty sure all sensible people can agree that that impact was predominantly negative, but even so, small pockets of good can happen. So, here’s a short post about all (the predominantly good) that happened in my life in this year. As you can guess this post will...
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tidygraph 1.1 – A tidy hope
I am very pleased to tell you that the next version of tidygraph (1.1) is now available on CRAN. This is not a bug-fix release, nor a change-it-all release, but rather a more-of-it-all release, and in this post I’m going to tell you all about it. The idea of tidygraph Before we enter the goldmine of new features that makes this release I’m g...
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