Interfacing R and Python

on YouTube ↗32m

" summary

Interfacing R and Python becomes practical: reticulate (available on CRAN and from GitHub) claims direct import of Python modules, running Python strings/files and exposing Python objects as first‑class R variables via functions like import, source_python and py_run_string. Virtual environments are managed from R (create and use), but interpreter choice binds on first use. Large arrays are memory‑mapped so data need not copy back and forth, enabling R model diagnostics and Python libraries to work side by side.

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" chapters (15)

1. 0:00R vs Python rivalry
2. 2:36R vs Python features
3. 6:30Mixing R and Python
4. 7:15R in Python with rpy2
5. 11:09Data transfer R-Python
6. 13:42Plots with R in Python
7. 16:09Synthetic data R in Python
8. 17:55Python in R with reticulate
9. 20:04Python packages in R
10. 23:17Python envs in R
11. 25:28Efficient R-Python data
12. 26:56Run Python from R
13. 28:03Interactive Python in R
14. 29:30rpy2 vs reticulate
15. 30:12Conclusion & Q&A

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