(ICYMI) RPweave: Unified R + Python + LaTeX system with uv

ICYMI: RPweave — Unified R + Python + LaTeX workflow (powered by uv)

When you juggle R, PythonAnd Latex for research you know the pain: fragmented scripts, mixed environments, manual copying and fragile reproducibility.

I needed a setup/workflow that could handle this both languages, any LaTeX template, isolation from the environmentand a command-line-first workflow – so I collected RPweaving. Not a new idea, but a polished, modern version that just works.

RPweaving (GitHub template here: https://github.com/Techtonique/RPweave) connects everything together using:

knitter for R+Python chunks
reticulate for seamless Python integration
Latex for publication-ready typesetting
uv for fast, reproducible Python environments
Makefile automation for building and viewing
One ready to clone Git template

Get started

The workflow starts by cloning the RPweave template repository and listing the Python packages you need requirements.txt. Then set up the isolated environment and install R dependencies via make setup. Finally, write your .Rnw document mixing R and Python chunks and building with them make view.

git clone https://github.com/Techtonique/RPweave my-paper
cd my-paper
uv venv venv && source venv/bin/activate
make setup
make view

Why it matters

With RPweave you can:

Walk R And Python in the same .Rnw document on the command line
Easily share objects in different languages
Use a LaTeX template or academic style
Keep everything reproducible with isolated environments
Build your PDF with a single command (make view)

Minimal example

The first part is mandatory.

<>=
library(knitr); library(reticulate)
use_python("venv/bin/python")
@

<>=
ggplot(mtcars, aes(wt, mpg)) + geom_point()
@

<>=
import pandas as pd
print(pd.DataFrame({'x': range(100)}).describe())
@

Ideal for

Mixing papers R statistics + Python ML
Projects that need to be cleaned Latex Exit
Reproducible workflows with pinned dependencies
Researchers are tired of context switching between RStudio, Jupyter, and LaTeX editors

Professional tips

Usage make view as your main loop – instant rebuild + preview
Store long pieces in it chunks/
Keep generated files out of Git
Pass data R → Python via py$object for smooth cross-language flows

Repository and documents

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