Part 2 of the dplyr tutorial is now available! Hadley will be covering Grouped Mutate/Filter, Joins, Do, and Databases. (You can find Part 1 of the tutorial here.)
For our viewers who have just discovered the wonders of R, keep in mind that this tutorial was filmed in early July of 2014. As R packages naturally update over time (well, depending on the programmers!), please make sure to check Hadley’s Githubfor the latest changes to the dplyr package.
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.
At useR! 2014 Ramnath Vaidyanathan gave a tutorial and a presentation on one of his current project, rCharts. This R package allows you to create, customize and share interactive visualizations straight from R. rCharts leverages several existing javascript visualization libraries such as polychart, MorrisJS, NVD3, xCharts, HighCharts, leaflet, and Rickshaw, providing R users a fantastic array of new features and visualization styles.
Martin Maechler is a member of R-Core. This distinction puts him in the very apex of understanding of the R programming language and environment. He is a professor of statistics with wide and varied interests and a leading expert in statistical computing environments. He is also a prolific programmer, evidenced by his maintenance of the officially recommended cluster package for unsupervised clustering and analytics, the lead developer of Emacs Speaks Statistics, and co-maintainer of the Matrix package… plus too many other contributions to list them all here.
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.
Matt Dowle holds a wealth of knowledge and is worth listening to for a variety of reasons. He has worked for some of the world’s largest financial organizations, holding positions at the focal point between money and data. He has been programming in R for well over a decade. He has lived the open source mantra, by not only consuming open source software but also releasing his own community contributions to CRAN. On top of those accomplishments, he’s also quite funny and spins a heck of a good yarn.
Winston Chang’s “Interactive Graphics with ggvis” talk at useR! 2014 was in many ways a watershed moment for the the data science community, as the future directions of rich data visualization for analytics, communication, and the web were revealed. In this talk, Winston introduces the viewer to ggvis, lays out the fundamental architecture, and provides an overview of reactive architectures.
At useR! 2014, John Chambers was generous enough to provide us with insight into the very early stages of user-centric interactive data exploration. He explains, step by step, how his insight to provide an interface into algorithms, putting the user first has placed us on the fruitful path which analysts, statisticians, and data scientists enjoy to this day. In his talk, John Chambers also does a fantastic job of highlighting a number of active projects, new and vibrant in the R ecosystem, which are helping to continue this legacy of “a software interface into the best algorithms.” The future is bright, and new and dynamic ideas are building off these thoughtful, well measured, solid foundations of the past.
