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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)....
And finally, we end our series of unconf project summaries (day 1, day 2, day 3, day 4). 🔗 mwparser Summary: Wikimarkup is the language used on Wikipedia and similar projects, and as such contains a lot of valuable data both for scientists studying collaborative systems and people studying things documented on or in Wikipedia. mwparser parses wikimarkup, allowing a user to filter down to specific types of tags such as links or templates, and then extract components of those tags....
Happy rOpenSci users can be found at