Shiny in Production 2025: Full Length Talks
We are pleased to announce the full line-up for this year’s Shiny in Production conference! The conference includes nine full-length talks (25 minutes each) and a lightning talk session (5 minutes per talk), we’ll cover those in a separate blog.
Talks
Cameron Race - Head of Children and Schools Statistics and Product Manager
shinyGovstyle: A ‘Shiny’ Secret Weapon for Production-Ready Government Public Services
In the UK, we are required to make public sector websites accessible to all users. While there is a wealth of UK government data publicly available through a number of existing digital services, it can be tough to engage with. Government analysts are increasingly turning to R Shiny to enhance their data dissemination, making it more engaging for users, but with hundreds of analysts working in silos across government, how can analysts build full digital services in a way that carries the same consistency, trustworthiness and authority as a domain such as GOV.UK?
Charlie Gao - Posit Software, PBC
Advances in the Shiny Ecosystem
Charlie Gao, Senior Software Engineer on Posit’s open source team will review some of the latest high-performance async tooling developed by Posit to support R Shiny in terms of performance, scalability and user experience.
Colin Fay - ThinkR
After {shiny} — Bringing R to Mobile with webR
As the use of mobile devices becomes increasingly central to how users interact with data products, the R community has long sought ways to bring R-powered applications into the mobile space. Historically, this has meant adapting {shiny} apps for smaller screens—either through responsive design or packages like {shinyMobile}. While effective for certain use cases, these approaches are fundamentally web-based, requiring a server and a stable internet connection, and lacking access to native device features.
This talk presents a new path forward: Rlinguo, a fully native mobile application built with webR, a version of R compiled to WebAssembly. Unlike traditional {shiny}-based solutions, Rlinguo runs R directly on the device, without a server. It works offline, stores data locally, and can leverage native mobile APIs—pushing the boundaries of what’s possible with R in a mobile context.
Through this case study, we’ll explore the architecture behind Rlinguo, contrast it with the {shiny} model, and discuss what it means for the future of R development. Topics will include:
What it takes to embed R in a mobile app using webR
Technical and design trade-offs between web-based and native solutions
Practical applications for offline, device-integrated R tools
Whether you’re building with {shiny} today or simply curious about the next evolution of R in production, this session offers a look at where R can go when it steps beyond the browser.
Gabriela De Lima Marin - Brazilian Network Information Centre
A Collaborative Initiative for Mapping and Georeferencing Public Schools in Brazil
This project presents a collaborative initiative aimed at improving the geolocation accuracy of Brazilian public schools through an interactive Shiny web application.
By integrating existing location data from the Brazilian School Census with APIs from Google, Microsoft, and OpenStreetMap, we established an innovative workflow to assign accurate geographic coordinates to schools previously lacking precise location data.
The Shiny application provides a user-friendly interface allowing school administrators and education managers to visually verify and manually adjust school locations via interactive maps. Over the past two years, this approach enabled the precise geolocation of previously unlocated schools and significantly enhanced the accuracy of geolocation data of schools.
The geolocation data collected and validated through this project will be openly shared with relevant governmental stakeholders, promoting transparency and supporting evidence-based decision-making. Moreover, the project exemplifies how collaborative data science and innovative web technology—particularly R Shiny—can be effectively leveraged in public administration, enabling managers, stakeholders, and the community to directly contribute to data accuracy and positively influence educational outcomes in Brazil.
Jack Anderson - National Disease Registration Service, NHS England
Transforming the reporting of national patient outcomes with Shiny: 30-day mortality post-Systemic Anti-Cancer Therapy
In June 2020, the National Disease Registration Service began reporting 30-day mortality post-Systemic Anti-Cancer Therapy (SACT) Case-Mix Adjusted Rates (CMAR) to NHS trusts in England. This work applies logistic regression to report trust-level case-mix adjusted 30-day mortality rates, which enable comparisons between trusts and with the national average. Historically, results were shared as an Excel workbook with an accompanying companion brief and FAQ document, and each report was shared in isolation from previous releases. Since April 2023, implementation of R Shiny has enabled 30-day mortality rates to be reported seamlessly on an interactive, publicly accessible dashboard. Utilising the Plotly and DT packages, dynamic funnel plots and data tables are tailored to user needs through Shiny input pickers, which reactively subset and summarise data visualisations based on user selections.
This enables NHS trust users to flexibly review their 30-day mortality outcomes against those of other trusts, their wider Cancer Alliance, and national averages, both overall and stratified by key patient demographics.
The Shiny dashboard also enables users to view current and previous CMAR reports together in one place and includes download button functionality for documentation and underlying data. With dedicated tabs for summary data, trust exclusions, and trust response statements, Shiny allows for end-to-end exploration of CMAR outcomes, making it easier for users to gain insight into clinical practice. The resulting Shiny dashboard supports clinical governance within trusts and enables clinical colleagues to better understand their patient outcomes within their wider context.
Laura Mawer & Marcus Palmer - Datacove, Harrison-Palmer Limited
Using Shiny for Python to Power AI-Driven University Application Forecasting
Universities face growing uncertainty in student recruitment, making accurate forecasting critical for strategic and financial planning. Athena is an AI-powered prediction tool that leverages Shiny for Python to provide real-time insights into application trends. By combining machine learning (Random Forest models), trend analysis, and interactive scenario planning, Athena enables universities to test recruitment strategies, adjust campaign spending, and instantly see the projected impact on future application numbers.
This talk will explore how Shiny for Python was used to develop a fully interactive forecasting tool without requiring extensive front-end development. We will discuss why Shiny for Python was chosen, how it integrates with a machine learning pipeline, and how it powers real-time scenario analysis with dynamic dashboards. Additionally, we’ll demonstrate how AI-generated recommendations via an API enhance decision-making, providing actionable insights tailored to user-selected scenarios.
Attendees will gain practical knowledge on building AI-driven, interactive applications using Shiny for Python, implementing predictive models, and designing intuitive decision-support tools for non-technical users. The session will conclude with a live demo, showing Athena in action and sharing best practices for deploying Shiny for Python in production. This talk is designed for developers, data scientists, engineers, and senior decision-makers looking to leverage AI-powered forecasting, business intelligence, and strategic planning in a real-world application.
Nic Crane - NC Data Labs
htmlwidgets Are a Secret Sauce in R – Can LLMs Make Them the Perfect Condiment?
htmlwidgets quietly power some of the most compelling Shiny apps out there, but writing them from scratch can be fiddly and time-consuming. In this talk, we’ll kick things off by taking an audience-sourced ingredient list and asking a large language model to whip up a fresh htmlwidget. Then we’ll plate up a version we prepared earlier - also model-generated - but chopped, seasoned, and finished with our own touches. Along the way, we’ll explore how LLMs can assist in crafting htmlwidgets that reflect your flavour of R - from tidy eval to package structure - rather than sticking to a bland house style.
