Science is reportedly in the middle of a reproducibility crisis. Reproducibility seems laudable and is frequently called for (e.g., nature and science). In general the argument is that research that can be independently reproduced is more reliable than research that cannot be independently reproduced.

Reproducible research involves the careful, annotated preservation of data, analysis code, and associated files, such that statistical procedures, output, and published results can be directly and fully replicated. As the push for reproducible research has grown, the R community has responded with an increasingly large set of tools for engaging in reproducible research practices (see, for example, the ReproducibleResearch Task View on CRAN).

Upcoming Book on Open Science with R We’re pleased to announce that the rOpenSci core team has just signed a contract with CRC Press/Taylor and Francis R series to publish a new book on practical ways to implement open science into your own research using R. Given all the talk about the importance of open science, the discussion often lacks practical suggestions on how one might actually incorporate these practices into their