R/Pharma 2025

Turbocharge your Shiny Apps with remote submission to HPC

Turbocharge your Shiny Apps with remote submission to HPC - Michael Mayer

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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.

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How to use {pointblank} to understand, validate, and document your data

How to use {pointblank} to understand, validate, and document your data - Rich Iannone

Abstract: This workshop will focus on the data quality and data documentation workflows that the pointblank package makes possible. We will use functions that allow us to: (1) quickly understand a new dataset (2) validate tabular data using rules that are based on our understanding of the data (3) fully document a table by describing its variables and other important details. The pointblank package was created to scale from small validation problems (“Let’s make certain this table fits my expectations before moving on”) to very large (“Let’s validate these 35 database tables every day and ensure data quality is maintained”) and we’ll delve into all sorts of data quality scenarios so you’ll be comfortable using this package in your organization. Data documentation is seemingly and unfortunately less common in organizations (maybe even less than the practice of data validation). We’ll learn all about how this doesn’t have to be a tedious chore. The pointblank package allows you to create informative and beautiful data documentation that will help others understand what’s in all those tables that are so vital to an organization.

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Getting Started with LLM APIs in R

Getting Started with LLM APIs in R - Sara Altman

Abstract: LLMs are transforming how we write code, build tools, and analyze data, but getting started with directly working with LLM APIs can feel daunting. This workshop will introduce participants to programming with LLM APIs in R using ellmer, an open-source package that makes it easy to work with LLMs from R. We’ll cover the basics of calling LLMs from R, as well as system prompt design, tool calling, and building basic chatbots. No AI or machine learning background is required—just basic R familiarity. Participants will leave with example scripts they can adapt to their own projects.

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Creating Polished, Branded Documents with Quarto

Creating Polished, Branded Documents with Quarto - Isabella Velasquez

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Max Kuhn - TabPFN: A Deep-Learning Solution for Tabular Data

TabPFN: A Deep-Learning Solution for Tabular Data (Max Kuhn)

Abstract: There have been numerous proposals for deep neural networks for tabular data, such as rectangular data sets (e.g., data frames). To date, none have really worked well and take far too long to train. TabPFN is a model that emulates a Bayesian approach and trains a deep learning model on a prior of simulated tabular datasets. Version 2 was released this year and offers several significant advantages, but also has one notable disadvantage. I’ll introduce this model and show an example.

Presented at the 2025 R/Pharma Conference Europe/US Track.

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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.

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{shinylive}: Serverless Shiny applications workshop

{shinylive}: Serverless Shiny applications workshop: An exercise in deploying your app to GitHub Pages - Barret Schloerke

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Tables in Python with Great Tables

Tables in Python with Great Tables - Rich Iannone, Michael Chow

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Positron Assistant for Developing Shiny Apps - Tom Mock

Positron Assistant for Developing Shiny Apps - Tom Mock (Posit)

Abstract: This talk will explore building AI Apps with a focus on Positron Assistant for Shiny developer experience and in-IDE tooling for accelerating app creation. This talk will discuss tools like ellmer / chatlas / querychat / shinychat and compare it to Positron Assistant.

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Building the Future of Data Apps: LLMs Meet Shiny

GenAI in Pharma 2025 kicks off with Posit’s Phil Bowsher and Garrick Aiden-Buie sharing a technical overview of how LLMs can integrate with Shiny applications and much more!

Abstract: When we think of LLMs (large language models), usually what comes to mind are general purpose chatbots like ChatGPT or code assistants like GitHub Copilot. But as useful as ChatGPT and Copilot are, LLMs have so much more to offer—if you know how to code. In this demo Garrick will explain LLM APIs from zero, and have you building and deploying custom LLM-empowered data workflows and apps in no time.

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