R Shiny in Life Sciences, Pharma, and Biotech - 17 Dashboards You Must See

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March 29, 2022

<p><b>UPDATED in April, 2025.</b></p>

<p>Without the right analysis and visualization tool, going through complex biological and pharmaceutical data can be overwhelming, even for top researchers.</p>

<p>R Shiny has emerged as the go-to solution for researchers and developers who need to create interactive dashboards quickly. The framework's flexibility lets you handle tasks like genomic visualization and clinical trial data exploration - and everything in between. Its growing adoption across pharmaceutical companies, research institutions, and biotech firms proves Shiny is a trusted tool for the job.</p>

<blockquote><b>Big Pharma companies are switching to R and open-source - <a href="https://www.appsilon.com/post/gsk-r-adoption-journey" target="_blank">Read about the transitioning process from GSK</a>.</b></blockquote>

<p>The examples in this article showcase how R Shiny is revolutionizing data workflows in life sciences and pharma. These applications demonstrate Shiny's ability to accelerate research, improve clinical decision-making, and streamline regulatory submissions.</p>

<p><b>We'll show you 17 application examples that might inspire your next project.</b> Who knows, maybe it'll be included in this list in the future!</p>

<h3>Table of contents:</h3>

<ul>

<li><a href="#why">Why Should You Use R Shiny in Life Sciences, Pharma, and Biotech?</a></li>

<li><a href="#life-sciences">R Shiny in Life Sciences</a></li>

<li><a href="#pharma">R Shiny in Pharma and Biotech</a></li>

<li><a href="#summary">Summing up R Shiny in Life Sciences and Pharma</a></li>

</ul>

<h2 id="why">Why Should You Use R Shiny in Life Sciences, Pharma, and Biotech?</h2>

<p>R Shiny combines the best of statistical analysis with interactive web capabilities. It's perfect for life sciences and pharmaceutical research.</p>

<blockquote><b>It took J&J 5 years to switch to R and open-source for regulatory submissions - <a href="https://www.appsilon.com/post/jj-open-source-journey" target="_blank">Here are the thigns they learned in the process</a>.</b></blockquote>

<p>Let's explore a couple of reason why it has become indispensable across these industries.</p>

<h3>Handling complex datasets with ease</h3>

<p>R is a statistical programming language specifically designed to handle large and complex datasets. It provides a rich ecosystem of packages for data manipulation, exploration, and visualization. While R itself has a learning curve, its specialized packages make it accessible to both beginners and experts.</p>

<p>The language and it's ecosystem of packages lets you to quickly identify patterns, trends, and insights in your biological and clinical data. This means faster analysis and more time for interpretation - which is also quite streamlined with R Shiny.</p>

<h3>Building interactive applications without web development expertise</h3>

<p>Shiny lets you create customizable web-based applications tailored to specific research needs without extensive web development knowledge. Your applications can include interactive visualizations, data input forms, and user-friendly features that make sharing results effortless.</p>

<p>The growing <a href="https://rhinoverse.dev/#rhino" target="_blank" rel="noopener">ecosystem of Shiny-related packages</a> provides extensive support both for developers and non-developers.</p>

<h3>Meeting strict regulatory requirements</h3>

<p>In pharma and biotech, consumer safety and product reliability are strictly regulated. Shiny applications can support your work from project planning and manufacturing to clinical trials and reporting.</p>

<p>A growing number of open-source R tools make <a href="https://www.appsilon.com/services/gxp-regulatory-compliance" target="_blank">GxP (Good x Practices) compliance</a> easier, with capabilities for generating reproducible reports that meet regulatory standards and <a href="https://appsilon.com/automated-r-data-quality-reporting/">automate data quality reporting</a></p>

<blockquote><b><a href="https://www.appsilon.com/post/gxp-audit-software">How a GxP Audit Will Help You Go Through FDA and EMA Submission</a></b></blockquote>

<h3>Rapid development and deployment</h3>

<p>With R Shiny, you can develop functional dashboards and applications in days, not months. That's the biggest selling point - you can protype quickly and develop in quick sprints.</p>

<p>This speed means you can respond rapidly to emerging research questions or urgent analysis needs. Your data insights become available when they're needed, not weeks later.</p>

<h3>Cost-effective solution for specialized needs</h3>

<p>Commercial data visualization platforms often come with hefty price tags and may not offer the specialized analytics needed for life science research. R Shiny provides a cost-effective alternative with no licensing fees.</p>

<p>Your team can create custom solutions perfectly matched to your research requirements. The open-source nature of R means you're not locked into proprietary systems or formats.</p>

<p>Up next, let's explore a couple of example applications of R Shiny in life sciences.</p>

<h2 id="life-sciences">R Shiny in Life Sciences</h2>

<p>Over the years, R Shiny has become one of the easiest ways for developers to create production-ready dashboards in no time.</p>

<p>Its intuitive nature and top of the line documentation lets researchers with non-coding backgrounds to produce impressive results. Pair that with a couple of web developers and you're golden.</p>

<p>Since 2022, adoption of both R Shiny and Python for Shiny has been increasing rapidly in the life sciences. That's why <b>we've prepared 11 dashboards</b> you must see that'll serve you and your team as inspiration.</p>

<p>Keep in mind that many new apps and dashboards are constantly emerging, and we strive to publish the most unique and exciting ones in the <a href="https://appsilon.com/shiny-weekly-announcement/" target="_blank" rel="noopener">Shiny Weekly Newsletter</a> - so be sure to subscribe!</p>

<p>The dashboards you'll see span from analyzing insect ecology to interpreting MRI images and everything in between. Continue below to see examples of how interactive Shiny dashboards can be used in your field.</p>

<blockquote><strong>Stuck in a Python vs R analysis paralysis? Read our take on</strong> <a href="https://appsilon.com/dash-vs-shiny/"><strong>Python Dash vs R Shiny</strong></a>.</blockquote>

<ul>

<li><a href="#dashboard-ls-1">RConsortium's FDA Pilot 2 - Rewritten in Rhino</a></li>

<li><a href="#dashboard-ls-2">Shiny Gosling - Genomics Data Visualizations</a></li>

<li><a href="#dashboard-ls-3">Drug Interactions Exploration Tool</a></li>

<li><a href="#dashboard-ls-4">DrawCell - Cell Illustrations Simplified</a></li>

<li><a href="#dashboard-ls-5">RStudio: Covid-19 Tracker</a></li>

<li><a href="#dashboard-ls-6">RStudio: Genome Browser</a></li>

<li><a href="#dashboard-ls-7">RStudio: ShinyMRI</a></li>

<li><a href="#dashboard-ls-8">Appsilon: Bee Colonies</a></li>

<li><a href="#dashboard-ls-9">FielDHub: Designing Experiments in R Shiny</a></li>

<li><a href="#dashboard-ls-10">CSBB: Computational Suite for Bioinformaticians and Biologists</a></li>

<li><a href="#dashboard-ls-11">GraphBio: Visualization Analysis for Omics Data</a></li>

</ul>

<h3 id="dashboard-ls-1">RConsortium's FDA Pilot 2 - Rewritten in Rhino</h3>

<p><a href="https://connect.appsilon.com/rhino-fda-pilot/" target="_blank" rel="noopener">The FDA Pilot 2 Rhino rewrite</a> features the <a href="https://www.r-consortium.org/" target="_blank" rel="noopener">RConsortium</a> Submissions Working Group's Pilot 2 app, which was successfully submited in 2023! The Shiny app, built with teal modules, demonstrates clinical trial data analysis and visualization and originally used Golem.</p>

<blockquote><b>Poor software quality can lead to FDA rejections - <a href="https://www.appsilon.com/post/pharma-fda-rejections" target="_blank">Make sure this doesn't happen to you</a>.</b></blockquote>

<p>For the rebuild, we chose <a href="https://rhinoverse.dev/#rhino" target="_blank" rel="noopener">Rhino</a>, an enterprise-level framework, supports clear code, comprehensive testing, and automation, making it well-suited for highly regulated environments and drug development processes.</p>

<p>A lot has progressed with Rhino over the past year. <a href="https://appsilon.com/rhino-1-4-0/" target="_blank" rel="noopener">Rhino 1.4.0 was recently updated on CRAN</a>, with streamlined dependency management, React support, improved Box modules, and more! There's also a growing <a href="https://www.appsilon.com/blog-tags/rhino" target="_blank" rel="noopener">list of helpful Rhino guides and tutorials</a>. You can now lead your team into production grade app development - without needing to be a Full Stack Developer!</p>

<h3 id="dashboard-ls-2">Shiny Gosling - Genomics Data Visualizations</h3>

<p><a href="https://connect.appsilon.com/shiny-gosling/" target="_blank" rel="noopener">Shiny Gosling</a> demonstrates the features of Gosling.js made available in R Shiny by the <a href="https://appsilon.github.io/shiny.gosling/" target="_blank" rel="noopener">shiny.gosling package</a>. Users can now access scalable and interactive genomics data visualizations in their Shiny apps and dashboards!</p>

<p>From compositive visualizations like gene and transcript annotations to interactive visualizations like multi-track apps and circular encoding of genes.</p>

<p>Looking for more R Shiny packages in the realm of drug discovery? We've recently released the <a href="https://appsilon.github.io/shiny.molstar/">shiny.molstar package</a>, an R Shiny wrapper for Mol* (/'molstar/) - A visualization toolkit of large-scale molecular data. See how you can start with our <a href="https://appsilon.com/shiny-molstar-r-package-molecular-structures-visualizations/" target="_blank" rel="noopener">introduction to molecular visualization and analysis with shiny.molstar</a>.</p>

<h3 id="dashboard-ls-3">Drug Interactions Exploration Tool</h3>

<p>The Drug Interactions Exploration Tool in Shiny is a valuable web application that utilizes an extensive online database to identify potential adverse effects related to drug combinations. This Shiny app serves as a valuable resource for healthcare professionals, researchers, and patients seeking to understand the risks associated with different drug interactions.</p>

<p>Developed using the Rhino package, the app offers modular code, SASS support, automation, testing, and a clean interface, streamlining the process of connecting with external drug databases for comprehensive drug interaction information.</p>

<h3 id="dashboard-ls-4">DrawCell - Cell Illustrations Simplified</h3>

<p>The DrawCell Shiny Application, powered by the <a href="https://github.com/svalvaro/drawCell" target="_blank" rel="noopener">drawCell package</a>, offers a user-friendly and engaging solution for life sciences educators, students, and researchers. Whether you need interactive cell diagrams for teaching or to streamline data visualization, DrawCell has you covered. Created by lead developer Álvaro Sánchez, this versatile tool has the potential to make a significant impact in biology education, enabling accessible research and knowledge sharing.</p>

<p>Learn more about DrawCell with Álvaro Sánchez, the co-creator of the drawCell package and Shiny application in <a href="https://www.youtube.com/watch?v=X0gku6K03N0" target="_blank" rel="noopener">his recent interview</a>.</p>

<h3 id="dashboard-ls-5">RStudio: Covid-19 Tracker</h3>

<p>It's near impossible to list the best dashboards made with R Shiny in life sciences without mentioning Covid-19. The dashboard made by RStudio shows daily updates on the number of cases and deaths among 199 countries.</p>

<p>The dashboard allows its end-users (citizens, researchers, government officials) to keep track of the pandemic stats from the very beginning (Jan 22, 2022). It has a section including an interactive world map, region plots (cases in regions of interest), and much more.</p>

<p>There are thousands or even tens of thousands of Covid-19 dashboards out there, so what makes this one unique? Well, it's built with R Shiny, it's free to access, and has the source code available on GitHub.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://vac-lshtm.shinyapps.io/ncov_tracker/" target="_blank" rel="noopener noreferrer">View the Covid-19 Tracker dashboard</a></li>

<li><a href="https://github.com/eparker12/nCoV_tracker" target="_blank" rel="noopener noreferrer">Source code</a></li>

</ul>

<h3 id="dashboard-ls-6">RStudio: Genome Browser</h3>

<p>This dashboard shows a visualization based on <em>Circos</em>, which is a way of visualizing whole genomes. It uses pancreatic adenocarcinoma tumor samples data from various donors, provided by ICGN.</p>

<p>If you're not familiar with the Circos-style plot, here's a crash course. It shows several layers, starting from the outer one representing color and size distinguished chromosomes, moving clockwise from chromosome 1 to 22, then X and Y. </p>

<p>Inside the ring, you can see a line representation of copy number mutations.

The dashboard serves researchers in the area of genomics and bioinformatics to make sense of complex data. It's not the most useful dashboard for people without advanced domain knowledge.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://gallery.shinyapps.io/genome_browser/" target="_blank" rel="noopener noreferrer">View the Genome Browser dashboard</a></li>

<li><a href="https://github.com/rstudio/shiny-gallery/tree/master/genome-browser" target="_blank" rel="noopener noreferrer">Source code</a></li>

</ul>

<h3 id="dashboard-ls-7">RStudio: ShinyMRI</h3>

<p>The ShinyMRI dashboard visualizes 3D MRI images and is made entirely with R Shiny. It was also recognized as an honorable mention in the 2019 Shiny Contest.</p>

<p>What makes this dashboard unique is the ability to upload your own files, either in <code>.nii</code> or <code>.nii.gz</code> format. That functionality makes the dashboard extremely useful for medical professionals. Sure, it's in a PoC state now, but a little tweaking and added functionality could take it a long way.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://haozhu233.shinyapps.io/shinyMRI-contest/" target="_blank" rel="noopener noreferrer">View the ShinyMRI dashboard</a></li>

<li><a href="https://github.com/hebrewseniorlife/shinyMRI-contest" target="_blank" rel="noopener noreferrer">Source code</a></li>

</ul>

<h3 id="dashboard-ls-8">Appsilon: Bee Colonies</h3>

<p>The Bee Colonies dashboard was made by R/Shiny Developer, Ryszard Szymański, in one day. You're reading that right - you can build Shiny dashboards that fast.</p>

<p>The dashboard shows stressors affecting bee colonies (diseases, pesticides, and others) in different US states for a given period. It also shows you how many bee colonies were lost and added in a given year, ranging from 2015 to 2021.</p>

<p>It helps decision-makers to early detect potential problems of adding bee colonies to one location instead of the other.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://demo.prod.appsilon.ai/bee-colony/" target="_blank" rel="noopener noreferrer">View the Bee Colonies dashboard</a></li>

<li><a href="https://github.com/szymanskir/bee_colony_losses" target="_blank" rel="noopener noreferrer">Source code</a></li>

</ul>

<h3 id="dashboard-ls-9">FielDHub: Designing Experiments in R Shiny</h3>

<p>The FielDHub dashboard by North Dakota State University helps in the creation of traditional, un-replicated, augmented, and partially-replicated designs applied to agriculture, plant breeding, forestry, animal, and biological sciences.</p>

<p>If you want to test the dashboard, you'll have to install it first. It's assumed you have R and RStudio configured - from there, there's only one package to install:</p>

<p>To actually view the dashboard, you'll have to import the library and run the app - it will open just as any other R Shiny application:</p>

<p>The dashboard allows end-users to evaluate research, teach, and train others. It also has the functionality to run simulations. You can simulate response variables along with the randomization. The info can be used directly to assess the correlation of data plots in spatial designs and for teaching statistical concepts.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://github.com/DidierMurilloF/FielDHub" target="_blank" rel="noopener noreferrer">Source code</a></li>

</ul>

<h3 id="dashboard-ls-10">CSBB: Computational Suite for Bioinformaticians and Biologists</h3>

<p>The CSBB is an R Shiny dashboard developed with an intention to empower researchers from wet and dry labs to perform downstream Bioinformatics analysis.</p>

<p>Once again, you can run the dashboard from your machine. Use the <code>runGitHub()</code> function from R Shiny, and it will take care of the dependency installation for you.</p>

<p>The dashboard comes with 8 modules (visualization, normalization, basics statistics, differential expression, correlation profiles, function enrichment, ChiP-ATAC seq, and single-cell RNA-seq analysis) that are designed in order to help researchers design a hypothesis or answer research questions with little or no expertise in bioinformatics.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://praneet1988.shinyapps.io/CSBB_Shiny/" target="_blank" rel="noopener noreferrer">View the CSBB dashboard</a></li>

<li><a href="https://github.com/praneet1988/CSBB-Shiny" target="_blank" rel="noopener noreferrer">Source code</a></li>

<li><a href="https://www.youtube.com/watch?v=c0P7TMu_IyY" target="_blank" rel="noopener noreferrer">Video tutorial</a></li>

</ul>

<h3 id="dashboard-ls-11">GraphBio: Visualization Analysis for Omics Data</h3>

<p>The GraphBio dashboard provides 15 popular visualization analysis methods (heatmap, volcano plot, MA plot, network plot, PCA, ROC analysis, and others).</p>

<p>If you're unfamiliar with the term, <em>Omics</em> stands for different disciplines in biology whose names end in “omics”, such as genomics, proteomics, metabolomics, and so on.</p>

<p>The dashboard enables experimental biologists without programming skills to easily perform popular visualization analysis and get publication-ready charts.</p>

<p>Learn more:</p>

<ul>

<li><a href="http://www.graphbio1.com/en/" target="_blank" rel="noopener noreferrer">View the GraphBio dashboard</a></li>

<li><a href="https://www.biorxiv.org/content/10.1101/2022.02.28.482106v1" target="_blank" rel="noopener noreferrer">Research article</a></li>

</ul>

<h2 id="pharma">R Shiny in Pharma and Biotech</h2>

<p>The pharmaceutical and biotech industries face unique sets of challenges with complex datasets from drug discovery, clinical trials, and regulatory submissions. R Shiny is a life saver here, as it offers specialized solutions for these high-stakes environments.</p>

<blockquote><b>Open-source adoption in Pharma brings many challenges and opporunities - <a href="https://www.appsilon.com/post/open-source-pharma" target="_blank">Our recent guide shows key insights from the industry</a>.</b></blockquote>

<p>In pharma and biotech, data visualization isn't just about creating charts - it's about accelerating drug development timelines, ensuring patient safety, and meeting strict regulatory requirements. The examples you're about to see demonstrate these points perfectly.</p>

<p>We've prepared <b>six high-quality R Shiny dashboards</b> for pharma and biotech. Keep in mind that the list is likely to expand as the time goes.</p>

<blockquote><b>Are you a director of a Pharma/Life Science company and are using AWS? <a href="https://www.appsilon.com/post/aws-guide-for-pharma-directors">Read our complete guide for getting the most from the cloud</a>.</b></blockquote>

<ul>

<li><a href="#dashboard-ph-1">Shiny and Proteomics Data Analysis - {Bigomics}</a></li>

<li><a href="#dashboard-ph-2">Shiny and Proteomics Data Analysis - {ProViz}</a></li>

<li><a href="#dashboard-ph-3">Shiny and Clinical Trial Data Exploration - {Teal}</a></li>

<li><a href="#dashboard-ph-4">Shiny Drug Discovery and Drug Interactions - {Drug Interactions}</a></li>

<li><a href="#dashboard-ph-5">Shiny Drug Discovery and Drug Interactions - {shinyDepMap}</a></li>

<li><a href="#dashboard-ph-6">R and Shiny in Regulatory Action - FDA Submission - {FDA-app}</a></li>

</ul>

<h3 id="dashboard-ph-1">Shiny and Proteomics Data Analysis - {Bigomics}</h3>

<p>Omics Playground is a self-service analytics platform for the exploration, visualization, and analysis of Big Omics Data in the Biotech and Pharma spaces. It is designed to allow biologists to apply a wide range of analysis tools to their own data without coding. </p>

<p>The platform is implemented using the R/Shiny web application framework and can be run either from source code or by downloading the Docker image.It consists of two main components, one for data importing and preprocessing, and the other for real-time visualization and interaction with users. </p>

<p>The online interface is subdivided into Basic and Expert modes to provide a customizable experience suited to each user's background. The platform comes with some example datasets, but users can analyze their data by using the upload function or creating/modifying scripts in the "scripts/" folder. More detailed information and feature explanations can be found in the <a href="https://omicsplayground.readthedocs.io/" target="_blank" rel="noopener">online documentation</a>.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://bigomics.ch/omics-playground/" target="_blank">Playground</a></li>

<li><a href="https://github.com/bigomics/omicsplayground" target="_blank" rel="noopener">GitHub</a></li>

<li><a href="https://bigomics.ch/" target="_blank" rel="noopener">Website</a></li>

</ul>

<h3 id="dashboard-ph-2">Shiny and Proteomics Data Analysis - {ProViz}</h3>

<p>ProViz is a data visualization and analysis tool developed at SomaLogic. </p>

<p>It imports ADAT files and enables users to perform various exploratory data analytic processes such as merging additional sample data, filtering, creating groups of data, and creating interactive boxplots, CDFs, and scatter plots.</p>

<p>It also allows users to perform basic statistical tests such as t-test, U-test, and KS-tests.ProViz is distributed under the MIT License, and the <a href="https://somalogic.github.io/ProViz/" target="_blank" rel="noopener">ProViz User's Guide</a> provides detailed information about the tool.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://github.com/SomaLogic/ProViz" target="_blank" rel="noopener">GitHub</a></li>

</ul>

<h3 id="dashboard-ph-3">Shiny and Clinical Trial Data Exploration - {Teal}</h3>

<p>Teal is a Shiny-based interactive exploration framework for analyzing data. This R package requires app developers to specify the type of data to be analyzed, which can be CDISC data, independent datasets, related datasets, or multi-omics experiments. </p>

<p>Teal modules are Shiny modules built within the framework that specifies the analysis to be performed. Some modules are provided in the teal.modules.general, teal.modules.clinical, and teal.modules.hermes packages. </p>

<p>Teal functionality also comes from other packages, including teal.data, teal.widgets, teal.slice, teal.code, teal.transform, teal.logger, and teal.reporter. The package provides examples of teal apps in the teal gallery and TLG Catalog. To install the package, it is recommended to create and use a Github PAT and run the provided code.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://insightsengineering.github.io/teal/latest-tag/">Documentation</a></li>

<li><a href="https://github.com/insightsengineering/teal">GitHub</a></li>

</ul>

<h3 id="dashboard-ph-4">Shiny Drug Discovery and Drug Interactions - {Drug Interactions}</h3>

<p>Appsilon's Drug Interaction dashboard allows users to find interactions between two existing drugs or active compounds from the US National Library of Medicine (NLM) database.</p>

<p>The Shiny dashboard facilitates access to information by eliminating the complexity of searching through multiple databases and filtering relevant data.The tool was built using Rhino for a high-quality, enterprise-grade Shiny application, and APIs like RxNorm, Drug Interaction, and PubChem were used to retrieve information about drug interactions, drug descriptions, and related drugs.</p>

<p>The tool has a responsive design that adapts to both desktop and mobile screens, providing users with the flexibility to access its features virtually anywhere. The tool also provides a preview of drug interactions so users can quickly identify potential adverse effects.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://appsilon.com/drug-drug-interactions-r-shiny/">Article</a></li>

</ul>

<h3 id="dashboard-ph-5">Shiny Drug Discovery and Drug Interactions - {shinyDepMap}</h3>

<p>A web tool to explore the <a href="https://depmap.org/portal/" target="_blank" rel="noopener">Cancer Dependency Map (DepMap) project</a> data. </p>

<p>It has many useful applications, such as predicting how effective and selective future drugs with a known target gene will be, identifying targets of highly selective drugs, finding the most sensitive cell lines for testing a drug, navigating from an undruggable protein with a desired selectivity profile to more druggable targets with similar profiles, and identifying novel pathways necessary for cancer cell growth and survival.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://depmap.org/portal">Website</a></li>

</ul>

<h3 id="dashboard-ph-6">R and Shiny in Regulatory Action - FDA Submission - {FDA-app}</h3>

<p>This Shiny application was submitted to the FDA as part of a pilot project to test the feasibility of submitting a Shiny app, bundled into a submission package to the FDA.</p>

<p>In December 2022, it was successfully submitted; the first of its kind.The project is an ongoing FDA-industry collaboration through the non-profit organization R Consortium, and all data, code, material, and communications from this pilot were shared publicly. </p>

<p>The R Consortium hoped to establish a working example to guide the industry for the future submission of Shiny applications created with the R language.The application includes a Demographic Table, KM-Plot for TTDE, Primary Table, and Efficacy Table. The purpose of the application is to showcase the potential of Shiny apps in streamlining the submission process and improving the communication of clinical trial results. </p>

<p>As the first Shiny app to be submitted to the FDA, its successful acceptance is a significant milestone in the use of innovative R and Shiny technology in the regulatory process and opens the door to future submissions.</p>

<p>Learn more:</p>

<ul>

<li><a href="https://rconsortium.github.io/submissions-wg/minutes/2022-04-01/" target="_blank" rel="noopener">Website</a></li>

</ul>

<h2 id="summary">Summing up R Shiny in Life Sciences and Pharma</h2>

<p>In this article, you've seen 17 examples of R Shiny applications transforming how life sciences and pharmaceutical data is analyzed and visualized. These range from example applications to real-world solutions used in 2025.</p>

<p><b>What makes R Shiny stand out is its unique combination of statistical power and accessibility.</b> Researchers with minimal coding experience can create interactive tools that would previously require dedicated development teams. This democratization of data analysis is changing and improving how scientific teams collaborate.</p>

<p>The applications we've explored represent just the tip of the iceberg. For every public dashboard, there are dozens of proprietary applications being used behind the scenes at research institutions and pharmaceutical companies worldwide.</p>

<blockquote><strong>Unsure about adopting R Shiny in your business? Here's <a href="https://appsilon.com/why-you-should-use-r-shiny-for-enterprise-application-development/">what we've learned by helping Fortune 500s develop enterprise Shiny apps</a>.</strong></blockquote>

<p>If you're looking to build your first R Shiny dashboard, you can use <a href="https://www.appsilon.com/resources/shiny-templates">Appsilon Shiny Dashboard Templates</a> to simplify the process. Our template bundle contains beautiful and easy-to-use designs that will jumpstart your development. <b>The best part? It's completely free.</b></p>

<b><p>Ready to take your R Shiny implementation to the next level? <a href="https://appsilon.com/">Reach out to Appsilon</a>. We develop advanced R Shiny applications for Fortune 500 companies, specifically tailored to pharma, biotech, and life sciences.</p></b>

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