reticulate

Polyglot Data Science: Why and How to Combine R and Python (Jeroen Janssens) | posit::conf(2025)

Polyglot Data Science: Why and How to Combine R and Python

Speaker(s): Jeroen Janssens

Abstract:

Data Science at the Command Line and Polars | Jeroen Janssens | Data Science Hangout

Computing and recommending company-wide employee training pair decisions at scale… posit conf 2024

Regis A. James developed MAGNETRON AI, an innovative, patent-pending tool that automates at-scale generation of high-quality mentor/mentee matches at Regeneron to enable rapid, yet accurate, company-wide pairings between employees seeking skill training/growth in any domain and others specifically capable of facilitating it. Built using R, Python, LLMs, shiny, MySQL, Neo4j, JavaScript, CSS, HTML, and bash, it transforms months of manual collaborative work into days. The reticulate, bs4dash, DT, plumber API, dbplyr, and neo4r packages were particularly helpful in enabling its full-stack data science. The expert recommendation engine of the AI tool has been successfully used for training a 400-member data science community of practice, and also for larger career development mentoring cohorts for thousands of employees across the company, demonstrating its practical value and potential for wider application.

Talk by Regis A. James

Validating and Testing R Dataframes with Pandera via reticulate - R-Python Interoperability

Presented by Niels Bantilan

Original Full Title: Validating and Testing R Dataframes with Pandera via reticulate: A Case Study in R-Python Interoperability

Data science and machine learning practitioners work with data every day to analyze and model them for insights and predictions. A major component of any project is data quality, which is a process of cleaning, and protecting against flaws in data that may invalidate the analysis or model. Pandera is an open source data testing toolkit for dataframes in the Python ecosystem: but can it validate R dataframes?

This talk is composed of three parts: first I’ll describe what data testing is and motivate why you need it. Then, I’ll introduce the iterative process of creating and refining dataframe schemas in Pandera. Finally, I’ll demonstrate how to use it in R with the reticulate package using a simple modeling exercise as an example.

Data Science Hangout | Javier Orraca-Deatcu, Centene | Excel to data science to lead ML engineer

We were joined by Javier Orraca-Deatcu, Lead Machine Learning Engineer at Centene.

Among many topics covered, Javier shared how his background in finance and consulting led to his interest in data science to automate some of his work - and how he helped get other data scientists together in his organization.

(26:31) How did you organize and recruit people for the data science community group at Centene?

So I sort of piggybacked from a general data science community chat that we had at the company. There were several hundred people on this of varying backgrounds and expertise levels so there was a lot of conversation happening. There was already a Python group that was meeting– I think every other month.

So three weeks after I started, I got really excited about the possibility of potentially creating something similar for R users.

1. It started by just trying to figure out who owns that already existing data science chat and see if they could help support the idea of creating an R user group, something to meet once a month or once every two months. At larger companies especially, getting that type of top-level executive stamp of approval and support can go a long way, especially if that individual is part of the already existing IT or data science function.

2. At the time, I created a Blogdown site. For those of you who are familiar with R Markdown, Blogdown is a package that allows you to create static websites and blogs with R Markdown. Now with Quarto you can do the same thing and create websites. I love the syntax of Quarto.

3. We had partnerships with Posit, so we were able to get some people to come in and do workshops as well.

4. We also had reticulate sessions, where it was a co-branded Python & R workshop where we were looking at ways in which we can actually communicate between teams of different languages a lot easier. I had a great experience with it. Everyone was so collaborative and it was such a great way to see the excitement around what you could do with both R and Python.

I think what started as 13 users the first month, jumped to about 100 - 125 monthly users on this monthly meetup.

…And on the journey to machine learning engineer, what was the hardest part? (49:10):

Because of SQL, I had a really good understanding of at least how tabular data could be joined and the different transformations that could be done to these data objects.

I think I would have really struggled without that basic understanding. But having said that, I think the part where I really struggled at first was function writing. Function writing was not intuitive to me.

Basic function writing was but in general, I found it to be very complicated and it took a solid three to six months of practice to feel actually comfortable with it.

Even when I started building Shiny apps– basic Shiny is quite easy but large functions underpin the entirety of a Shiny app. Everything you do within Shiny is effectively writing functions.

The process of learning Shiny and becoming more comfortable with Shiny was very difficult and something that just took a lot of repetitions but it all sort of played together.

While people may think of Shiny more as a frontend type of system, it did make me a much better programmer in the way I thought about actual functions and function writing.

Other things that I found hard, looking back, I’m sort of embarrassed to say this, was reproducibility of machine learning – being able to reproduce a code set and get the exact same predictions every time.

I wasn’t quite sure why this wasn’t working or how to create these fixed views, setting a seed or whatever you need to do to ensure that someone else downstream could replicate your study or analysis and get the exact same findings themselves.

R Markdown Advanced Tips to Become a Better Data Scientist & RStudio Connect | With Tom Mock

R Markdown is an incredible tool for being a more effective data scientist. It lets you share insights in ways that delight end users.

In this presentation, Tom Mock will teach you some advanced tips that will let you get the most out of R Markdown. Additionally, RStudio Connect will be highlighted, specifically how it works wonderfully with tools like R Markdown.