Best way to use a conda environment with source_python

What is the best way to call a specific conda environment when using source_python in R code to call Python.

I have this Python script where I define a small function.

import xarray as xr
import os

def open_dscim_econ_vars(ssp_in, model_in, year_list, econ_vars, 
    econ_var_data = xr.open_zarr(path_in)
    econ_var_out = (econ_var_data[[econ_vars]]
                    .sel(ssp = ssp_in, model = model_in, year = year_list)
    econ_var_out.reset_index(inplace = True)
    return econ_var_out

Then in my R code I use source_python to call the function open_dscim_econ_vars

econ_vars_raw = open_dscim_econ_vars(ssp_in ='SSP3', model_in = 'low', year_list =c(2015, 2099),
                                       econ_vars ='gdp')

I have a conda environment I already made via reticulate::conda_create() called r-reticulate-ineq and I would like to make sure that when open_dscim_econ_vars() is called it is inside this environment. What is the best way to load the conda environment.

Should I just modify my R script to be something like this?

econ_vars_raw = open_dscim_econ_vars(ssp_in ='SSP3', model_in = 'low', year_list =c(2015, 2099),
                                       econ_vars ='gdp')

Or will that not work and I should do something else?

I think you need to call use_condaenv() prior to source_python(). In other words, you need to tell {reticulate} which Python executable you want to use as your Python interpreter before you have it parse Python code you have written in a file.

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