Nvim-R VS jupyterlab-lsp

If you do a lot of R coding, then a package more specific to R, and more fully featured is Nvim-R.

The whole reason I ended up going this route was also sort of data-science related: there’s a really spectacular R plugin for Vim, which I wanted to recapitulate as best I could when using python: https://github.com/jalvesaq/Nvim-R

But just wondering if people have tried using it with the Nvim-R plugin? I'm not sure it is worth the effort for me looking through the steps needed. Also, do I need to switch my init.vim/vimrc to lua? Perspectives appreciated!

However, there doesn't seem to be much available support yet for Quarto editing. The only plugin I could find is limited to syntax highlighting. To my knowledge, Quarto also isn't a built-in filetype yet. I've worked around this by manually creating a filetype and using R Markdown syntax highlighting with the Nvim-R plugin, which lets me send R code in chunks to a REPL and see results while I edit. Nvim-R also supports evaluation of Python code chunks using an R package that evaluates Python code, but that's not an ideal solution for editing a Python-only file.

looking for a neovim plugin that's similar to Nvim-R, but for python.

For that reason, I want to use Nvim-R in combination with ncm-R. I get the completion to work if I use it manually with Ctrl+x Ctrl+o, but it does not start automatically. My init.vim file looks like this:

I use Nvim-R. It took a bit of time to learn the key bindings, but the documentation is pretty clear.

If you want to go in a very different direction, you can try vim (or neovim) with nvim-r. For a variety of reasons, that’s what I tend to use.

nvim + Nvim-R user here as well.

I use https://github.com/jalvesaq/vimcmdline for python and https://github.com/jalvesaq/Nvim-R for R

I've recently seen JupyterLab LSP that bring the static analysis aspect of LSP to the notebook, and been wondering if there is anything similar that try to bridge a running kernel back into Neovim.

JupyterLab is optimised for handling R, Python and Julia. Code intelligence-wise it requires installing jupyterlab-lsp to get all the best features.

On the upside, going down this rabbit hole also made me install jupyterlab-lsp for VS Code like auto complete, and jupyterlab_code_formatter to have auto formatting in notebooks.

JupyterLab optionally supports these features by integrating with Language Servers using the jupyterlab-lsp extension. Language Servers are available for many languages, including Python and R. Note that some Language Servers might not support every convenience feature.

Heard that. Check out these LSP extensions https://github.com/krassowski/jupyterlab-lsp I expect more like that will make their way into the main experience.

JupyterLab with the lsp extension?

Make sure to try out experimental IDE features with: https://github.com/krassowski/jupyterlab-lsp

There is also ongoing work on adding more IDE features (autocompletion, diagnostics, hover, jump to definition) for JupyterLab here: https://github.com/krassowski/jupyterlab-lsp It also experimentally works with JupyterLab Classic which is a backport of the old notebook interface using the new JupyterLab infrastructure.