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.

Evolutionary biologists are increasingly using R for building,editing and visualizing phylogenetic trees.The reproducible code-based workflow and comprehensive array of toolsavailable in packages such as ape,phangorn andphytools make R an ideal platform forphylogenetic analysis.Yet the many different tree formats are not well integrated,as pointed out in a recentpost.

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!

Data == knowledge! Much of the data we use, whether it be fromgovernment repositories, social media, GitHub, or e-commerce sites comesfrom public-facing APIs. The quantity of data available is trulystaggering, but munging JSON output into a format that is easilyanalyzable in R is an equally staggering undertaking.

You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research. Where’s rOpenSci?

Introduction I never thought that I’d be programming software in my career. I startedusing R a little over 2 years now and it’s been one of the most importantdecisions in my career. Secluded in a small academic office with no oneto discuss/interact about my new hobby, I started searching the web fortutorials and packages. After getting to know how amazing and nurturingthe R community is, it made me want to become a data scientist.

For the fourth and last day of project recaps from this year’s unconf, here is an overview of the next five projects. (Full set of project recaps: recap 1, recap 2, recap 3, recap 4.) In the spirit of exploration and experimentation at rOpenSci unconferences, these projects are not necessarily finished products or in scope for rOpenSci packages.

KO: What is your name, job title, and how long have you been using R? JS: My name is Julia Silge and I’m a data scientist at Stack Overflow. I have been working in R for less than three years. KO: Wow! What were you all about before that?