box VS rocker

As for organising code within a project, as mentioned packages really don’t allow this beyond collation order. The best solution in this space is the ‘box’ package which implements a fully-featured module system for R. ‘box’ notably gets used by some folks to implement large-scale Shiny applications; if this is what you’re after, I would recommend the ‘rhino’ framework, which builds upon Siny and ‘box’.

You can write amazing, clean, modular code with the box package.

I would look into box https://github.com/klmr/box if you haven’t heard of it already

Shapeless plug: check out the already mentioned ‘box’, I think it’s strictly superior to ‘import’ (but I’m biased).

By contrast, R code doesn’t need to change the working directory at all! Having to do so hides other flaws in the code. For instance, when trying to load code or data, use the tools provided by R. That is, write packages and use system.file or, when not writing packages, use ‘box’.

‘box’ fixes that.

I think your use-case is best addressed by the ‘targets’ package. But I would also recommend checking out the ‘box’ package for a more general way of structuring R projects in modules which isn’t supported well natively by R (disclaimer: I wrote that package). Writing R code as modules fundamentally side-steps the issue of having to deal with absolute paths. Instead, all code and data are either contained in the module or can be accessed relative to the working directory.

However, using ‘box’, as recommended in another comment, allows you to achieve the same effect with less (and cleaner) code, by declaring your imports locally with the box::use function.

If you're writing you're own code you can use packageName::functionName(), or the box package. Which is definitely useful on larger codebases.

I am, strictly speaking, not a big R user, so take my opinion with a grain of salt, but if I were using R extensively, I would absolutely use the Rocker project containers to manage different R versions and different sets of dependencies for different projects: https://rocker-project.org/

Perhaps the main difference is r package are not available as binaries from rstudio (posit) repo but their is a cran2copr repo that works really well or you can still install from source in your home. For more info on cran2copr see: https://cran.rstudio.com/bin/linux/fedora/ . Personally I am slowly moving to container based workflow with podman (and not toolbox as you end up having your r package install directly in home but that can be worked out by specifying the ribs path). I use docker image from the rocker project: https://rocker-project.org/

If you have such specific requirements it’s often easier to use a container like the one from rocker (runs in der docker) instead. Btw wouldn’t be surprised if you’d get the latest version running in there as well.

Check out the Rocker Project, comprising of Docker containers for R, and can be used with RStudio. Also, virtual environments e.g., renv package can also help solve the package versioning issue, aside from containerization, and is transferable to a new machine via the renv::restore() function.

https://hub.docker.com/u/rocker has a lot of R-related images and they look pretty legit (look at "Tags" to find different versions). Don't use weird looking images. There's a lot of malware out there. Here's a guide on nice docker files: https://docs.docker.com/develop/develop-images/dockerfile\_best-practices/

Other people have mentioned renv and packrat already (hasn't renv basically superseded packrat at this point?), but what is also nearly ready-made to deal with this is rocker's R images. They have a bunch of images preconfigured for typical TidyVerse stuff, Shiny, etc.

The Apple M1 is ARM. As far as my knowledge, ARM isn't supported. Looks like the rocker project is aware of it. Considering how popular these chips are, i'm confident a lot of smart people are working on it. :)

But, with docker, there are many prebuilt images provided by the RStudio team directly, and other great repositories from rocker. These are basically images for your full SDLC with R, from development to deployments.