reticulate VS Pluto.jl

Hi! At this moment I'm not opening the source code, but I can explain you the tech used. This site is based on another site I created before named https://r-charts.com/ and it was created with blogdown (HUGO + R Markdown). Hence, each tutorials is an R markdown file. For PYTHON CHARTS, in order to run Python within an R markdown file I had to use an R package named reticulate. In addition, the template depends on shuffle.js for filtering and fuse.js for searching

It's super easy and environments work really well too. What I typically do is start a markdown document and run whatever chunks in R or Python I'd like and then use the reticulate package to communicate between them. Using an R object in Python is as easy as calling "r.objectname" or you can simply run "source_python" and preface python functions with py$ and run them directly on R objects. The reticulate package does a good job of converting objects into the appropriate types. I was up and running in an afternoon and most of the Python code I simply just saved as .py scripts and ran directly in R-Studio with no problems. I often get output from Python functions in some format and convert it into a data frame, do what I need to do with tidy if required, and then ggplot for the visuals. Here is the tutorial that got mestarted

Fastest way to get from messy data to model IMO. Plus I can code in python in R with reticulate. https://rstudio.github.io/reticulate/

How does RStudio have little interest in interoperability with other languages? They produce the reticulate package[1] to allow calling Python code for R, they have added support for Python to RMarkdown and RStudio[2], they let you host Python apps on their RStudio Connect product[3], they sponsor Ursa Labs to work on the Arrow project for easy data interchange[4].

1) https://rstudio.github.io/reticulate/

Reticulate

Seems like a python problem. Do you have any version of Python already installed on your computer? Context: ( https://github.com/rstudio/reticulate/issues/313 )

So we discuss this briefly on our FAQ but let me try to expand on it.

Our goal is to make a modern literate programming tool. On a surface level, a tool like that would end up looking very similar to Jupyter, though with better features. We've mentioned some things we'd like to have in this final tool in our README and also in the post above.

Our first thought was to make a tool from scratch. The challenge was, it's very hard to get people to switch and so, we had to go where people already are - that meant Jupyter.

We could've made this one feature an extension with some difficulty (in-fact, our early experiments, we started by making an extension). It would have some downsides - we wouldn't have granular control over certain core Jupyter behaviours like we do right now (for eg, we wanted to allow creating hidden folders to store some files). But we probably could have made a 95% working version of Pretzel work as a jupyter extension.

The bigger reason we chose to fork was because down the line, we want to completely change the code execution model to being DAG based to allow for reproducible notebooks (similar to https://plutojl.org/ for eg). Similarly, we want to completely remove Codemirror and replace it with Monaco (the core editor engine in VSCode) to provide a more IDE like experience in Jupyter. These things simply couldn't have been done as extensions.

I thought that notebook based development and package based development were diametrically opposed in the past, but Pluto.jl notebooks have changed my mind about this.

A Pluto.jl notebook is a human readable Julia source file. The Pluto.jl package is itself developed via Pluto.jl notebooks.

https://github.com/fonsp/Pluto.jl

Also, the VSCode Julia plugin tooling has really expanded in functionality and usability for me in the past year. The integrated debugging took some work to setup, but is fast enough to drop into a local frame.

https://code.visualstudio.com/docs/languages/julia

Julia is the first language I have achieved full life cycle integration between exploratory code to sharable package. It even runs quite well on my Android. 2023 is the first year I was able to solve a differential equation or render a 3D surface from a calculated mesh with the hardware in my pocket.

Re Julia there is also pluto.jl that is another notebook-like environment for julia. It's been a few years since I played with it but it looked cool, for example it handles state differently so you don't get into the same messes as with ipython notebooks. https://plutojl.org/

For this, Notebooks are often used. Julia offers a uniquely nice and interactive Pluto notebook for the web https://github.com/fonsp/Pluto.jl

I believe this is what Pluto sets out to do for Julia.

I used it as part of the “Computational Thinking” with Julia course a year or two back. Even then the beta software was very good and some of the demos the Pluto dev showed were nothing short of amazing

https://plutojl.org/

I think what you want is Pluto!