The recommended method of building binary packages is to use Nearly all bioconductor packages to be installed, as the underlying system dependenciesĪre all included. Levi Waldron and Nitesh Turaga to produce the bioconductor_full docker image will allow for Version, compiler, etc., we could use binary packages in linux as well.ĭocker provides just such a shareable and identical environment for linux. If, however, one had an identical set of installed libraries, kernel The primary audience for a personal R package is yourself, and it doesn’t matter if no else uses or cares about it.One of the challenges of producing a performant build environment for linux, such as what mightīe used to have developers test software in identical environments, is the need to compile But nobodies can do the same: I now have one. Plenty of prominent statisticians and data scientists have personal R packages in which they store their miscellaneous functions: Karl Broman Yihui Xie and David Robinson, to name a few. And thanks to the wonderful usethis package, creating a personal R package could scarcely be easier.

But a package doesn’t need to be the next data.table or have the audience of dplyr to be worthwhile. If you’ve browsed the source code of a popular CRAN package then you can be forgiven for feeling overwhelmed. Writing an R package can seem daunting at first.

If you’re nodding along so far, this blogpost is for you.ĭo you use Professional Posit Products? If so, checkout out our managed Posit services You’re proud of some of it, and ashamed at the mess of the rest of it. It’s entirely untested, poorly documented and takes a while to decipher every time you return to it. It’s spread across multiple locations, wherever was easiest at the time: a directory on a laptop a pen drive line 243 of the script in which it was originally conceived. If you’re anything like me, your personal library of code has grown gradually and haphazardly.

Said hodgepodge might include functions you source into multiple projects bits and bobs that you copy and paste where needed or code that solved a particularly esoteric problem and will never be applicable elsewhere, but you still enjoy revisiting sometimes. If you’ve been using R for a while, you’ve likely accumulated a hodgepodge of useful code along the way.

R build package