Conducting sentiment analysis using R

I’ve been asked on numerous occasions how to conduct sentiment analyses in R. There used to be a sentiment package, but it’s now only available via the archive. An alternative approach (and one that works with languages other than R) is to use a sentiment analysis web service. There are numerous services to choose from. […]
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How to Display Data on a World Map in R

One of the strengths of R is its vast ecosystem of libraries. This includes numerous sophisticated visualization libraries—some of which, such as the excellent ggplot, are capable of producing publication quality charts. I’m not a huge fan of charts (e.g. scatterplots, barcharts) as communication devices. I believe that conclusions can usually be presented more succinctly. […]
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Five R Packages Data Scientists Should Know About

One of the strengths of R is its comprehensive ecosystem of packages. If you want to do something, chances are that someone’s been there first and written the package. Of course, with so many packages out there, quality varies considerably—and some packages are so specific that it’s difficult to imagine them having a wide audience. […]
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How Big is Big Data?

One of the questions I’m often asked is “How big does my data have to be before I need to start using big data tooling?” There’s no right answer to this—but that’s largely because it’s not the right question. Big Data is actually a pretty unhelpful term. It focuses attention exclusively on the volume of […]
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Using a Pipeline Operator in R

I find myself programming more and more in a functional style these days. Obviously, R, F# and Scala encourage it—but I’m a heavy user of LINQ in C# and my JavaScript has been going that way for a while too. When programming in languages other than F#, I yearn for the pipeline operator (|>). The […]
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