Analytics with R Programming


Analytics 2.0 problems are fundamentally different. Specifically, they have three characteristics that Analytics 1.0 problems don’t have: relying heavily on statistical methods, operating on big data, and requiring seamless integration with web technologies.

R is designed for statistical computing, with thousands of packages that contain the implementations of almost every available statistical methods, making it meet the first characteristic perfectly. In fact, it’s perhaps the only tool that does so in such a comprehensive fashion.

Think of R packages as Lego pieces. When a problem requires a different method, just plug in a different R package. It’s that simple. In addition, R can handle more data points than Excel by default. You can even write R code in a parallel fashion and use inexpensive commodity computers in the cloud to analyze large datasets, like Amazon Web Services (AWS) instances. Finally, you can easily integrate R with web technologies to make analytic web apps.

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