Creating dynamic reports using R Markdown

R Markdown is a formatting syntax that makes it easy to create dynamic reports. It’s based on the popular Markdown lightweight markup language for web writing. Markdown Markdown is a response to the growing importance of writing for on-line publication. Tools based around the concept of a printed page (e.g. Word, Google Docs) make little […]
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Building Data-driven Web Applications Using R and Shiny

Shiny is a web application framework for the R statistical computing language. It allows interactive applications to be written entirely in R. While it’s possible to write a general purpose web application using R and Shiny, it’s not the sweet spot of the framework. There are better general purpose web application frameworks—such as ASP.NET MVC. […]
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Version control for data scientists using Git and RStudio

Look at the working directory of the average data science project and you’ll see things like this: cust-churn.csv cust-churn.R cust-churn-old.csv cust-churn-progess-meeting.R cust-churn-working.R cust-churn-working2.R cust-churn-20160217.R test.R test-bk.R Every time a change needs to be made, files are copied to preserve the working code. As changes will often be made to multiple files, it’s common to […]
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SQL Server 2016: R Integration Redux

SQL Server 2016 has now reached Release Candidate 3 (RC3). One of the new features that continues to foster interest in the analytics and data mining community is the integration of SQL Server with the open-source statistics toolset R. If you fall into this category, I’m inclined to suggest you remain patient and wait for […]
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Introducing Microsoft R Server

Earlier this year Microsoft released Microsoft R Server. This is essentially a rebranding of Revolution R Enterprise—acquired through Microsoft’s acquisition of Revolution Analytics in April 2015. However, the fact that Microsoft is backing the product makes a big difference to many potential corporate users. And with Microsoft embracing R across the company, more investment in […]
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