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R-bloggers | R news and tutorials contributed by (750) R bloggers - Software
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Specialized R packages for spatial machine learning: An introduction to RandomForestsGLS, spatialRF, and meteo | R-bloggers
This is the fifth part of a blog post series on spatial machine learning with R. You can find the list of other blog posts in this series in part one. This document provides an overview of three R packages, RandomForestsGLS, spatialRF, a...
How to draw a pie chart on a map in R with ggplot2 and scatter pie? An example for Turkey | R-bloggers
Pie Chart… The unloved boy of visualization family. However, it is getting popularity especially when it is in conjuction with maps. For example, the following chart was publised by to illustrate the vote distribution across the country.
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
How to open files, folders, websites in R | R-bloggers
Coming to you from France, a post about Mise en place for R projects. In a less francophone phrasing: to get to work on something you have to open that thing, be it a script or a project or a website. The easier that is, the faster you get to work. In ...
Chat with LLMs on your R environment | R-bloggers
LLM provides many advantages to the users, especially for coding. Once user had to switch the windows from the coding environment to the browser to search for the solution. But now, thanks to the newly advancements, users can chat with the LLM and get the solution for their queries on the same coding environment in R.
Future got better at finding global variables | R-bloggers
The future package celebrates ten years on CRAN as of June 19, 2025. This is the first of a series of blog posts highlighting recent improvements to the futureverse ecosystem. The globals package is part of the futureverse and has had two recent rel...
Segmented Total Bar Chart in R with “`ggsegmentedtotalbar“` | R-bloggers
Kevin Flerlage, who is a data visualization specialist, suggested a great alternative to stacked bar plot on his blog. He called this new alternative “segmented total bar plot”. This R package ggsegmentedtotalbar implements this idea. The package is built on top of the ggplot2 package, which is a popular data visualisation package in R. The ggsegmentedtotalbar function creates a segmented total bar plot with custom annotations (boxes) added for each group. The height of each box is determined by the Total value associated with each group.
THAMES for mixtures, a reply from the authors | R-bloggers
[Here is a reply to my comments on THAMES sent by the first author of the paper, Martin Metodiev. The above replica of the cover of Rivers of London is obviously unrelated with the reply or the original blog, beyond presenting a fantasy map of the Thames!] Thank you for your review of our article! […]
R’da ggplot2 paketi kullanarak Türkiye Haritası Çizdirmek | R-bloggers
2018 yılında hazırlamış olduğum R’da ggplot2 ve maps Kutuphanelerini Kullanarak Harita Cizdirmek adlı yazımda, R’da ggplot2 ve maps paketlerini kullanarak harita çizdirmeyi anlatmıştım. Yıllar içinde oldukça fazla bu yazıyla ilgili mailler aldım, ancak aldığım son mailler bu yazıda kullandığım kodların, kullandığım veri kaynağı olan GADM platformunun paylaştığı veri içeriğini değiştirmesi nedeniyle istenilen sonucu vermediğine dairdi, o nedenle yıllar sonra bu içeriği güncellemek istedim. Bu içerikte de yine GADM de bu sefer JSON olarak paylaşılan verileri kullanarak bir Türkiye haritası oluşturacağız ve bir örnek üzerinden gradyan (gradient) renklendirmeler yapacağız.
Multilingual Publishing: Frequently Asked Questions | R-bloggers
Read it in: Español.Read it in: Français. As we’ve said before, we believe that publishing multilingual resources can lower the barrier to access to knowledge, help democratize access to quality resources and increase the possibilities of contri...
Understanding the Link Between Uncertainty and Imports by glmnet | R-bloggers
According to the graph below, suggested by Fernando Leibovici, the increase in uncertainty that began in late 2024 aligns with a rise in imports, indicating that US importers accelerated their purchases as a precaution against expected tariff increases or supply chain disruptions. When we model the variables with the glmnet engine, we can see that this impact is limited and negative. This […]
ggplot2 4.0.0 is coming and why ultimately it’s on YOU to ensure your environments are reproducible | R-bloggers
It looks like a major update to {ggplot2} is coming (version 4.0.0), where Posit is switching the internals from S3 to S7. This will break many reverse dependencies of {ggplot2} (a reverse dependency is a package that depends on {ggplot2})...
Season 48 is now in 📦{survivoR} + new datasets and data updates | R-bloggers
Survivor 48 has wrapped up, and the data has been added to the package and is now available on CRAN. […] The post Season 48 is now in 📦{survivoR} + new datasets and data updates appeared first on Dan Oehm | Gradient Descending.
Kendallknight: An R package for efficient implementation of Kendall’s correlation coefficient computation | R-bloggers
The kendallknight package introduces an efficient implementation of Kendall’s correlation coefficient computation, significantly improving the processing time for large datasets without sacrificing accuracy. The kendallknight package, following K...
Futureverse – Ten-Year Anniversary | R-bloggers
The future package turns ten years old today. I released version 0.6.0 to CRAN on June 19, 2015, just days before I presented the package and sharing my visions at useR! 2016. I had no idea adoption would snowball the way it has. It’s been an exciting, fun journey, and the best part has been
Creating A Question Bank Using Google Sheet, Plumber, and Digital Ocean Droplet | R-bloggers
Learn how to build a flash-card style question bank using Google Sheets as storage, R’s Plumber API, and host it on a Digital Ocean droplet—step-by-step setup, deployment, and tips. Motivations One of my colleagues wanted ...
Shiny in Production 2025: Full Length Talks | R-bloggers
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. Register now 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 Bringing Connectivity Data Together: An R Shiny Platform for Public Schools 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. For updates and revisions to this article, see the original post
My data science internship with the openwashdata team | R-bloggers
I was first introduced to Global Health Engineering (GHE) when I took a fantastic class; “International Engineering: Hubris to Hope” by Prof. Elizabeth Tilley. As I neared the completion of my master’s program in Science, Technology, and Po...
Correlation Analysis of Silver, Bitcoin, SOXX, and Blockchain Index with {corrr} package | R-bloggers
Silver, Nasdaq Blockchain Economy Index, Bitcoin, and iShares Semiconductor ETF are always thought of as correlated with each other by investors. We will test this perception with the corrr package. The common perception appears to be correct based on the correlation analysis, specifically between the Nasdaq Blockchain Economy index and Bitcoin.
Internship – Data Science with openwashdata – 80% or more | R-bloggers
Background As part of our Open Science team you will support us in developing efficient data cleaning processes, comprehensible visualizations, and teaching events that provide an inclusive and safe learning environment. Your efforts will prima...
Paper: ‘Semi-Markov modeling for disease incidence risk and duration’ | R-bloggers
I’m happy to share that my latest research paper, “Semi-Markov modeling for disease incidence risk and duration” has been accepted for publication in the journal Biostatistics & Epidemiology (Soetewey et al., 2025). Read the full paper here. T...
Building Your Own Mini-ChatGPT with R: From Markov Chains to Transformers! | R-bloggers
Remember our journey so far? We started with simple Markov chains showing how statistical word prediction works, then dove into the core concepts of word embeddings, self-attention, and next word prediction. Now, it’s time for the grand finale: if you want to build your own working transformer language model in R, read on! You will … Continue reading "Building Your Own Mini-ChatGPT with R: From Markov Chains to Transformers!"
Celebrating 18 Years of ggplot2: A Special Bundle Offer | R-bloggers
It’s been 18 years since ggplot2 revolutionized data visualization in R. To celebrate this milestone, I’m offering a special bundle deal on my data visualization books. The “Beautiful Plots Across Languages” Bundle For just $19.99 USD, get two ...
R Shiny App for DOE Analysis | R-bloggers
Apps Team: This app is develpoed by a team of five members as listed below: Muhammad Riaz Jerry Joel Abdul Muqtadir Ahmed Amgad Alkhiaty Mustafa Hamed App Intrduction: This app allows you to perform statistical analsysis of Experimental Design data. It covers the following contents: 1. Single Factor Design(CRD, RCBD, LSD, GLSD), 2. Factorial Design … Continue reading R Shiny App for DOE AnalysisR Shiny App for DOE Analysis was first posted on June 14, 2025 at 11:15 am.
Use the duplicated Function in R: Find & Remove Duplicates | R-bloggers
Your statistical model is built, and your p-values are perfect, but is your conclusion valid? What if a single, overlooked duplicate entry in your dataset is silently skewing your results, leading to flawed insights? How can you be certain that the data you're analyzing is clean, accurate, and trustworthy?The key to data integrity lies in identifying and managing redundancies. R provides a powerful, built-in tool: the duplicated() function in R. It scans a vector or data frame and determines which elements are duplicates of entries that appeared earlier. It returns a logical vector (TRUE/FALSE) of the same length as your input, where TRUE marks an element as a duplicate. Learning how to use the duplicated function isn't just a programming trick; it's a fundamental step in pre-processing that ensures the validity of your entire data analysis. It's your first line of defense against the kind of data errors that can lead to skewed results and compromise your research.Key PointsFind Duplicates Instantly: The duplicated() function is your go-to tool in R to find copied data. It scans your data and tags every repeated entry as TRUE, making it simple to spot duplicates.Remove Duplicates with One Simple Trick: To get a clean dataset, just add a ! before the function. This single line of code keeps only the unique rows and is the fastest way to clean your data.# This is the most common way to get a clean data framecleaned_df
More on power and ‘fragile’ p-values by @ellis2013nz | R-bloggers
Last week I posted about the proportion of p values that will be ‘fragile’ under various types of scientific experiments or other inferences. The proportion of p values that is fragile had been defined as the proportion that are between 0.01 and 005. I...
From lab to real life: How your Shiny application can survive its users | R-bloggers
You can read the original post in its original format on Rtask website by ThinkR here: From lab to real life: How your Shiny application can survive its users From prototype to production, make sure nothing breaks… You’ve created a fantastic mockup and your client is delighted. You’re ready to move to production with your application. But one question haunts you: how can you ensure that your application will remain stable and functional through modifications and evolutions? The answer comes down to one word: testing. Three weeks ago, part This post is better presented on its original ThinkR website here: From lab to real life: How your Shiny application can survive its users
Setting up VScode for R and generative AI tools | R-bloggers
Setting up VScode for R and generative AI tools VScode has many extensions that let you create and run entire workflows via using prompts to a large language model. Its not widely used in the R community yet, but I expect it will be soon. You can crea...