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Icons in R
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The <a href="https://github.com/ropenscilabs/icon">icon</a> package provides a convenient interface for adding icons from popular web fonts to R Markdown documents. The project began at <a href="https://ozunconf17.ropensci.org/">rOpenSci OzUnconf 2017</a>, and was developed by <a href="https://github.com/mitchelloharawild">Mitchell O’Hara-Wild</a>, <a href="https://github.com/earowang">Earo Wang</a> and <a href="https://github.com/timothyhyndman">Timothy Hyndman</a>. The package currently supports icons from <a href="https://fontawesome.com/">Font Awesome</a>, <a href="https://jpswalsh.github.io/academicons/">Academicons</a>, and <a href="https://ionicons.com/">ionicons</a>.
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Icons can be added to your R Markdown documents using short prefixes which identify the font’s library.
...Our onboarding process ensures that packages contributed by the community undergo a transparent, constructive, non adversarial and open review process. Before even submitting my first R package to rOpenSci onboarding system in December 2015, I spent a fair amount of time reading through previous issue threads in order to assess whether onboarding was a friendly place for me: a newbie, very motivated to learn more but a newbie nonetheless. I soon got the feeling that yes, onboarding would help me make my package better without ever making me feel inadequate....
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. Registration is optional, although registration and the use of an API key allows for larger queries without the risk of being timed out or rate limited by the API....
Our onboarding process, that ensures that packages contributed by the community undergo a transparent, 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-review process is a major part of ensuring its sustainability and quality. In this post, we’ll take a look at the effort put in by participants in the review process, and also learn something about exploring GitHub data along the way....
Our onboarding reviews, that ensure that packages contributed by the community undergo a transparent, constructive, non adversarial and open review process, take place in the issue tracker of a GitHub repository. Development of the packages we onboard also takes place in the open, most often in GitHub repositories.
Therefore, when wanting to get data about our onboarding system for giving a data-driven overview, my mission was to extract data from GitHub and git repositories, and to put it into nice rectangles (as defined by Jenny Bryan) ready for analysis. You might call that the first step of a “tidy git analysis” using the term coined by Simon Jackson. So, how did I collect data?
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