rocker
R configurations for Docker (by rocker-org)
box
Write reusable, composable and modular R code (by klmr)
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rocker | box | |
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14 | 31 | |
1,434 | 809 | |
0.8% | - | |
3.7 | 7.8 | |
about 2 months ago | 8 days ago | |
Shell | R | |
GNU General Public License v3.0 only | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rocker
Posts with mentions or reviews of rocker.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-15.
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What's the best way to manage packages for different versions of R?
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/
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What is the 'Fedora experience' like for scientific computing?
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/
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[S] Step-by-step on how update to a specific version of R.
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.
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Temporarily Disable R Studio
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.
- rocker: R configurations for Docker
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Tips for using docker
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/
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Does anyone feel like R is actually vastly worse for dependency/environment management than Python?
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.
- My experience of trying to get the latest software on Linux is as confusing (annoying?) as Windows!
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Trying To Run R Studio from Docker (rocker/rstudio) "Cannot Connect to R Session"
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. :)
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Which video course or book would you recommend for R on AWS?
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.
box
Posts with mentions or reviews of box.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-04.
- Trying to Replicate Excel financial Functions
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Can someone explain how R project are organized and deployed?
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’.
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Good practice with long R scripts - any examples?
You can write amazing, clean, modular code with the box package.
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Does anyone feel like R is actually vastly worse for dependency/environment management than Python?
I would look into box https://github.com/klmr/box if you haven’t heard of it already
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"Managing large codebases in R" webinar (Oct. 6, 2022)
Shapeless plug: check out the already mentioned ‘box’, I think it’s strictly superior to ‘import’ (but I’m biased).
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Get tsarted wiht R using this Cheat Sheet - DataCamp
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’.
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Which R function do you find somewhat tricky?
‘box’ fixes that.
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Restructuring a large R project. Need advice on how to wire up file paths and associated objects.
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.
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[Q] Loading `dplyr` packages within a function but not outside of it
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.
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Is it possible to see what functions are used from what library?
If you're writing you're own code you can use packageName::functionName(), or the box package. Which is definitely useful on larger codebases.
What are some alternatives?
When comparing rocker and box you can also consider the following projects:
r-docker - Docker images for R
renv - renv: Project environments for R.
r-minimal - Minimal Docker images for R
ggplot2 - An implementation of the Grammar of Graphics in R
covidapp-shiny - A simple Shiny app to display and forecast COVID-19 daily cases
rnim - A bridge between R and Nim
hadolint - Dockerfile linter, validate inline bash, written in Haskell
tidytable - Tidy interface to 'data.table'
buildkit - concurrent, cache-efficient, and Dockerfile-agnostic builder toolkit
sys - Easily create reusable command line scripts with R
paws - Paws, a package for Amazon Web Services in R
workflowr - Organize your project into a research website