rnim
box
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rnim
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Deeplearning in Nim?
While indeed we are less people developing stuff in Nim compared to even the Julia community (which itself is of course much smaller than say Python), we do have cover a large amount of the typical needs in the scientific computing domain. And where we miss stuff it's a) easy to wrap C/C++ or b) simply call Julia, R or Python (As a personal reference I'm doing data analysis & numerical physics stuff in context of my PhD in physics and I literally do everything in Nim. The only significant C dependency {and only as a shared lib} I depend on is libhdf5 via nimhdf5).
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Stan in Nim?
use Rnim to access the R bindings
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Anyone attempted to make Nim serve R's role? How is it currently?
If you're willing to help out, you'll surely be able to do anything you need. If Nim libraries fail, you can also always call R directly from Nim via Rnim!
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Generate Python extensions using Nim language
Maybe also of interest is a nascent package for R calling Nim (or vice versa): https://github.com/SciNim/rnim
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Nim -- a modern "glue" language like Python
c2nim is a tool to translate ANSI C code to Nim. The output is human-readable Nim code that is meant to be tweaked by hand after the translation process. If you are tired of wrapping C library, you can try futhark which supports "simply import C header files directly into Nim". Similar to futhark, cinterop allows one to interop with C/C++ code without having to create wrappers. nimLUA is a glue code generator to bind Nim and Lua together using Nim's powerful macro. nimpy and nimporter is a bridge between Nim and Python. rnim is a bridge between R and Nim. nimjl is a bridge between Nim and Julia! Last but not least, genny generates a shared library and bindings for many languages such as Python, Node.js, C.
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What would you like to see from an R2 / R++ / R#
I am risking to be offtopic, but there is somenthing interesting happening in Nim, where someone wrote a wrapper to R: https://github.com/SciNim/rnim
box
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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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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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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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How do you organize your code snippets/notes?
Check out ‘box’, it’s designed precisely to solve (most of) your issues. Namely, it allows you to organise your R code files into (nested) modules, which can either be part of one project, or they can be stored centrally, and reused seamlessly across projects. It also allows you to document your code and makes this documentation available just like package documentation.
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Creating your own R package
I'll also name drop the Box package, which serves like a middle of the road option between creating packages and sourcing scripts. It allows you to treat scripts (and the functions inside them) like packages without having to go through all the extra steps to build packages. In that way, it's more similar to creating and importing python modules.
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import functions from a package without loading the package
I don't fully understand what happens when you attach a package, so I can't speak to your question about side-effects. However, if you're looking for an alternative, possibly safer, strategy, you might try the package box.
There is: use the ‘box’ package. It allows this and more. In your specific case, you’d write
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Reference project for an R 2+2=4 style application coverage, testthat and a build file
That said, I’ve been playing with a framework for command line applications in R (as a ‘box’ module), and I guess you could call the example application “2+2=4” style (it’s a command line calculator, complete with automatically generated option parser etc.).
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What would you like to see from an R2 / R++ / R#
You can do that with ‘box’, check it out!
What are some alternatives?
genny - Generate a shared library and bindings for many languages.
renv - renv: Project environments for R.
c2nim - c2nim is a tool to translate Ansi C code to Nim. The output is human-readable Nim code that is meant to be tweaked by hand before and after the translation process.
ggplot2 - An implementation of the Grammar of Graphics in R
nimpy - Nim - Python bridge
Datamancer - A dataframe library with a dplyr like API
nimLUA - glue code generator to bind Nim and Lua together using Nim's powerful macro
nimhdf5 - Wrapper and some simple high-level bindings for the HDF5 library for the Nim language
stan - Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
tidytable - Tidy interface to 'data.table'
futhark - Automatic wrapping of C headers in Nim
ggplotnim - A port of ggplot2 for Nim