In this technote I will outline what phylotaR was developed for, how to install it and how to run it with some simple examples. 🔗What is phylotaR? In any phylogenetic analysis it is important to identify sequences that share the same orthology – homologous sequences separated by speciation events. This is often performed by simply searching an online sequence repository using sequence labels.

eBird is an online tool for recording birdobservations. The eBird database currently contains over 500 millionrecords of bird sightings, spanning every country and nearly every birdspecies, making it an extremely valuable resource for bird research andconservation. These data can be used to map the distribution andabundance of species, and assess how species’ ranges are changing overtime. This dataset is available for download as a text file;

🔗Motivation A few weeks ago, as part of the rOpenSci Unconference, a group of us (Sean Hughes, Malisa Smith, Angela Li, Ju Kim, and Ted Laderas) decided to work on making the UMAP algorithm accessible within R. UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique that allows the user to reduce high dimensional data (multiple columns) into a smaller number of columns for visualization purposes (github,

tl;dr : we propose three calls to action:Share your curricular materials in the open.Participate in the rOpenSci Education profile series.Discuss with us how you want to be involved in rOpenSci Educators’ Collaborative.

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.

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.

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.

Evolutionary biologists are increasingly using R for building,editing and visualizing phylogenetic trees.The reproducible code-based workflow and comprehensive array of toolsavailable in packages such as ape,phangorn andphytools make R an ideal platform forphylogenetic analysis.Yet the many different tree formats are not well integrated,as pointed out in a recentpost.

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, One of the best ways to start feeling less intimidated is to start talking to others. Ullman continues, At rOpenSci unconf18, we learned that it’s ok to feel like you don’t know everything – indeed, that’s how just about everyone feels!

Data == knowledge! Much of the data we use, whether it be fromgovernment repositories, social media, GitHub, or e-commerce sites comesfrom public-facing APIs. The quantity of data available is trulystaggering, but munging JSON output into a format that is easilyanalyzable in R is an equally staggering undertaking.