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Before everybody made their way to the unconf via LAX and Lyft, attendees discussed potential project ideas online. The packagemetrics package was our answer to two related issues. The first proposal centered on creating and formatting tables in a reproducible workflow. After many different package suggestions started pouring in, we were left with a classic R user conundrum: “Which package do I choose?” With over 10,000 packages on CRAN - and thousands more on GitHub and Bioconductor - a useR needs a way to navigate this wealth of options....
What’s that? You’ve heard of R? You use R? You develop in R? You know someone else who’s mentioned R? Oh, you’re breathing? Well, in that case, welcome! Come join the R community! We recently had a group discussion at rOpenSci’s #runconf17 in Los Angeles, CA about the R community. I initially opened the issue on GitHub. After this issue was well-received (check out the emoji-love below!), we realized people were keen to talk about this and decided to have an optional and informal discussion in person....
charlatan makes fake data. Excited to annonunce a new package called charlatan. While perusing packages from other programming languages, I saw a neat Python library called faker. charlatan is inspired from and ports many things from Python’s https://github.com/joke2k/faker library. In turn, faker was inspired from PHP’s faker, Perl’s Faker, and Ruby’s faker. It appears that the PHP library was the original - nice work PHP. 🔗 Use cases What could you do with this package?...
Two years ago at #runconf15, there was a great discussion about best practices for organizing R-based analysis projects that yielded a nice guidance document describing research compendia. Compendia, as we described them, were minimal products of reproducible research, using parts of R package structure to organize the inputs, analyses, and outputs of research projects. Since then, we’ve seen more examples and models of research compendia emerge (the organization of such projects is something of an obsession for some of the community)....
Textual data and natural language processing are still a niche domain within the R ecosytstem. The NLP task view gives an overview of existing work however a lot of basic infrastructure is still missing. At the rOpenSci text workshop in April we discussed many ideas for improving text processing in R which revealed several core areas that need improvement: Reading: better tools for extracing text and metadata from documents in various formats (doc, rtf, pdf, etc)....
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