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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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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