🔗What is Taxonomy? Taxonomy in its most general sense is the practice and science of classification. It can refer to many things. You may have heard or used the word taxonomy used to indicate any sort of classification of things, whether it be companies or widgets. Here, we’re talking about biological taxonomy, the science of defining and naming groups of biological organisms.

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.

In my training as a AAAS Community Engagement Fellow, I hear repeatedly about the value of extending a personal welcome to your community members. This seems intuitive, but recently I put this to the test. Let me tell you about my experience creating and maintaining a #welcome channel in a community Slack group.

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.

rOpenSci’s mission is to promote a culture of open, transparent, and reproducible research across various research domains. Everything we do, from developing high-quality open-source software for data science and, software review, to building community through events like our community calls and annual unconference are all geared toward lowering barriers to reproducible, open science.

We, Alicia Schep and MilesMcBain, drove the webrockets projectat #runconf17.To make progress we solicited code, advice, and entertaining anecdotesfrom a host of other attendees, whom we humbly thank for helping to makeour project possible. This post is divided into two sections: First up we’ll relate ourexperiences, prompted by some questions we wrote forone another.

We are pleased to welcome our Postdoctoral Fellow, Dr. Dan Sholler. Dan is an expert in qualitative research (yes, you read that correctly) and studies digital infrastructure creation, growth, and maintenance efforts. Through this research interest, he was drawn to the open science community and its ongoing development of tools and communities to support sustainable, reproducible, high-quality research.

Before everybody made their way to the unconf via LAX and Lyft, attendees discussed potential project ideas online. The packagemetrics package was our answer to two related issues. The first proposal centered on creating and formatting tables in a reproducible workflow.

What’s that? You’ve heard of R? You use R? You develop in R? You know someone else who’s mentioned R? Oh, you’re breathing? Well, in that case, welcome! Come join the R community! We recently had a group discussion at rOpenSci’s #runconf17 in Los Angeles, CA about the R community. I initially opened the issue on GitHub.

Excited to annonunce a new package called charlatan. While perusingpackages from other programming languages, I saw a neat Python librarycalled faker. charlatan is inspired from and ports many things from Python’shttps://github.com/joke2k/faker library. In turn, faker was inspired fromPHP’s faker,Perl’s Faker, andRuby’s faker. It appears that the PHPlibrary was the original - nice work PHP. 🔗Use cases What could you do with this package?

Two years ago at #runconf15, there was a great discussion about best practices for organizing R-based analysis projects that yielded a nice guidance document describing research compendia . Compendia, as we described them, were minimal products of reproducible research, using parts of R package structure to organize the inputs, analyses, and outputs of research projects.