Working together to push science forward
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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. In this three-part series of blog posts, participants at the rOpenSci 2018 unconf briefly survey the state of teaching science with R....
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 offers tools to analyze and visualize the relationship among functions within a package, and to report whether or not functions’ interfaces are consistent....
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,1 I was aware at all times that I had only islands of knowledge separated by darkness; that I was surrounded by chasms of not-knowing, into one of which I was certain to fall. One of the best ways to start feeling less intimidated is to start talking to others....
Data == knowledge! Much of the data we use, whether it be from government repositories, social media, GitHub, or e-commerce sites comes from public-facing APIs. The quantity of data available is truly staggering, but munging JSON output into a format that is easily analyzable in R is an equally staggering undertaking. When JSON is turned into an R object, it usually becomes a deeply nested list riddled with missing values that is difficult to untangle into a tidy format....
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