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,

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.

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.

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.

Phylogenetic trees are commonly used to present evolutionary relationships of species. Newick is the de facto format in phylogenetic for representing tree(s). Nexus format incorporates Newick tree text with related information organized into separated units known as blocks. For the R community, we have ape and phylobase packages to import trees from Newick and Nexus formats.

Our onboarding processensures that packages contributed by the community undergo atransparent, constructive, non adversarial and open review process.Before even submitting my first R package to rOpenSci onboarding systemin December 2015, I spent a fair amount of time reading through previousissue threads in order to assess whether onboarding was a friendly placefor me: a newbie, very motivated to learn more but a newbie nonetheless.I soon got

I’m excited to announce a new package for accessing official statistics from the UK. nomisr is the R client for the Nomis database. Nomis is run by Durham University on behalf of the UK’s Office for National Statistics (ONS), and contains over a thousand datasets, primarily on the UK labour market, census data, benefit spending and general economic activity.

Our onboarding process, thatensures that packages contributed by the community undergo atransparent, constructive, non adversarial and open review process,involves a lot of work from many actors: authors, reviewers and editors;but how much work ? Managing the effort involved in the peer-reviewprocess is a major part of ensuring its sustainability and quality.

Our onboardingreviews,that ensure that packages contributed by the community undergo atransparent, constructive, non adversarial and open review process, takeplace in the issue tracker of a GitHub repository. Development of thepackages we onboard also takes place in the open, most often in GitHubrepositories.

On March the 17th I had the honor to give a keynote talk about rOpenSci’s package onboarding system at the satRday conference in Cape Town, entitled “Our package reviews in review: introducing and analyzing rOpenSci onboarding system”. You can watch its recording, skim through the corresponding slides or… read this series!What is rOpenSci onboarding?