Interactive Visualizations from R using rCharts

At useR! 2014, one of the most anticipated presentations was Joe Cheng’s Shiny: R made interactive. It was but one of the of a fantastic series of talks by RStudio representatives; check out Winston Chang’s ggvis: Interactive graphics in R for another great talk. Shiny begins with your R code and ends with the customer’s view: an interactive, browser-accessible application. This enables you to place a great deal of power into your users’ hands to customize their view and provides a variety of new and innovative ways to interact with the data, such as by tuning parameters and focusing on sub-populations. Essentially, Shiny functions by allowing you to “wire up” up a reactive application, greatly speeding up the development of a data and analysis based web app.

Hadley Wickham is hard at work, releasing packages which leverage the expressive power of R to keep easy things intuitive, and to make hard things possible. Having already developed a number of packages which address other steps in the data processing and analysis pipeline, he chose to address the process by which R developers ingest, manipulate, and transform data. This package, dplyr, is a narrowly focused library, designed to provide 90% of the functionality required in most data munging tasks. It may not solve every possible problem in data munging, but dplyr’s built in primitives will reduce your need to drop to “base R” for most common tasks. It does so by leveraging the magritr package’s “pipe” operator, and is inspired by dataflow programming constructs. It is fast, it is powerful, and it has been the single largest productivity boost that many of us here at DataScience.LA have adopted in 2014. In this video, Hadley provides a very quick introduction to the dplyr package, describing his philosophy behind the package’s design while providing a number of illustrative examples as to the flexibility, expressiveness, and power of this tool.

There comes a time in a software toolchain’s lifecycle where the focus shifts from developer productivity and preference to the ability to deploy solutions in production which coexist gracefully in the enterprise. Configuration management and dependency management are two of the first points of friction in this transition, as the free-wheeling methods preferred by developers are at odds with the structured methods and isolation desired by administrators, who serve as gatekeepers to enterprise deployment. R is currently at this very point in the transition, in the midst of transformation from a language and environment used primarily in academic research to its growing place in the enterprise. At useR! 2014 there were a series of presentations on how dependency management can be addressed within the R ecosystem, ranging from “provider” side options (e.g. freezing CRAN and private repositories) to the client side options, where a project maintains its own dependencies as part of its codebase.