Jeroen Janssens

devtools

Tools to make an R developer's life easier

dplyr

dplyr: A grammar of data manipulation

forcats

: tools for working with categorical variables (factors)

ggplot2

An implementation of the Grammar of Graphics in R

haven

Read SPSS, Stata and SAS files from R

lubridate

Make working with dates in R just that little bit easier

plotnine

A Grammar of Graphics for Python

purrr

A functional programming toolkit for R

readr

Read flat files (csv, tsv, fwf) into R

stringr

A fresh approach to string manipulation in R

tidymodels

Easily install and load the tidymodels packages

tidyr

Tidy Messy Data

usethis

Set up commonly used components

pkgdown

Generate static html documentation for an R package

roxygen2

Generate R package documentation from inline R comments

ellmer

Call LLM APIs from R

cli

Tools for making beautiful & useful command line interfaces

DBI

A database interface (DBI) definition for communication between R and RDBMSs

vroom

Fast reading of delimited files

chatlas

Your friendly guide to building LLM chat apps in Python with less effort and more clarity

magrittr

Improve the readability of R code with the pipe

vitals

Large language model evaluation for R

odbc

Connect to ODBC databases (using the DBI interface)

dbplyr

Database (DBI) backend for dplyr

duckplyr

A drop-in replacement for dplyr, powered by DuckDB for speed

gh

Minimalistic GitHub API client in R

mirai

Minimalist Async Evaluation Framework for R

R6

Encapsulated object-oriented programming for R

ragnar

RAG in R

rlang

Low-level API for programming with R

testthat

An R to make testing

yardstick

Tidy methods for measuring model performance

nanonext

R binding for NNG (Nanomsg Next Gen)

httr2

Make HTTP requests and process their responses. A modern reimagining of httr

tidypredict

Run predictions inside the database

ps

R package to query, list, manipulate system processes

fs

Provide cross platform file operations based on libuv

profvis

Visualize R profiling data

finetune

Additional functions for model tuning

parsnip

A tidy unified interface to models

rsample

Classes and functions to create and summarize resampling objects

tibble

A modern re-imagining of the data frame

tidyselect

A backend for functions taking tidyverse selections

callr

Call R from R

infer

An R package for tidyverse-friendly statistical inference

tidyclust

A tidy unified interface to clustering models

downlit

Syntax Highlighting and Automatic Linking

bigrquery

An interface to Google's BigQuery from R

reprex

Render bits of R code for sharing, e.g., on GitHub or StackOverflow

vctrs

Generic programming with typed R vectors

applicable

Quantify extrapolation of new samples given a training set

archive

R bindings to libarchive, supporting a large variety of archive formats

asciicast

Turn R scripts into terminal screencasts

available

Check if a package name is available to use

bench

High Precision Timing of R Expressions

bit64

An R package with an S3 Class for Vectors of 64bit Integers

blob

A simple S3 class for representing BLOBs

bonsai

parsnip wrappers for tree-based models

brio

Basic R Input Output

broom

Convert statistical analysis objects from R into tidy format

brulee

High-Level Modeling Functions with 'torch'

butcher

Reduce the size of model objects saved to disk

cachem

Key-value caches for R

censored

Parsnip wrappers for survival models

clock

A Date-Time Library for R

concept-maps

Concept maps for all things data science

coro

Coroutines for R

crayon

️ R package for colored terminal output — now superseded by cli

debugme

Easy and efficient debugging for R packages

desc

Manipulate DESCRIPTION files

dials

Tools for creating tuning parameter values

discrim

Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package

dtplyr

Data table backend for dplyr

embed

Extra recipes for predictor embeddings

filelock

Cross platform file locking in R

gargle

Infrastructure for calling Google APIs from R, including auth

glue

Glue strings to data in R. Small, fast, dependency free interpreted string literals

gmailr

Access the Gmail RESTful API from R

hardhat

Construct Modeling Packages

isoband

isoband: An R package to generate contour lines and polygons

keyring

:closed_lock_with_key: Access the system credential store from R

later

Schedule an R function or formula to run after a specified period of time

lifecycle

Manage the life cycle of your exported functions and arguments

lobstr

Understanding complex R objects with tools similar to str()

multilevelmod

Parsnip wrappers for mixed-level and hierarchical models

nanoparquet

R package to read and write Parquet files

open-source-website

Posit Open Source Website

pak

A fresh approach to package installation

pingr

ICMP and TCP ping and related tools

pkgbuild

Find tools needed to build R packages

pkgdepends

R Package Dependency Resolution

pkgload

Simulate installing and loading a package

prettycode

Syntax highlight R code in the terminal

prettyunits

Pretty, human readable formatting of quantities

probably

Tools for post-processing class probability estimates

processx

Execute and Control Subprocesses from R

progress

Progress bar in your R terminal

ragg

Graphic Devices Based on AGG

rappdirs

Find OS-specific directories to store data, caches, and logs. A port of python's AppDirs

rcmdcheck

Run R CMD check from R and collect the results

remotes

Install R packages from GitHub, GitLab, Bitbucket, git, svn repositories, URLs

rules

parsnip extension for rule-based models

sessioninfo

Print Session Information

slider

Sliding Window Functions

supported-by-posit

Add a "Supported by Posit" badge to a package website

systemfonts

System Native Font Handling in R

tune

Tools for tidy parameter tuning

urlchecker

Run CRAN URL checks from older versions of R

usemodels

Boilerplate Code for tidymodels

vdiffr

Visual regression testing and graphical diffing with testthat

watcher

Watch the File System for Changes

webfakes

Fake web apps for HTTP testing R packages

withr

Methods For Temporarily Modifying Global State

workflows

Modeling Workflows

workflowsets

Create a collection of modeling workflows

xml2

Bindings to libxml2

xopen

Open System Files, URLs, Anything

zip

Platform independent zip compression via miniz

PyCon US 2026

PyCon US is the largest and longest-running Python gathering globally

R/Pharma 2025

R/Pharma is a scientifically & industry oriented online conference focused on the use of R in the development of pharmaceuticals

Data visualization with Plotnine

Quick reference guide for data visualization with plotnine

Jeroen Janssens, Hassan Kibirige

Polars: The Blazing Fast Python Framework for Modern Clinical Trial Data Exploration

Polars: The Blazing Fast Python Framework for Modern Clinical Trial Data Exploration - Michael Chow, Jeroen Janssens Abstract: Clinical trials generate complex and standards driven datasets that can slow down traditional data processing tools. This workshop introduces Polars, a cutting-edge Python DataFrame library engineered with a high-performance backend and the Apache Arrow columnar format for blazingly fast data manipulation. Attendees will learn how Polars lays the foundation for the pharmaverse-py, streamlining the data clinical workflow from database querying and complex data wrangling to the potential task of prepping data for regulatory Tables, Figures, and Listings (TFLs). Discover the 'delightful' Polars API and how its speed dramatically accelerates both exploratory and regid data tasks in pharmaceutical drug development. The workshop is led by Michael Chow, a Python developer at Posit who is a key contributor to open-source data tools, notably helping to launch the data presentation library Great Tables, and focusing on bringing efficient data analysis patterns to Python. Resources mentioned in the workshop: * Polars documentation: https://docs.pola.rs/ * Plotnine documentation: https://plotnine.org/ * pyreadstat: https://github.com/Roche/pyreadstat * Examples of Great Tables and Pharma TFLs: https://github.com/machow/examples-great-tables-pharma * UV Python package manager: https://docs.astral.sh/uv

Jeroen Janssens, Michael Chow

Jeroen Janssens - Python Polars: The Definitive Crash Course - PyData Global 2025

Polars is a lightning fast DataFrame library that is taking the data science community by storm. Its elegant and expressive API makes analyses pleasant to write and efficient to run. In this workshop, we’ll demonstrate how Polars enables data scientists to go from raw data to reports–by reading, transforming, and visualizing data

Jeroen Janssens

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: Doing everything in one language is convenient but not always possible. For example, your Python app might need an algorithm only available as an R package. Or your R analysis might need to fit into a Python pipeline. What do you do? You take a polyglot approach! Many data scientists hesitate to explore beyond their main language, but combining R and Python can be powerful. In my talk, I’ll explain why polyglot data science is beneficial and address common concerns. Then, I’ll show you how to make it happen using tools like Quarto, Positron, Reticulate, and the Unix command line. By the end, you’ll gain a fresh perspective and practical ideas to start. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Jeroen Janssens

Untangling Nested JSON With Wes McKinney | PydyTuesday #3

Join Wes McKinney (principal architect at Posit) and Jeroen Janssens (developer relations engineer at Posit) as they dive into deeply nested JSON. Using Python, Positron, Polars, and Altair, they look at scraped GitHub data and create some sort of pulse signal for various GitHub repos. As always, true to the "PydyTuesday Uncut" title, this video is completely unedited. Every typo, mistake, web search, and "aha!" moment is left in so you can see exactly how we approach a new dataset from scratch. Things mentioned during the session and related resources: - Posit Conf https://posit.co/conference/ - The Test Set https://thetestset.co - Positron https://positron.posit.co - Python for Data Analysis, 3E https://wesmckinney.com/book/ - Python Polars: The Definitive Guide https://polarsguide.com - Code produced during the session https://github.com/jeroenjanssens/pydytuesday-uncut/blob/main/session-03/github.py #polars #pydytuesday #datascience

Jeroen Janssens

Visualizing Gas Prices | PydyTuesday Uncut #2

Join Michael Chow (open source developer at Posit) and Jeroen Janssens (developer relations engineer at Posit) as they dive into this week's #PydyTuesday dataset. This time, they visualize gas prices using the four P's: Positron, Python, Polars, and Plotnine. True to the "PydyTuesday Uncut" title, this video is completely unedited. Every typo, mistake, web search, and "aha!" moment is left in so you can see exactly how we approach a new dataset from scratch. Things mentioned during the session and related resources: - Weekly US Gas Prices https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-07-01/readme.md - Code produced during the session https://github.com/jeroenjanssens/pydytuesday-uncut/ - Plotnine https://plotnine.org - Positron https://positron.posit.co #python #polars #tidytuesday #datascience

Jeroen Janssens, Michael Chow

How marimo adds reactivity to your Quarto documents

Jeroen Janssens talks with Vincent Warmerdam about marimo and the Quarto plugin that was recently released. Links: - https://quarto.org - https://marimo.io/ - https://docs.marimo.io/ - https://github.com/marimo-team/quarto-marimo - https://github.com/koaning/wigglystuff - https://anywidget.dev/ 00:00 Introduction 01:19 Getting started with marimo 35:34 ScatterWidget 42:45 Data sources 53:24 Marimo plugin for Quarto

Jeroen Janssens

Jeroen Janssens - Package Your Python Code as a CLI | PyData London 25

www.pydata.org Learn how to transform your Python code into a command-line tool. Jeroen Janssens, author of Data Science at the Command Line, guides you through the process of turning your scripts into reusable, executable tools, integrating them into your data workflows and harnessing the power of the Unix command line. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Jeroen Janssens

Exploring Web APIs | PydyTuesday Uncut #1

Join Michael Chow (open source developer at Posit) and Jeroen Janssens (developer relations engineer at Posit) as they dive into this week's #PydyTuesday dataset about Web APIs. Tools include uv, Positron, Polars, Plotnine, Great Tables, and the Unix command line. True to the "PydyTuesday Uncut" title, this video is completely unedited. Every typo, mistake, web search, and "aha!" moment is left in so you can see exactly how others approach a new dataset from scratch. Things mentioned during the session and related resources: - Code produced during the session: https://github.com/jeroenjanssens/pydytuesday-uncut/blob/main/2025-06-17/01-start.py - PydyTuesday https://github.com/posit-dev/pydytuesday - TidyTuesday https://github.com/rfordatascience/tidytuesday - Getting Data from the TidyTuesday Repo with Python https://www.youtube.com/watch?v=ol2FrSL5gVU - Positron IDE https://positron.posit.co - Data Science at the Command Line https://jeroenjanssens.com/dsatcl/ - Python Polars: The Definitive Guide https://polarsguide.com - Polars https://pola.rs - Plotnine https://plotnine.org - Great Tables https://posit-dev.github.io/great-tables/ - The Big Year https://www.imdb.com/title/tt1053810/ 00:00 Introduction 02:46 Getting the data with uv 13:18 Positron IDE 17:42 Importing Polars 23:17 Plotting a bar chart with Plotnine 33:55 Inspecting duplicates 46:30 Handling missing values 58:56 Crafting a great table 1:38:48 Reflection

Jeroen Janssens, Michael Chow

885: Python Polars: The Definitive Guide — with Jeroen Janssens and Thijs Nieuwdorp

Python #Polars #Pandas Jeroen Janssens and Thijs Nieuwdorp are data frame library Polars’ greatest advocates in this episode with @JonKrohnLearns where they discuss their book, Python Polars: The Definitive Guide, best practice for using Polars, why Pandas users are switching to Polars for data frame operations in Python, and how the library reduces memory usage and compute time up to 10x more than Pandas. Listen to the episode to be a part of an O’Reilly giveaway! This episode is brought to you by: • Trainium2, the latest AI chip from AWS: https://aws.amazon.com/ai/machine-learning/trainium/ • Adverity, the conversational analytics platform: https://eu1.hubs.ly/H0jxK210 • Dell AI Factory with NVIDIA: https://www.dell.com/superdatascience Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • (00:00:00) Introduction • (00:04:46) Why Jeroen and Thijs wrote Python Polars: The Definitive Guide • (00:18:18) Best practices in Polars • (00:25:08) Why Polars has so many users • (00:32:37) The benefits of the Great Tables package • (00:50:05) Jeroen and Thijs’ partnership with NVIDIA and Dell for Python Polars: The Definitive Guide Additional materials: https://www.superdatascience.com/885

Jeroen Janssens

Polars Meetup #1 - Migrating a large codebase to Polars by Jeroen Janssens and Thijs Nieuwdorp

In this community talk, Jeroen Janssens and Thijs Nieuwdorp share their experiences and best practices for migrating a large pandas codebase to Polars at one of the largest utility companies in the Netherlands. By implementing Polars, they achieved a 98% cost reduction. Watch the video to learn how you can start migrating your own codebase. Check our Meetup page to see when the next event is planned: www.meetup.com/polars-meetup/ #polars #dataframe #python #dataengineering #datascience

Jeroen Janssens

KEYNOTE: Dr. Jeroen Janssens - Embrace the Unix Command Line and Supercharge Your PyData Workflow

www.pydata.org Discover why the Unix command line remains a powerful and relevant tool for data scientists, even in a Python-dominated landscape. This talk will demonstrate how embracing the command line and leveraging its many tools can significantly enhance your productivity, streamline data workflows, and complement your Python skills. Jeroen Janssens, PhD, is a polyglot data science consultant and certified instructor. His expertise lies in visualizing data, implementing machine learning models, and building solutions using Python, R, JavaScript, and Bash. Jeroen is passionate about open source and sharing knowledge. He is the author of Data Science at the Command Line (O’Reilly, 2021) and is currently writing Python Polars: The Definitive Guide (O’Reilly, 2025). Every now and then he blogs at https://jeroenjanssens.com. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Jeroen Janssens

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

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Jeroen Janssens, Senior Developer Relations Engineer at Posit, to chat about his career journey from machine learning to developer relations, the advantages of using the command line for data science, his books "Data Science at the Command Line" and "Python Polars", and advice for aspiring DevRel professionals. In this Hangout, we explore the benefits of working on the command line versus not. Jeroen explained that while the initial command line interface might seem stark, it offers a very different and powerful way to interact with your computer. The Unix command line is ubiquitous across various systems, from Raspberry Pis to supercomputers. Its strength lies in the ability to connect tools together through standard output and input, allowing for quick and iterative solutions by combining specialized tools. This fosters an interactive nature with a short feedback loop and provides closer interaction with the file system, making ad hoc data exploration efficient. Resources mentioned in the video and zoom chat: Jeroen's LinkedIn → https://www.linkedin.com/in/jeroenjanssens/ Data Science at the Command Line → https://jeroenjanssens.com/dsatcl/ Python Polars: The Definitive Guide → https://polarsguide.com/ Plotnine → https://plotnine.org/ Winner of the 2024 plotnine Plotting Contest → https://posit.co/blog/winner-of-the-2024-plotnine-plotting-contest/ Talk about plotnine → https://www.youtube.com/watch?v=xdD8r84sqYY R for Data Science → https://r4ds.had.co.nz/ Jeroen's plotnine translation of R for Data Science → https://jeroenjanssens.com/plotnine/ froggeR package → https://azimuth-project.tech/froggeR/ Reticulate → https://rstudio.github.io/reticulate/ Install Windows Subsystem for Linux (WSL) → https://learn.microsoft.com/en-us/windows/wsl/install UTM for macOS (Virtualization) → https://mac.getutm.app fish shell → https://fishshell.com/ Quartodoc → https://github.com/machow/quartodoc Focusmate (Accountability Partner Tool) → https://www.focusmate.com/ Surface Area of Luck → https://modelthinkers.com/mental-model/surface-area-of-luck CRAN R Extensions Manual → https://cran.r-project.org/doc/manuals/r-release/R-exts.html If you didn’t join live, one great thing you missed from the zoom chat was people sharing their varied experiences with the command line, with many admitting they primarily use it for basic navigation or only when necessary, and some sharing helpful tools and tips for those less familiar. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Jeroen Janssens

Jeroen Janssens - How I hacked UMAP and won at a plotting contest | PyData Amsterdam 2024

www.pydata.org In this talk, I’ll share my journey of animating UMAP, a cutting-edge dimensionality reduction algorithm, by visualizing not just its final output but each intermediate step as well. I’ll explain why and how I modified UMAP’s source code, while also demonstrating the use of Polars for data wrangling, Plotnine for visualization, and ffmpeg for animation. The result ultimately earned me a runner-up position in the 2024 Plotnine plotting contest. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 01:06 Plotnine contest 02:18 Hoi! I'm Jeroen 03:13 Visualizing the behavior of an algorithm 07:58 Keeping track of intermediate predictions 09:23 Tool: UMAP 12:31 Tool: Polars 13:15 Python Polars: The Definitive Guide 13:40 MNIST dataset 14:46 Tool: Plotnine 17:04 Tool: FFmpeg 18:58 Final result 19:38 To conclude 20:40 Q & A

Jeroen Janssens

Seeing Beyond Statistics: Anscombe's Quartet and the Power of Graphs

Jeroen Janssens