phylogram: dendrograms for evolutionary analysis

  Shaun Wilkinson   | JULY 12, 2018

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.

Exploring ways to address gaps in maternal-child health research

  Monica Gerber   |   Jennifer Thompson   |   Jenny Draper   |   Kyle Hamilton   |   Charles Gray   | JULY 5, 2018

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,1 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. One of the best ways to start feeling less intimidated is to start talking to others.

A package for tidying nested lists

  Amanda Dobbyn   |   Jim Hester   |   Laura DeCicco   |   Christine Stawitz   |   Isabella Velasquez   | JUNE 26, 2018

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.

Exploring European attitudes and behaviours using the European Social Survey

  Jorge Cimentada   | JUNE 14, 2018

Introduction I never thought that I’d be programming software in my career. I started using R a little over 2 years now and it’s been one of the most important decisions in my career. Secluded in a small academic office with no one to discuss/interact about my new hobby, I started searching the web for tutorials and packages. After getting to know how amazing and nurturing the R community is, it made me want to become a data scientist.

drake's improved high-performance computing power

  Will Landau   | MAY 18, 2018

The drake R package is not only a reproducible research solution, but also a serious high-performance computing engine. The package website introduces drake, and this technical note draws from the guides on high-performance computing and timing in the drake manual. You can help! Some of these features are brand new, and others are newly refactored. The GitHub version has all the advertised functionality, but it needs more testing and development before I can submit it to CRAN in good conscience.

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