One of the best things about learning R is that no matter your skill level, there is always someone who can benefit from your experience. Topics in R ranging from complicated machine learning approaches to calculating a mean all find their relevant audiences. This is particularly true when writing R packages.

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.

Introduction When I was in grad school at Emory, I had a favorite desk in the library. The desk wasn’t particularly cozy or private, but what it lacked in comfort it made up for in real estate. My books and I needed room to operate. Students of the ancient world require many tools, and when jumping between commentaries, lexicons, and interlinears, additional clutter is additional “friction”, i.e., lapses in thought due to frustration.

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.

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.

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.

One of the greatest assets human beings possess is the power of speech and language, from which almost all our other accomplishments flow. To be able to analyse communication offers us a chance to gain a greater understanding of one another.

Why care about patents? 1. Patents play a critical role in incentivizing innovation, withoutwhich we wouldn’t have much of the technology we rely on everyday What does your iPhone, Google’s PageRank algorithm, and a buttersubstitute called Smart Balance all have in common? …They all probably wouldn’t be here if not for patents.

We have started working on a new rOpenSci package called writexl. This package wraps the very powerful libxlsxwriter library which allows for exporting data to Microsoft Excel format. The major benefit of writexl over other packages is that it is completely written in C and has absolutely zero dependencies. No Java, Perl or Rtools are required. Getting Started The write_xlsx function writes a data frame to an xlsx file.