oh ok i see: so i will always be able to do clean(path = "~/data.csv")
for example? in that case I completely misunderstood this and there must indeed be some system.file call buried somewhere. will check. thanks! Florian On Fri, 28 Jun 2019 at 17:11, Georgi Boshnakov < georgi.boshna...@manchester.ac.uk> wrote: > You need to give details what exactly gets you into trouble and about your > use case, since any advice would be conditional on making assumptions about > that. It is usually a bad a idea to have a function working on a hardcoded > full filename. You will thank yourself later if you at least make it > argument to your function(s). It can have for default value the one that is > currently hardcoded. > > Please note that your use case does not seem inherently related to staged > installation. > "Hardcoded" in the context of staged installation does not refer to any > hardcoded path > but to those paths that contain the temporary installation directory. Such > paths can be obtained, > for example, with calls to system.path(), as illustrated by Tomas. > > Georgi Boshnakov > > > > -----Original Message----- > From: R-package-devel [mailto:r-package-devel-boun...@r-project.org] On > Behalf Of Florian Oswald > Sent: 28 June 2019 09:17 > To: r-package-devel@r-project.org > Subject: [R-pkg-devel] implications of staged install for data processing > packages > > Hi all > > I ran into trouble with the changes starting to come in with R3.6 stemming > from the new staged installation, which checks and errors on hard coded > paths in R code. I understand there is an opt out, but still want to know. > here's the blog post: > https://developer.r-project.org/Blog/public/2019/02/14/staged-install/ > > I have several packages which look like that: > > 1. large messy dataset stored on disk as `filename`, maybe a csv. > 2. R package has a function `clean(filename)` which reads the data and > brings it into useable form > 3. R package does analysis > 4. R package exports results > > `filename` is hard coded. What is the proper way to do this instead? Should > I store the inital raw data inside the R package in `/inst`? These things > are typically very large, so I like to decouple the raw data from the > package (easier to share). > > thanks for any suggestions! > > [[alternative HTML version deleted]] > > ______________________________________________ > R-package-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-package-devel > [[alternative HTML version deleted]] ______________________________________________ R-package-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-package-devel