At their closing keynote at the 2020 RStudio Conference, Hilary Parker and Roger Peng mentioned that they hatched the idea for their excellent Not So Standard Deviations podcast following their reunion at the 2015 rOpenSci unconf, (“runconf15”). That statement went straight to my heart because it pin-pointed how I had been feeling throughout the week of RStudio Conference that I had been unable to name.

It was the best of times, it was the worst of times. Dickens might have meant it figuratively, but in the case of the rOpenSci OzUnconf 2019, we mean it literally. Set to the backdrop of a national emergency that is still ongoing from 11-13 December, our participants came from across Australia as well as New Zealand, Japan, India and Indonesia.

In late November 2018, we ran the third annual rOpenSci ozunconf. This is the sibling rOpenSci unconference, held in Australia. We ran the first ozunconf in Brisbane in 2016, and the second in Melbourne in 2017. Photos taken by Ajay from Fotoholics As usual, before the unconf, we started discussion on GitHub issue threads,and the excitement was building with the number of issues.

While many people groan at the thought of participating in a group ice breaker activity, we’ve gotten consistent feedback from people who have been to recent rOpenSci unconferences. We’ve had lots of requests for a detailed description of how we do it. This post shares our recipe, including a script you can adapt, a reflection on its success, examples of how others have used it, and some tips to remember.

Motivation A few weeks ago, as part of the rOpenSci Unconference, a group of us (Sean Hughes, Malisa Smith, Angela Li, Ju Kim, and Ted Laderas) decided to work on making the UMAP algorithm accessible within R. UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique that allows the user to reduce high dimensional data (multiple columns) into a smaller number of columns for visualization purposes (github,

tl;dr : we propose three calls to action:Share your curricular materials in the open.Participate in the rOpenSci Education profile series.Discuss with us how you want to be involved in rOpenSci Educators’ Collaborative.

In the first post of this series, we sketched out some of the common challenges faced by educators who teach with R across scientific domains. In this post, we delve into what makes a “good” educational resource for teaching science with R. For instructors teaching sciences with R, there are a number of open educational resources that they can reuse, tailor to their own teaching style, or use to inspire them in creating their own materials.

Educators who teach science using R tend to face common pedagogical problems, regardless of their scientific domain. Yet instructors who teach with R often feel isolated at their institutions. They may be the only ones in their departments to teach using R. Even if there are others, the culture of collaboration around teaching is generally impoverished, unlike the rich culture of collaboration around research.

R packages are widely used in science, yet the code behind them often does not come under scrutiny. To address this lack, rOpenSci has been a pioneer in developing a peer review process for R packages. The goal of pkginspector is to help that process by providing a means to better understand the internal structure of R packages.

It’s easy to come to a conference and feel intimidated by the wealth of knowledge and expertise of other attendees. As Ellen Ullman, a software engineer and writer describes, One of the best ways to start feeling less intimidated is to start talking to others. Ullman continues, At rOpenSci unconf18, we learned that it’s ok to feel like you don’t know everything – indeed, that’s how just about everyone feels!