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📦 Caching in Android: Boosting App Performance with Real-World Scenarios & Code | by Prahalad Sharma | Jun, 2025 | Medium
Caching is a critical concept in Android development that improves app performance, reduces network usage, and enhances user experience. In this article, we’ll dive deep into: Caching is the process…
Shiny in Production 2025: Lightning Talk Lineup | R-bloggers
We are pleased to announce the lightning talks for this year’s Shiny in Production conference! We’ve already announced the full length talks (25 minutes each) in this blog. This blog however is all about this year’s lightning talks session (5 minutes per talk). Register now Lightning talks Andreas Wolfsbauer - AGES - Austrian Agency for Health and Food Safety Enhancing Epidemiological Surveillance with a Shiny Application for Standardized Data Analysis The Agency for Health and Food Safety (AGES) is responsible for monitoring notifiable infectious diseases in Austria. Within the Institute for Surveillance & Infectious Disease Epidemiology, we have developed a Shiny application designed to provide standardized analysis and visualization of all (n=76) notifiable disease categories, by processing data from the epidemiological notification system of Austria. The application offers a dashboard that enables users to select specific diseases and visualize data through interactive plots. Features include filtering by year, federal state and age-group, facilitating descriptive epidemiological analysis of the notification data. An analysis tab allows users to apply custom filters and generate tailored plots, enhancing the depth of data exploration. Users can download all plots along with the underlying data and generate a PDF report. They also have the option to export filtered data as a CSV file for further use. Further development plans include a starting page highlighting long-term trends, to provide a compact overview for quick identification of diseases with need of action. Additionally, we will create an information page, that shows disease-specific metadata, and analyses of seasonal trends. Furthermore, discussions are ongoing to develop a dashboard for broader accessibility, initially within the organization, with potential public access. Challenges encountered include optimizing application performance and availability, particularly given the constraints of utilizing the free version of Shiny Server. To address this, we are exploring parallel and asynchronous programming techniques to enhance efficiency and responsiveness. Additionally, we are evaluating deployment solutions such as ShinyProxy to improve multi-user access and scalability. David Carayon - INRAE Rescuelog: a Shiny-Based Monitoring System for Lifeguards: Insights from Southwest France Drowning prevention on coastal beaches relies heavily on lifeguard vigilance and timely intervention. However, traditional rescue data collection methods often suffer from inefficiencies, delayed reporting, and a lack of real-time analytics. To modernize lifeguard operations across the beaches of southwest France, we developed an end-to-end open-source data pipeline powered by R, Shiny, and ruODK. At the core of this system is ruODK, an R package that facilitates seamless integration with Open Data Kit (ODK), a widely used tool for field data collection. Lifeguards use tablets running ODK Collect to log rescue incidents in real time, which are then ingested directly into an R-managed database. The data is processed, analyzed, and visualized through a Shiny dashboard, offering lifeguards and supervisors instant access to key operational insights, trend analysis, predictive models and customizable reports. By leveraging R’s data manipulation capabilities (tidyverse) alongside Shiny’s interactivity, we achieved a fully automated and scalable monitoring system that replaces paper-based logs with a dynamic, data-driven approach. Initial deployments in 2023 (on five beaches) demonstrated significant improvements in efficiency and situational awareness, prompting an expansion to 80 beaches by 2025. The system’s open-source nature ensures cost-effectiveness, reproducibility, and adaptability for other regions and applications. This project exemplifies how R and Shiny can power real-time decision-making in public safety operations. It also highlights the untapped potential of ruODK for bridging field data collection with analytical pipelines—showcasing an impactful use case of Shiny in production. Kia Mack - Kent Wildlife Trust Building the Kent BNG Register: Shiny for UI-First Development in a Small Charity Tech Team R Shiny is a powerful and beginner-friendly tool for rapidly developing interactive applications, but is it the best choice for UI-first web design? In this talk, we share our experience building the Kent Biodiversity Net Gain Site Register, a user-authenticated web portal that links the demand and supply of biodiversity credits. We’ll discuss the ways in which Shiny was a great fit—allowing rapid prototyping, seamless integration with R’s data analysis tools, and reactive programming. We’ll explore why it suited a small conservation charity with a two-person team, enabling us to build a functional, data-driven application without the need for specialist web development skills. However, we’ll also examine its limitations, from performance bottlenecks to challenges in creating a polished, responsive UI. We’ll share the strategies we used to overcome these issues, including optimising reactive dependencies, using custom CSS and JavaScript for a more refined UI, implementing caching and database indexing to improve performance, and leveraging Shiny modules to enhance scalability. Whether you’re considering Shiny for a large-scale project or looking for ways to improve an existing app, this talk will provide practical insights into where Shiny excels and what can be learned from mainstream web development languages to improve our use of Shiny. Natalia Petersen - NHS England Hackathon to Streamline the National Disease Registration Service Cancer Treatments Shiny App The Cancer Treatments dashboard is an interactive tool, built in Shiny, produced by the National Disease Registration Service (NDRS), within NHS England. The Shiny app displays graphs and tables presenting statistics on surgery, chemotherapy, and radiotherapy treatments for patients diagnosed with cancer in England. Users can select to view the data by demographic factors such as ethnicity and stage at diagnosis, and by geography, via dropdown menus. The app is refreshed annually and is publicly available, aimed at supporting the understanding of cancer treatments for both technical and non-technical audiences. The previous Shiny code was long and repetitive, making it difficult to navigate, challenging to de-bug, time-consuming to run, and prone to human error due to limited automation. To address these concerns, whilst also delivering improvements to the user interface, the team took part in a targeted hackathon day where individuals each took on a specific workflow and set of objectives, guided by user feedback. The re-developed app is now built on the NDRS Shiny app template, ensuring consistent styling. Bespoke, reusable functions are sourced throughout the code, allowing for modularisation, and graphs are built using the Plotly package to improve usability and interactivity. All code required for producing the publication is available on GitHub, increasing transparency and scope for reuse. Through collaborative effort, careful division of labour, communication in person and online, and application of Reproducible Analytical Pipeline principles we were able to successfully and quickly deliver improvements to the Shiny app, which will be published May 2025. Rhian Davies - The Strategy Unit, NHS The Accidental Engineers: Managing Shiny Apps, Pipelines, and Tech Debt in the NHS How big should the hospitals of the future be? That’s the question we’re trying to answer. Our team has built a complex statistical model with over 100 parameters, using 140 million rows of patient data to help healthcare leaders plan for future demand. The model incorporates uncertainty, allowing users to explore different policy scenarios and compare their hospital against national benchmarks. But while the maths is complicated, the hardest part isn’t the modelling, it’s making sure everything keeps running smoothly. What started as a small data science project has grown into a sprawling web of interconnected tools, and some days, it feels like we’ve become accidental software engineers. Maintaining multiple Shiny apps, APIs, and pipelines across R, Python, and PySpark means we’re now juggling Databricks workflows, GitHub Actions, Azure Blob Storage, and Posit Connect deployments. Every month, we release a new version of the model while ensuring legacy versions are maintained and compatible. And with technical debt piling up, we’re starting to ask: do we keep patching things, or should we tear it all down and start again? This talk is an honest reflection on the challenges of managing large-scale Shiny apps in a high-pressure environment, how we balance new development with maintenance, and what we’ve learned along the way. The code for all our tools is available publicly on GitHub. Samer Hijjazi - MD Anderson Cancer Center From Clicks to Insights: Harnessing RSelenium in R Shiny Applications This talk explores the opportunity to incorporate the RSelenium package into R Shiny applications. RSelenium is a package which allows users to perform web automation and advanced web scraping. In comparison to rvest, RSelenium can give you the ability to web scrape data from more difficult websites. This talk would teach the R community a lot about web automation, as well as showing another creative way of using R Shiny applications. Thibault Senegas Monitoring container logs from shinyproxy with shinyproxyLogs When running multiple Shiny apps in production with hundreds of daily users, monitoring logs effectively is critical—but can quickly become overwhelming. This talk introduces shinyproxyLogs, an R package designed to help track app health, analyze logs, and debug issues across large-scale ShinyProxy deployments. Discover how to extract meaningful insights from containerized logs, detect failures early, and enhance observability for Shiny applications at scale. Register now For updates and revisions to this article, see the original post