Russian Fake Tweets Visualized

With the fervor of the Presidential election being skewed by Russian probing’s as well as the notorious Facebook / Cambridge Analytics scandal still topping daily domestic headlines, it became clear to us that “fake news” and Russian users are still prevalent, yet vague concepts. Who are these fake users disguising as? How are these fake users fooling people? How are they influencing people? Therefore, with our backgrounds in natural language processing, data visualization, and interest in the combination of technology and politics, it was only natural to examine the fake Russian usersTweet data with Python and Plotly.

Microsoft R Open 3.4.4 now available

An update to Microsoft R Open (MRO) is now available for download on Windows, Mac and Linux. This release upgrades the R language engine to version 3.4.4, which addresses some minor issues with timezone detection and some edge cases in some statistics functions. As a maintenance release, it’s backwards-compatible with scripts and packages from the prior release of MRO.

How To: LEGO mosaics from photos using R & the tidyverse

Using R and the tidyverse, we can turn a photo into a 48 x 48 brick LEGO set. We’ll use official LEGO colors and optimize the number of bricks we use to keep the price low.

Interpretable Machine Learning with iml and mlr

Machine learning models repeatedly outperform interpretable, parametric models like the linear regression model. The gains in performance have a price: The models operate as black boxes which are not interpretable. Fortunately, there are many methods that can make machine learning models interpretable.

Comparing dependencies of popular machine learning packages with `pkgnet`

When looking through the CRAN list of packages, I stumbled upon this little gem: ‘pkgnet is an R library designed for the analysis of R libraries! The goal of the package is to build a graph representation of a package and its dependencies.’ And I thought it would be fun to play around with it. The little analysis I ended up doing was to compare dependencies of popular machine learning packages.