Unconf 2017: The Roads Not Taken

  Noam Ross   | AUGUST 8, 2017

Since June, we have been highlighting the many projects that emerged from this year’s rOpenSci Unconf. These projects start many weeks before unconf participants gather in-person. Each year, we ask participants to propose and discuss project ideas ahead of time in a GitHub repo. This serves to get creative juices flowing as well as help people get to know each other a bit through discussion. This year wasn’t just our biggest unconf ever, it was the biggest in terms of proposed ideas!

emldown - From machine readable EML metadata to a pretty documentation website

  MaĆ«lle Salmon   |   Andrew MacDonald   |   Kara Woo   |   Carl Boettiger   |   Jeff Hollister   | AUGUST 1, 2017

How do you get the maximum value out of a dataset? Data is most valuable when it can easily be shared, understood, and used by others. This requires some form of metadata that describes the data. While metadata can take many forms, the most useful metadata is that which follows a standardized specification. The Ecological Metadata Language (EML) is an example of such a specification originally developed for ecological datasets.

notary - Signing & Verification of R Packages

  Rich FitzJohn   |   Oliver Keyes   |   Stephanie Locke   |   Jeroen Ooms   |   Bob Rudis   | JULY 25, 2017

Most of us who work in R just want to Get Stuff Done™. We want a minimum amount of friction between ourselves and the data we need to wrangle, analyze, and visualize. We’re focused on solving a problem or gaining insights into a new area of research. We rely on a rich, community-driven ecosystem of packages to help get our work done and likely make an unconscious assumption that there is a safety net out there, protecting us from harm.

skimr for useful and tidy summary statistics

  Eduardo Arino de la Rubia   |   Shannon Ellis   |   Julia Stewart Lowndes   |   Hope McLeod   |   Amelia McNamara   |   Michael Quinn   |   Elin Waring   |   Hao Zhu   | JULY 11, 2017

Like every R user who uses summary statistics (so, everyone), our team has to rely on some combination of summary functions beyond summary() and str(). But we found them all lacking in some way because they can be generic, they don’t always provide easy-to-operate-on data structures, and they are not pipeable. What we wanted was a frictionless approach for quickly skimming useful and tidy summary statistics as part of a pipeline.

Launching webrockets at runconf17

  Alicia Schep   |   Miles McBain   | JULY 5, 2017

We, Alicia Schep and Miles McBain, drove the webrockets project at #runconf17. To make progress we solicited code, advice, and entertaining anecdotes from a host of other attendees, whom we humbly thank for helping to make our project possible. This post is divided into two sections: First up we’ll relate our experiences, prompted by some questions we wrote for one another. Second, we’ll put the webrockets package into context and walk you through a fun example where you can live plot streaming sensor data from a mobile device.

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