Insight. Applied.
R Tools for Visual Studio is an open-source plug-in that makes Visual Studio an IDE for R development. It looks very similar to the excellent RStudio. If you are already using Visual Studio it seems like it’ll be an obvious choice for R development.
It’s not available yet, but you can sign up for early access.
As spreadsheet abuse has now been “Dilbertized” it’s clearly entered mainstream consciousness.
About time. We’ve been complaining about it for years.
Jan Mulkens recently published an article on Microsoft’s recent rush to enhance it’s data science offering. And, as he illustrates, they have been very busy this year.
He highlights a number of their flagship data science initiatives.
Other significant data science activities at Microsoft this year, in my opinion, include:
I’m looking forward to what they come up with in 2016.
Twitter have an interest in detecting anomalies in their service. Anomalies could be down to user engagement, spamming or technical issues. Regardless of the reasons, it’s something they want to know about when it happens.
To aid detection of anomalies in their time series data they have developed, and open-sourced, an anomaly detection package for R. Their algorithm is based on the Generalized ESD Test and can detect both global and local anomalies. The package is also capable of detecting anomalies when seasonal and trend factors are present in the time series data.
It’s that time of year when we start getting the “best x of 2015″ posts. Nathan Yau of FlowingData just published his list of the best visualization projects. Yau reckons that this was the year of using visualization to teach about data and statistics.
My favorite is “Science Isn’t Broken” by Christie Aschwanden of FiveThirtyEight. It’s a visual interactive demonstration of how you can shape the results of a study through p-hacking (using US political parties and the economy as the example). The choices you make as to variables, data, methods, etc. have a significant impact on the outcomes of an analysis. Quantification and statistical significance are no guarantee of quality.