As part of our work documenting R-Universe,we’re adding screenshots of the interface to the documentation website.Taking screenshots manually could quickly become very cumbersome, especially as we expect they’ll need updating in future: we might want to change the universes we feature, the interface might improve yet again and therefore look slightly different.Therefore, we decided to opt for a programmatic approach.In this post we shall present

It has been a while since we posted an update about magick, but behind the scenes we are constantly tweaking and improving this package, which has become a very mature and complete toolkit for image processing in R. Over the past year, we did 6 CRAN releases, containing many small features and fixes, but perhaps more importantly, the package is getting betting better due to updates of the underlying ImageMagick library.

rOpenSci HQ The recording, collaborative notes and links to community discussion and resources are up for our latest Community Call on Maintaining an R package. Panel moderated by Julia Silge included Elin Waring, Erin Grand, Leonardo Collado-Torres, and Scott Chamberlain.   Software Peer Review 1 staff-contributed package passed software peer review. parzer - Parse messy geographic coordinates.

rOpenSci HQ rOpenSci Announces a New $896k Award From The Gordon and Betty Moore Foundation to Improve the Scientific Package Ecosystem for R. We’re excited to announce a new member of our team! Introducing Mark Padgham, rOpenSci’s new Software Research Scientist NumFOCUS recognizes Melina Vidoni and Will Landau for their contributions to rOpenSci.

Image processing is one of the core focus areas of rOpenSci. Over the last few months we have released several major upgrades to core packages in our imaging suite, including magick, tesseract, and av. This post highlights a few cool new features.

In this new post, we’re taking a break from modern birding data in ourbirder’s series… let’s exploregorgeous drawings from a natural history collection!

This post is the 1st post of a series showcasing various rOpenScipackages as if Maëlle were a birder trying to make the most of R ingeneral and rOpenSci in particular. Although the series use cases willmostly feature birds , it’ll be the occasion to highlight rOpenSci’spackages that are more widely applicable, so read on no matter what yourfield is! Moreoever, each post should stand on its own.

This week magick 1.6 appeared on CRAN. This release is a big all-round maintenance update with lots of tweaks and improvements across the package. The NEWS file gives an overview of changes in this version. In this post we highlight some changes. library(magick)stopifnot(packageVersion('magick') >= 1.6) If you are new to magick, check out the vignette for a quick introduction.

This week magick 1.5 appeared on CRAN. The latest update adds support for using images in knitr documents and shiny apps. In this post we show how this nicely ties together a reproducible image workflow in R, from source image or plot directly into your report or application.

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.