Simon Couch

dplyr

dplyr: A grammar of data manipulation

positron

Positron, a next-generation data science IDE

recipes

Pipeable steps for feature engineering and data preprocessing to prepare for modeling

shinychat

Chat UI component for Shiny

tidymodels

Easily install and load the tidymodels packages

roxygen2

Generate R package documentation from inline R comments

rstudio

RStudio is an integrated development environment (IDE) for R

ellmer

Call LLM APIs from R

tidyverse.org

Source of tidyverse.org

cli

Tools for making beautiful & useful command line interfaces

btw

A complete toolkit for connecting R and LLMs

mcptools

Model Context Protocol For R

vitals

Large language model evaluation for R

odbc

Connect to ODBC databases (using the DBI interface)

dbplyr

Database (DBI) backend for dplyr

rlang

Low-level API for programming with R

yardstick

Tidy methods for measuring model performance

tidypredict

Run predictions inside the database

TMwR

Code and content for "Tidy Modeling with R"

finetune

Additional functions for model tuning

parsnip

A tidy unified interface to models

rsample

Classes and functions to create and summarize resampling objects

infer

An R package for tidyverse-friendly statistical inference

ark

Ark, an R kernel

bonsai

parsnip wrappers for tree-based models

broom

Convert statistical analysis objects from R into tidy format

bundle

Prepare objects for serialization with a consistent interface

butcher

Reduce the size of model objects saved to disk

censored

Parsnip wrappers for survival models

dials

Tools for creating tuning parameter values

discrim

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

embed

Extra recipes for predictor embeddings

hardhat

Construct Modeling Packages

hex-stickers

RStudio hex stickers

probably

Tools for post-processing class probability estimates

rules

parsnip extension for rule-based models

shinymodels

stacks

An R package for tidy stacked ensemble modeling

tidy-dev-day

Tidyverse developer day

tune

Tools for tidy parameter tuning

workflows

Modeling Workflows

workflowsets

Create a collection of modeling workflows

workshops

Website and materials for tidymodels workshops

R/Pharma 2025

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

useR! 2025

useR! 2025 brings together R users, developers, and enthusiasts from around the world for three days of tutorials, presentations, and networking

How to deploy Shiny apps in 2026 | Alex Chisholm | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We'd love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Posit product manager Alex Chisholm as he walks through the evolution of shiny app deployment over the years, how to deploy shiny apps in the modern era, and peeks into Posit's roadmap for future development. Do you call it "deployment" or "publishing" when it comes to Shiny apps? 🤔 This is a super friendly and conversational space, and being there live in the Discord chat can't be beat!! We hope you get to join us sometime soon. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Alex Chisholm Alex Chisholm's LinkedIn: http://www.linkedin.com/in/chisholm1 Resources from the hosts and chat: Posit Connect Cloud for deploying Shiny apps in the modern era: https://connect.posit.cloud/ Install Positron: https://positron.posit.co/ Simon Couch's blog post on local LLMs not being good enough yet: https://www.simonpcouch.com/blog/2025-12-04-local-agents/ Blue-Green Shiny App Deployments using Posit Connect posit::conf(2025) talk by Ryszard Szymański: https://youtu.be/QEEGLWj0nas Digital Ocean: https://www.digitalocean.com/ Ollama local LLM: https://ollama.com/ py-sidebot app template: https://shiny.posit.co/py/templates/sidebot/ querychat app template: https://shiny.posit.co/py/templates/querychat/ Dan Chen mentioned Render in the chat as an alternative to Digital Ocean: https://render.com/ Alex Chisholm's AB testing GitHub repo example: https://github.com/alex-chisholm/shiny-r-abtesting Edward in the chat shared a GitHub repo for using GitHub actions to execute remote SSH commands: https://github.com/appleboy/ssh-action Abu in the chat shared blue-green vs. canary deployments: https://octopus.com/devops/software-deployments/blue-green-vs-canary-deployments/ Frank in the chat mentioned Simon's blog on using local LLMs with the chores package: https://www.simonpcouch.com/blog/2025-12-10-chores-0-3-0/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 03:03 Meaningful applications and value creation 05:31 The evolution of Shinyapps.io and Posit Connect 08:12 DigitalOcean and Droplets 09:36 DigitalOcean vs. commercial cloud providers, 11:48 Comparisons: DigitalOcean, Azure, and AWS 14:47 Replicating local environments with Docker 16:51 The open-source Shiny Server 18:20 Use case: University of Illinois CITL 20:02 Key considerations for deployment decisions 21:53 GitHub Actions and version control 23:31 Addressing single points of failure and maintainability 24:38 Posit Connect Cloud features and portfolio 26:01 Beyond Shiny: Quarto, Streamlit, and Dash 27:07 Handling secrets and database credentials 28:56 Custom vanity links vs. UUIDs 30:04 Blue-Green deployment strategies 31:55 "Is it easy to set up a developer workflow?" 34:46 Guardrails for AI powered apps and token usage 37:32 Small language models and Ollama 38:29 Sidebot AI demo and LLM integration 39:41 Understanding manifest.json and dependencies 45:00 Automatic publish on GitHub push 46:51 The future of Shinyapps.io and migration 48:33 "Did you just build a custom agent for that specific dashboard?" 51:43 Publishing from RStudio IDE to Connect Cloud 54:16 Preview: Inspecting website APIs for data harvesting

Simon Couch

Simon Couch - Practical AI for data science

Practical AI for data science (Simon Couch) Abstract: While most discourse about AI focuses on glamorous, ungrounded applications, data scientists spend most of their days tackling unglamorous problems in sensitive data. Integrated thoughtfully, LLMs are quite useful in practice for all sorts of everyday data science tasks, even when restricted to secure deployments that protect proprietary information. At Posit, our work on ellmer and related R packages has focused on enabling these practical uses. This talk will outline three practical AI use-cases—structured data extraction, tool calling, and coding—and offer guidance on getting started with LLMs when your data and code is confidential. Presented at the 2025 R/Pharma Conference Europe/US Track. Resources mentioned in the presentation: - {vitals}: Large Language Model Evaluations https://vitals.tidyverse.org/ - {mcptools}: Model Context Protocol for R https://posit-dev.github.io/mcptools/ - {btw}: A complete toolkit for connecting R and LLMs https://posit-dev.github.io/btw/ - {gander}: High-performance, low-friction Large Language Model chat for data scientists https://simonpcouch.github.io/gander/ - {chores}: A collection of large language model assistants https://simonpcouch.github.io/chores/ - {predictive}: A frontend for predictive modeling with tidymodels https://github.com/simonpcouch/predictive - {kapa}: RAG-based search via the kapa.ai API https://github.com/simonpcouch/kapa - Databot https://positron.posit.co/dat

Simon Couch

Small boosts here and there - Simon Couch

Rather than writing an entire R package or carrying out a data analysis in one fell swoop, I’m interested in large language models (LLMs) doing things for me that I don’t like to do: tedious little refactors, transitioning from deprecated APIs, and templating out boilerplate. Chores, if you will. This talk introduces chores, an R package implementing an extensible library of LLM assistants to help with repetitive but hard-to-automate tasks. I’ll demonstrate that LLMs are quite good at turning some 45-second tasks into 5-second ones and show you how to start automating drudgery from your work with a markdown file and a dollar on an API key

Simon Couch

Simon Couch - From hours to minutes: Accelerating your tidymodels code

From hours to minutes: Accelerating your tidymodels code - Simon Couch Abstract: This talk demonstrates a 145-fold speedup in training time for a machine learning pipeline with tidymodels through 4 small changes. By adapting a grid search on a canonical model to use a more performant modeling engine, hooking into a parallel computing framework, transitioning to an optimized search strategy, and defining the grid to search over carefully, users can drastically cut down on the time to develop machine learning models with tidymodels without sacrificing predictive performance. Resources mentioned in the talk: - Presentation slides https://simonpcouch.github.io/rpharma-24/#/ - GitHub repository for talk https://github.com/simonpcouch/rpharma-24 - Efficient Machine Learning with R: Low-Compute Predictive Modeling with tidymodels https://emlwr.org - Optimizing model parameters faster with tidymodels https://www.simonpcouch.com/blog/2023-08-04-parallel-racing/ Presented at the 2024 R/Pharma Conference

Simon Couch

Simon Couch - Fair machine learning

In recent years, high-profile analyses have called attention to many contexts where the use of machine learning deepened inequities in our communities. After a year of research and design, the tidymodels team is excited to share a set of tools to help data scientists develop fair machine learning models and communicate about them effectively. This talk will introduce the research field of machine learning fairness and demonstrate a fairness-oriented analysis of a machine learning model with tidymodels. Talk by Simon Couch Slides: https://simonpcouch.github.io/conf-24 GitHub Repo: https://github.com/simonpcouch/conf-24

Simon Couch

Simon Couch: Fair machine learning

Simon Couch Fair machine learning Cascadia R Conf 2024 Regular talk, 10:25-10:40 In recent years, high-profile analyses have called attention to many contexts where the use of machine learning deepened inequities in our communities. A machine learning model resulted in wealthy homeowners being taxed at a significantly lower rate than poorer homeowners; a model used in criminal sentencing disproportionately predicted black defendants would commit a crime in the future compared to white defendants; a recruiting and hiring model penalized feminine-coded words—like the names of historically women's colleges—when evaluating résumés. In late 2022, a group of Posit employees across teams, roles, and technical backgrounds formed a reading group to engage with literature on machine learning fairness, a research field that aims to define what it means for a statistical model to act unfairly and take measures to address that unfairness. We then designed functionality and resources to help data scientists measure and critique the ways in which the machine learning models they've built might disparately impact people affected by that model. This talk will introduce the research field of machine learning fairness and demonstrate a fairness-oriented analysis of a model with tidymodels, a framework for machine learning in R. Pronouns: he/him Chicago, IL Simon Couch is a software engineer at Posit PBC (formerly RStudio) where he works on open source statistical software. With an academic background in statistics and sociology, Simon believes that principled tooling has a profound impact on our ability to think rigorously about data. He authors and maintains a number of R packages and blogs about the process at simonpcouch.com

Simon Couch

How to train, evaluate, and deploy a machine learning workflow with tidymodels & Posit Team

Helpful resources: Github: https://github.com/simonpcouch/mutagen Follow-up Q&A Session: https://youtube.com/live/vwBVOBQfc_U If you want to book a call with our team to chat more about Posit products: pos.it/chat-with-us Don't want to meet, but curious who else on your team is using Posit? pos.it/connect-us Blog post on tidymodels + Posit Connect: https://posit.co/blog/pharmaceutical-machine-learning-with-tidymodels-and-posit-connect/ Tidy Modeling with R book: https://www.tmwr.org/ Timestamps: 1:44 - Three steps for developing a machine learning model 3:35 - What is a machine learning model? 7:02 - Overview of machine learning with Posit Team 7:36: Step 1: Understand and clean data 11:05 - Step 2: Train and evaluate models (why you might be interested using tidymodels) 23:02 - Step 3: Deploying a machine learning model from Posit Workbench to Posit Connect 30:14 - Summary 31:21 - Helpful resources __________________ Machine learning models are all around us, from Netflix movie recommendations to Zillow property value estimates to email spam filters. As these models play an increasingly large role in our personal and professional lives, understanding and embracing them has never been more important; machine learning helps us make better, data-driven decisions. The tidymodels framework is a powerful set of tools for building—and getting value out of—machine learning models with R. Data scientists use tidymodels to: 1. Gain access to a wide variety of machine learning methods 2. Guard against common mistakes 3. Easily deploy models through tidymodels’ integration with vetiver Join Simon Couch from the tidyverse team on Wednesday, October 25th at 11am ET as he walks through an end-to-end machine learning workflow with Posit Team. No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo

Simon Couch

posit::conf(2023) Workshop: Introduction to tidymodels

Register now: http://pos.it/conf Instructors: Hannah Frick, Simon Couch, Emil Hvitfeldt Workshop Duration: 1-Day Workshop This workshop is for you if you: • have intermediate R knowledge, experience with tidyverse packages, and either of the R pipes • can read data into R, transform and reshape data, and make a wide variety of graphs • have had some exposure to basic statistical concepts such as linear models, random forests, etc. Intermediate or expert familiarity with modeling or machine learning is not required. This workshop will teach you core tidymodels packages and their uses: data splitting/resampling with rsample, model fitting with parsnip, measuring model performance with yardstick, and basic pre-processing with recipes. Time permitting, you’ll be introduced to model optimization using the tune package. You’ll learn tidymodels syntax as well as the process of predictive modeling for tabular data

Emil Hvitfeldt, Hannah Frick, Simon Couch

Simon Couch | tidymodels/stacks: A Grammar for Stacked Ensemble Modeling | RStudio

Full title: tidymodels/stacks, Or, In Preparation for Pesto: A Grammar for Stacked Ensemble Modeling Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole. About Simon: Simon Couch is an R developer and statistics student at Reed College, where he is entering the final semester of his undergraduate degree. He co-authors and maintains R packages including broom, infer, and stacks, leads trainings and workshops as an RStudio-certified tidyverse trainer, and researches in algorithmic data privacy. He interned on the RStudio tidymodels team in summer 2020, and is currently applying to doctoral programs in statistics

Simon Couch