Building Communities Together at ozunconf, 2017

Nicholas Tierney OCTOBER 31, 2017

Just last week we organised the 2nd rOpenSci ozunconference, the sibling rOpenSci unconference, held in Australia. Last year it was held in Brisbane, this time around, the ozunconf was hosted in Melbourne, from October 27-27, 2017. At the ozunconf, we brought together 45 R-software users and developers, scientists, and open data enthusiasts from academia, industry, government, and non-profits. Participants travelled from far and wide, with people coming from 6 cities around Australia, 2 cities in New Zealand, and one city in the USA.

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

Maƫlle 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.

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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