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.

A new rOpenSci package provides access to data to which users may already have directly contributed, and for which contribution is fun, keeps you fit, and helps make the world a better place. The data come from using public bicycle hire schemes, and the package is called bikedata.

KO: What is your name, job title, and how long have you been using R? DS: My name is David Smith. I work at Microsoft and my self-imposed title is ‘R Community Lead’. I’ve been working with R specifically for about 10 years, but I’d been working with S since the early 90s. KO: How did you transition into using R? DS: I was using S for a long, long time, and I worked for the company that commercialized S, where I was a project manager for S-PLUS.

This week we released version 3.0 of the curl R package to CRAN. You may have never used this package directly, but curl provides the foundation for most HTTP infrastructure in R, including httr, rvest, and all packages that build on it. If R packages need to go online, chances are traffic is going via curl.