All posts (Page 76 of 122)
In the first post of this series, we sketched out some of the common challenges faced by educators who teach with R across scientific domains. In this post, we delve into what makes a “good” educational resource for teaching science with R.
For instructors teaching sciences with R, there are a number of open educational resources that they can reuse, tailor to their own teaching style, or use to inspire them in creating their own materials. Some examples:
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Educators who teach science using R tend to face common pedagogical problems, regardless of their scientific domain. Yet instructors who teach with R often feel isolated at their institutions. They may be the only ones in their departments to teach using R. Even if there are others, the culture of collaboration around teaching is generally impoverished, unlike the rich culture of collaboration around research. In this three-part series of blog posts, participants at the rOpenSci 2018 unconf briefly survey the state of teaching science with R.
...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.
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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. The Gifski Website has more technical details and beautiful examples.
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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. If you are reviewing an R package (maybe your own!), pkginspector is for you.
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. However, non-biologists have tended to use a tree structure called the “dendrogram”, which is a deeply nested list with node properties defined by various attributes stored at each level. While certainly not as memory efficient as the matrix-based format, dendrograms are versatile and intuitive to manipulate, and hence a large number of analytical and visualization functions exist for this object type. A good example is the dendextend package, which features an impressive range of options for editing dendrograms and plotting publication-quality trees.
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