@noamross @mauro_lepore @JennyBryan @hadleywickham @rOpenSci The recent follow-up to the rrrpkg essay is https://t.co/Qd8g91OhjR with @cboettig & @lincolnmullen. But as @noamross notes, there's now a rich diversity of approaches to organizing research projects using #rstats for reproducibility, each suiting different types of research.
@tjowens @Ted_Underwood On the specific details, here are articles about data sharing and reproducible research. While both are aimed at data scientists, I don't think there would be any meaningful difference for humanities data.
Our article (with @cboettig & @lincolnmullen) 'Packaging data analytical work reproducibly using #rstats (& friends)' was one of the top 5 most viewed #Anthropology #ComputationalBiology & #ComputationalScience articles published in @thePeerJ in 2017! https://t.co/gMclq5a07D https://t.co/qOFmFKQTM0
@surlyurbanist It's not easy, but for #rstats users it's getting less difficult every year, thanks to pkgs like @xieyihui's knitr and bookdown, and our rrtools (https://t.co/JOwkoeT8TR). We summarise several real-world examples of reproducible academic output in https://t.co/C029piyzXm
@petermacp @ma_salmon @rOpenSci It's a good way to work. We surveyed some of current practices in our recent article "Packaging data analytical work reproducibly using R (and friends)" https://t.co/Qd8g91OhjR with @cboettig & @lincolnmullen, published in 'The American Statistician'