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do you mean the template task? Currently no, we added it to facilitate the concept explanation for new users @Jess Mankewitz (they/she) has been doing a lot of stuff with R. 😀 but I'm unsure if she has experience mixing R and Python. I also did a bit of this successfully, I was using parquet to allow R/Python interoperability. since you can install the R interpreter and R packages with conda-forge, you can declare a single environment.yml to document all your dependencies. please share your experience with us!

Hi! I have a project that switches seamlessly between my python scripts and my r scripts using ploomber and conda as the “glue”. The main quirk we experienced were needing to initialize our conda environment with both R and Python. We’ve also had some conda troubles when switching machines, but we think that was a conda problem not a ploomber problem.

thanks for sharing your experience! for solving thr problem when switching machines. have you tried

conda env export --no-build > environment.yml

? this will produce a portable yml file that you can use to re-create environments on other machines

@Jess Mankewitz (they/she) is there a use case of running both R and python in the same notebook with magics?

in my very humble opinion, not really lol but some of my collaborators like to do exploratory modeling and on-the-fly plotting in the same notebook

So you have a separate environment.yml for conda. What d u use for R environment management and how does it blend together in the env.yml of conda? I.e. How does your project set up look like? I mean the following:

Conda env create ... 
Pip install -e

yeah we have a .yml for our conda environment that has both r and python like…

name: proj_pipeline
channels:
  - conda-forge
  - defaults
dependencies:
  - ploomber=0.19.4
  - python=3.8.13
  - r-base=4.0.5

If u have specific r libraries where d u put them? Just r-.... ?

for most of them! we have some that arent on forge. in that case, our full setup commands are

conda env create -f proj_pipeline.yml

conda activate proj_pipeline
ploomber scaffold --entry-point pipeline.preprocessing.yaml
ploomber scaffold --entry-point pipeline.modeling.yaml
#install childesr in R, not conda
R
install.packages(c("childesr", "dclone"))  # mirror 76

#setup the nltk
python
import nltk
nltk.download('wordnet')
nltk.download('omw-1.4')
nltk.download('semcor')

And can you conda export the complete .yml including the specific packages or r?

so we enter R from within our conda environment and install the non-conda R packages manually

I see. Not the neatest solution but it works.

I dont think so, i think conda export skips over the non-conda packages?

I LL have to try and play around with this. Perhaps, R has very different idea on how reproducible environments should be done. I wonder if this mixing is a good idea or just trouble waiting to happen. For me even just pure conda often fails to keep reproducible env.

yeah there are some reproducibility focused R packages, but they all function within R and dont play nice with conda

so u do not mix conda + renv for the underlying R