tidymodels

Strategic Budget Optimization through Marketing Mix Modeling (MMM)

Stop guessing which channels drive growth.

Join Isabella Velásquez and Daniel Chen for this end-to-end workflow demo on modeling marketing spend, building ‘what-if’ scenario planners in Shiny, and deploying automated Quarto reports that give non-technical stakeholders a clear, data-backed view of ROI.

We will explore:

1. The Workflow Architecture: A look at the “plumbing” required to move from data to insights.
2. The Interface: Wrapping an existing model in a Shiny dashboard for real-time “what-if” scenario planning.
3. The Delivery: Automating communication via Quarto reports to keep stakeholders aligned.
4. The Deployment: Using Posit Connect to turn your code into an accessible business asset for leaders.

This workflow demo grew out of the incredible energy at our recent Data Science Hangout with Ryan Timpe at LEGO. We were so inspired by the community’s interest in MMM that we decided to build this session to address your specific questions. Thank you for the great discussion. We’re excited to show you what we’ve put together!

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:

Precision Medicine for All: Using Tidymodels to Validate PRS in Brazil (Flávia Rius) | posit::conf

Precision Medicine for All: Using Tidymodels to Validate Breast Cancer PRS in Brazil

Speaker(s): Flávia E. Rius

Abstract:

The Power of Snowflake and Posit Workbench (Jonathan Regenstein, Snowflake) | posit::conf(2025)

The Power of Snowflake and Posit Workbench: Macroeconomic Data Exploration in the Cloud

Speaker(s): Jonathan Regenstein

Abstract:

In this talk, we will utilize the Posit Workbench Native App to demonstrate how macroeconomic research can be run in the Snowflake cloud, powered by R & RStudio.

Data Science in the Energy Industry | Frank Hull | Data Science Hangout

Deploying Scikit-learn models for in-database scoring with Snowflake and Posit Team

Modern data science workflows often face a critical challenge: how to efficiently deploy machine learning models where the data lives. Traditional approaches require moving large datasets out of the database for scoring, creating bottlenecks, security concerns, and unnecessary data movement costs.

In this demo, Nick Pelikan at Posit highlights how orbital bridges the gap between Python’s rich ML ecosystem and database-native execution by converting scikit-learn pipelines to optimized SQL. This enables data scientists to:

1. Use familiar Python tools for model development
2. Automatically translate complex feature engineering to SQL
3. Deploy models without rewriting code in different languages
4. Maintain model accuracy through faithful SQL translations

Easier data and asset sharing across projects and teams with {pins} and Databricks

Led by Edgar Ruiz, Software Engineer at Posit PBC April 30th at 11 am ET / 8 am PT

Sharing data assets can be challenging for many teams. Some may rely on emailed files to keep analyses up to date, making it difficult to keep current or know what version of the data is used. {pins} improves sharing data and other assets across projects and teams. It enables us to publish, or ‘pin’, to a variety of places, such as Amazon S3, Posit Connect and Dropbox.

Given recent customer feedback, the ability to publish, or ‘pin’ to Databricks Volumes has been added to R. The same capability is also currently in the works for the Python version of {pins}.

This session on April 30th will showcase the acceleration of predictions by distributing a ‘pinned’ model using pins and Spark in Databricks. We’ll walk through integrating {pins} with Databricks in your team’s projects and cover novel uses of pins inside the Databricks ecosystem.

Standardizing a safety model with tidymodels, Posit Team & Databricks at Suffolk Construction

If you’ve ever struggled with standardizing machine learning workflows, ensuring secure data access, or scaling insights across your organization, this month’s Posit Team Workflow demo is for you.

Maxwell Patterson, Data Scientist at Suffolk walked us through how their team is:

Standardizing model workflows using tidymodels, vetiver, Shiny, and Quarto Leveraging row-level permissions in Shiny apps to improve data governance Using Databricks and Posit to gain insights faster and more securely

The Power of Snowflake and Posit Workbench: Macroeconomic Data Exploration in the Cloud

In this live event, we will utilize the Posit Workbench Native App to demonstrate that macroeconomic research can be run in the Snowflake cloud but powered by R and RStudio.

Starting with data sourced from the Snowflake marketplace, we will import, transform, visualize, and, finally, model data using the Orbital framework to push tidymodels down to the cloud. This is full-stack, R-driven macroeconomic research in the cloud.

Wes McKinney & Hadley Wickham (on cross-language collaboration, Positron, career beginnings, & more)

We hosted a special event hosted by Posit PBC with Wes McKinney (Pandas & Apache Arrow) and Hadley Wickham (rstats & tidyverse) to ask questions, share your thoughts, and exchange insights about cross-language collaboration with fellow data community members.

Here’s a preview into what came up in conversation:

1. Cross-language collaboration between R and Python
2. Positron, a new polyglot data science IDE
3. Open source development, how Wes and Hadley got involved in open source and their experiences in building and maintaining open-source projects such as Pandas and the tidyverse.
4. Documentation for R and Python, especially in the context of teams that use both languages (shoutout to Quarto!)
5. The use of LLMs in data science
6. The emergence of libraries like Polars and DuckDB
7. Challenges of switching between the two languages
8. Package development and maintenance for polyglot teams that have internal packages in both languages
9. The future of data science

The chat was on fire for this conversation and we’ve gathered most of the links shared among the community below:

Tidymodel prediction workflows inside databases with orbital and Snowflake

Nick Pelikan, Senior Solution Architect at Posit highlighted how you can:

1. Fit and deploy R models directly in Snowflake
2. Use Snowflake’s compute to drastically reduce model runtime
3. Save and share models across your team, making them accessible through tools like Snowsight and Python

This end-to-end workflow makes it easier to implement models and scale insights across your organization using Posit and Snowflake.

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:

Emil Hvitfeldt - Tidypredict with recipes, turn workflow to SQL, spark, duckdb and beyond

Tidypredict is one of my favorite packages. Being able to turn a fitted model object into an equation is very powerful! However, in tidymodels, we use recipes more and more to do preprocessing. So far, tidypredict didn’t have support for recipes, which severely limited its uses. This talk is about how I fixed that issue. After spending a couple of years thinking about this problem, I finally found a way! Being able to turn a tidymodels workflow into a series of equations for prediction is super powerful. For some uses, being able to turn a model to predict inside SQL, spark or duckdb allows us to handle some problems with more ease.

Talk by Emil Hvitfeldt

Hannah Frick - tidymodels for time-to-event data

Time-to-event data can show up in a broad variety of contexts: the event may be a customer churning, a machine needing repairs or replacement, a pet being adopted, or a complaint being dealt with. Survival analysis is a methodology that allows you to model both aspects, the time and the event status, at the same time. tidymodels now provides support for this kind of data across the framework.

Talk by Hannah Frick

Max Kuhn - Evaluating Time-to-Event Models is Hard

Censoring in data can frequently occur when we have a time-to-event. For example, if we order a pizza that has not yet arrived after 5 minutes, it is censored; we don’t know the final delivery time, but we know it is at least 5 minutes. Censored values can appear in clinical trials, customer churn analysis, pet adoption statistics, or anywhere a duration of time is used. I’ll describe different ways to assess models for censored data and focus on metrics requiring an evaluation time (i.e., how well does the model work at 5 minutes?). I’ll also describe how you can use tidymodel’s expanded features for these data to tell if your model fits the data well. This talk is designed to be paired with the other tidymodels talk by Hannah Frick.

Talk by Max Kuhn

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

Live Q&A following Workflow Demo - June 26th

Please join us for the live Q&A session for the June 26th Workflow Demo - this will start immediately following the demo.

Predicting Lending Rates with Databricks, tidymodels, and Posit Team

Predicting Lending Rates with Databricks, tidymodels, and Posit Team

Machine learning algorithms are reshaping financial decision-making, changing how the industry manages financial risk.

For our workflow demo today on June 26th at 11 am ET, Garrett Grolemund at Posit will show how to use both Posit and Databricks to apply machine learning methods to the consumer credit market, where accurately predicting lending rates is critical for customer acquisition.

*Please note that while the workflow focuses on a financial example, the general workflow will be useful to those using Databricks and R together across any industry.

During this workflow demo, you will learn how to:

1. Connect to historical lending rate data stored in Databricks Delta Lake
2. Tune and cross-validate a penalized linear regression (LASSO) that predicts interest rates
3. Select variables with the penalized linear regression model (LASSO)
4. Build an interactive Shiny app to provide a customer-facing user interface for our model
5. Deploy the app to production on Posit Connect, and arrange for the app to access Databricks

Conformal Inference with Tidymodels - posit::conf(2023)

Presented by Max Kuhn

Conformal inference theory enables any model to produce probabilistic predictions, such as prediction intervals. We’ll demonstrate how these analytical methods can be used with tidymodels. Simulations will show that the results have good coverage (i.e., a 90% interval should include the real point 90% of the time).

Making a (Python) Web App is easy! - posit::conf(2023)

Presented by Marcos Huerta

Making Python Web apps using Dash, Streamlit, and Shiny for Python

This talk describes how to make distribution-free prediction intervals for regression models via the tidymodels framework.

By creating and deploying an interactive web application you can better share your data, code, and ideas easily with a broad audience. I plan to talk about several Python web application frameworks, and how you can use them to turn a class, function, or data set visualization into an interactive web page to share with the world. I plan to discuss building simple web applications with Plotly Dash, Streamlit, and Shiny for Python.

Materials:

Open Source Property Assessment: Tidymodels to Allocate $16B in Property Taxes - posit::conf(2023)

Presented by Nicole Jardine and Dan Snow

How the Cook County Assessor’s Office uses R and tidymodels for its residential property valuation models.

The Cook County Assessor’s Office (CCAO) determines the current market value of properties for the purpose of property taxation. Since 2020, the CCAO has used R, tidymodels, and LightGBM to build predictive models that value Cook County’s 1.5 million residential properties, which are collectively worth over $400B. These predictive models are open-source, easily replicable, and have significantly improved valuation accuracy and equity over time.

Join CCAO Chief Data Officer Nicole Jardine and Director of Data Science Dan Snow as they walk through the CCAO’s modeling process, shares lessons learned, and offer a sneak peek at changes planned for the 2024 reassessment of Chicago.

tidymodels: Adventures in Rewriting a Modeling Pipeline - posit::conf(2023)

Presented by Ryan Timpe

An overview of the benefits unlocked on our data science team by adopting tidymodels.

Data science sure has changed over the past few years! Everyone’s talking about production. RStudio is now Posit. Models are now tidy.

This talk is about embracing that change and updating existing models using the tidymodels framework. I recently completed this change, letting go of our in-production code and revisioning it with tidymodels. My team ended up with a faster, more scalable pipeline that enabled us to better automate our workflow and increase our scale while improving our stakeholders’ experiences.

I’ll share tips and tricks for adopting the tidymodels framework in existing products, best practices for learning and upskilling teams, and advice for using tidymodel packages to build more accessible data science tools.

webR 0.2: R Packages and Shiny for WebAssembly | George Stagg | Posit

WebR makes it possible to run R code in the browser without the need for an R server to execute the code: the R interpreter runs directly on the user’s machine. But just running R isn’t enough, you need the R packages you use every day too.

webR 0.2.0 makes many new packages available (10,324 packages - about 51% of CRAN!) and it’s now possible to run Shiny apps under webR, entirely client side.

George Stagg shares how to load packages with webR, know what ones are available, and get started running Shiny apps in the web browser. There’s a demo webR Shiny app too!

00:15 Loading R packages with webR 01:50 Wasm system libraries available for use with webR 05:30 Tidyverse, tidymodels, geospatial data, and database packages available 08:00 Shiny and httpuv: running Shiny apps under webR 11:05 Example Shiny app running in the web browser 12:05 Links with where to learn more

Bite-sized tricks for machine learning with tidymodels | Posit

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This video highlights a number of tidymodels features that could improve your modeling workflows.

0:03 Switching modeling engines is easy 0:21 Never lose your tuning results 0:36 Built-in visualizations for modeling objects 1:03 Grouped resampling 1:16 Case weights 1:32 Select variables based on role and type 2:00 Spatial resampling 2:16 Keep your tidymodels objects small

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

Workflow Demo Q&A - Oct 25th

Live Q&A for the end-to-end machine learning workflow with tidymodels & Posit Team on October 25th.

Emil Hvitfeldt - Slidecraft: The Art of Creating Pretty Presentations

Slidecraft: The Art of Creating Pretty Presentations by Emil Hvitfeldt

posit::conf(2023) Workshop: Advanced tidymodels

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

Hannah Frick | Censored - Survival Analysis in Tidymodels | Posit (2022)

tidymodels is extending support for survival analysis and censored is a new parsnip extension package for survival models. It offers various types of models: parametric models, semi-parametric models like the Cox model, and tree- based models like decision trees, boosted trees, and random forests. They all come with the consistent parsnip interface so that you can focus on the modelling instead of details of the syntax. Happy modelling!

Julia Silge & Max Kuhn | Good Practices for Applied Machine Learning | Posit (2022)

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. Whether you are just starting out today or have years of experience with ML, tidymodels offers a consistent, flexible framework for your work. In this talk, learn how tidymodels has been designed to promote ergonomic, effective, and safe modeling practice. We will discuss how to think about the steps of building a model from beginning to end, how to fluently use different modeling and feature engineering approaches, how to avoid common pitfalls of modeling like overfitting and data leakage, and how to version and deploy reliable models trained in R.

Kelly Bodwin | Translating from {tidymodels} and scikit-learn: Lessons from a ‘bilingual’ course

The friendly competition between R and python has gifted us with two stellar packages for workflow-style predictive modeling: tidymodels in R, and scikit- learn in python. When I had to choose between them for a Machine Learning Course, I said: ¿Porque no los dos? (Why not both?)

In this talk, I will share how the differences in structure and syntax between tidymodels and scikit-learn impacted student understanding. Can a helper function hide an important decision about tuning parameters? Can a slight change in argument input influence the way we describe a model? The answer is a resounding, “¡Sí!”

Don’t despair, though, because I will also provide advice for avoiding pitfalls when switching between languages or implementations. Together, let’s think about the power that programming choices has to shape the mental model of the user, and the ways that we can responsibly document our modeling decisions to increase cross-language reproducibility.

Data Science Hangout | Michael Chow, Posit | Exploring Team Structure w/ Data Scientists & Engineers

We were joined by Michael Chow, Data Scientist and Software Engineer at RStudio. Michael also previously led a team at the California Integrated Travel Project.

On this week’s hangout there were a lot of thoughts shared on structuring a data science team from both Michael and the broader group:

Julia Silge | Monitoring Model Performance | RStudio

0:00 Project introduction 1:50 Overview of the setup code chunk 3:05 Getting new data 4:05 Getting model from RStudio Connect using httr and jsonlite 6:20 Bringing in metrics 9:45 Using the pins package 10:50 Using boards on RStudio Connect 13:30 Benefits of using pins 14:00 Visualizations using ggplot and plotly 17:00 Knitting the flexdashboard 18:10 Project takeaways

Max Kuhn | parsnip A tidy model interface | RStudio (2019)

parsnip is a new tidymodels package that generalizes model interfaces across packages. The idea is to have a single function interface for types of specific models (e.g. logistic regression) that lets the user choose the computational engine for training. For example, logistic regression could be fit with several R packages, Spark, Stan, and Tensorflow. parsnip also standardizes the return objects and sets up some new features for some upcoming packages.