Querying SQL Server Data from R Using RODBC

In the previous blog, we looked at some of the annoyances encountered when installing RODBC on Linux. Of course, RODBC can also be used in R running on Windows. In either case, running queries using RODBC is straightforward and without surprises. As is always the case, the first thing we need to do is connect. […]
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Creating a Web Service in R

R is a powerful language and environment for statistical computing. It excels at crunching data and performing complex modeling. In application development, its strength is in creating the “back end” components. Shiny Server allows R developers to create web applications using R. It provides R functions to create pretty sophisticated user interfaces and link them […]
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Why You Must Manage Packages in R—and How to Do It

Reproducibility is a critical component of data science. In fact, it’s critical to all research—witness the reproducibility crisis currently engulfing academic research. It equally important in software development. We all expect our applications to work the same way…time after time. Software that operates erratically is undesirable. The unyielding regularly with which computers perform tasks could […]
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Machine Learning using Spark and R

R is ubiquitous in the data science community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory caching) makes it ideal […]
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Tackling a new dataset

With all the excitement (hype?) surrounding machine learning, there’s a tendency for decision makers (and many analysts) to believe that insights are just waiting to spring forth from the data when they are finally released by these magical algorithms. When it’s time to sit down and actually work with a real dataset, disillusionment quickly sets […]
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