I’ve raved about the value of extending a personalized welcome to new community members and I recently shared six tips for running a successful hackathon-flavoured unconference. Building on these, I’d like to share the specific approach and (free!) tools I used to help prepare new rOpenSci community members to be productive at our unconference.

rOpenSci is pleased to announce a new collaboration with the Methods in Ecology and Evolution (MEE), a journal of the British Ecological Society, published by Wiley press ¹ . Publications destined for MEE that include the development of a scientific R package will now have the option of a joint review process whereby the R package is reviewed by rOpenSci, followed by fast-tracked review of the manuscript by MEE.

Are you new to version control and always running into trouble with Git?Or are you a seasoned user, haunted by the traumas of learning Git and reliving them whilst trying to teach it to others?Yeah, us too. Git is a version control tool designed for software development, and it is extraordinarily powerful.

The second rOpenSci OzUnConf was held in Melbourne Australia a few weeks ago. A diverse range of scientists, developers and general good-eggs came together to make some R-magic happen and also learn a lot along the way. Before the conference began, a huge stack of projects were suggested on the unconf GitHub repo. For six data-visualisation enthusiasts, one issue in particular caught their eye, and the ochRe package was born.

In May 2017, I helped run a wildly successful “unconference” that had a huge positive impact on the community I serve.

This year’s rOpenSci ozunconf was held in Melbourne, bringing together over 45 R enthusiasts from around the country and beyond. As is customary, ideas for projects were discussed in GitHub Issues (41 of them by the time the unconf rolled around!) and there was no shortage of enthusiasm, interesting concepts, and varied experience.

KO: What is your name, job title, and how long have you been using R? [Note: This interview took place in May 2017. Mara joined RStudio as their tidyverse developer advocate in November 2017.] MA: My name is Mara Averick, I do consulting, data science, I just say “data nerd at large” because I’ve seen those Venn diagrams and I’m definitely not a data scientist. I used R in high school for fantasy basketball.

Nearly 4 years ago I wrote on this blog about an R package solr for working with the database Solr. Since then we’ve created a refresh of that package in the solrium package. Since solrium first hit CRAN about two years ago, users have raised a number of issues that required breaking changes. Thus, this blog post is about a major version bump in solrium. 🔗What is Solr?

This week magick 1.5 appeared on CRAN. The latest update adds support for using images in knitr documents and shiny apps.

Release 1.4 of the magick package introducesa new feature called image convolution thatwas requested by Thomas L. Pedersen. In this post we explain what this is all about. 🔗Kernel Matrix The new image_convolve() function applies a kernel over the image. Kernel convolution means that each pixel value is recalculated using the weighted neighborhood sum defined in the kernel matrix.

Just last week we organised the 2nd rOpenSci ozunconference, the sibling rOpenSci unconference, held in Australia. Last year it was held in Brisbane, this time around, the ozunconf was hosted in Melbourne, from October 26-27, 2017. At the ozunconf, we brought together 45 R-software users and developers, scientists, and open data enthusiasts from academia, industry, government, and non-profits.