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The gifski package which was demonstrated in May at eRum 2018 in Budapest is now on CRAN. Gifski is a simple but powerful package which can hopefully take away an important performance bottleneck for generating animated graphics in R. 🔗 What is Gifski Gifski is a multi-threaded high-quality GIF encoder written in Rust. It can create animated GIF images with thousands of colors per frame and do so much faster than other software....
R packages are widely used in science, yet the code behind them often does not come under scrutiny. To address this lack, rOpenSci has been a pioneer in developing a peer review process for R packages. The goal of pkginspector is to help that process by providing a means to better understand the internal structure of R packages. It offers tools to analyze and visualize the relationship among functions within a package, and to report whether or not functions' interfaces are consistent....
Evolutionary biologists are increasingly using R for building, editing and visualizing phylogenetic trees. The reproducible code-based workflow and comprehensive array of tools available in packages such as ape, phangorn and phytools make R an ideal platform for phylogenetic analysis. Yet the many different tree formats are not well integrated, as pointed out in a recent post. The standard data structure for phylogenies in R is the “phylo” object, a memory efficient, matrix-based tree representation....
It’s easy to come to a conference and feel intimidated by the wealth of knowledge and expertise of other attendees. As Ellen Ullman, a software engineer and writer describes, I was aware at all times that I had only islands of knowledge separated by darkness; that I was surrounded by chasms of not-knowing, into one of which I was certain to fall. Ellen Ullman in Life in Code: A Personal History of Technology....
Data == knowledge! Much of the data we use, whether it be from government repositories, social media, GitHub, or e-commerce sites comes from public-facing APIs. The quantity of data available is truly staggering, but munging JSON output into a format that is easily analyzable in R is an equally staggering undertaking. When JSON is turned into an R object, it usually becomes a deeply nested list riddled with missing values that is difficult to untangle into a tidy format....
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