I never really thought I would write an R package. I use R pretty casually. Then, this year, I was invited to participate during the last week of the Analytical Paleobiology short course, an intensive month-long experience in quantitative paleontology. I was thrilled to be invited. But I got a slight sinking feeling in my stomach when I realized all the materials were in R. And so I, a Pythonista, decided I would spend some of my maternity leave writing R packages to try to blend in with students who had spent the month living and breathing R.

spatsoc is an R package written by Alec Robitaille, Quinn Webber and Eric Vander Wal of the Wildlife Evolutionary Ecology Lab (WEEL) at Memorial University of Newfoundland. It is the lab’s first R package and was recently accepted through the rOpenSci onboarding process with a big thanks to reviewers Priscilla Minotti and Filipe Teixeira, and editor Lincoln Mullen. spatsoc started as a single function (what would eventually become group_pts) written by Alec in 2017 to help answer some of the questions that Quinn and Eric were asking about how animal social structure is related to spatial processes.

What is restez? R packages for interacting with the National Center for Biotechnology Information (NCBI) have, to-date, depended on API query calls via NCBI’s Entrez. For computational analyses that require the automated look-up of reams of biological sequence data, piecemeal querying via bandwith-limited requests is evidently not ideal. These queries are not only slow, but they depend on network connections and the remote server’s consistent behaviour. Additionally, users who make very large requests over extended periods of time run the risk of being blocked.

A few months ago, I wasn’t sure what to expect when looking at fluorescence microscopy images in published papers. I looked at the accompanying graph to understand the data or the point the authors were trying to make. Often, the graph represents one or more measures of the so-called co-localization, but I couldn’t figure out how to interpret them. It turned out; reading the images is simple. Cells are simultaneously stained by two dyes (say, red and green) for two different proteins.

Every R package has its story. Some packages are written by experts, some by novices. Some are developed quickly, others were long in the making. This is the story of jstor, a package which I developed during my time as a student of sociology, working in a research project on the scientific elite within sociology. Writing the package has taught me many things (more on that later) and it is deeply gratifying to see, that others find the package useful.