Open Source (general)
Open Source GuidesGeneral Open Source Guides like how to contribute, how to start, best practices and much more
The Legal Side of Open SourceThe Legal Side of Open Source Guide, details on how to legally use Open Source
Open Source Portal for Clinical Study EvaluationsCollection of open source tools which can be used for clinical study evaluations, including searchable macros and programs
R / RStudio / Shiny / Tidyverse
R Self AssessmentAre you ready for Shiny? Self Assessment Quiz
Using Python with RStudioDescription of using Python with Rstudio
Happy Git with RInstall Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE. Develop a few key workflows that cover your most common tasks. Integrate Git and GitHub into your daily work with R and R Markdown.
The Coprehensive R Archive NetworkCRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R.
CRAN R PackagesAvailable CRAN Packages by Name
Download RStudioRStudio is a set of integrated tools designed to help you be more productive with R. It includes a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting, viewing history, debugging and managing your workspace.
Download RStudio ServerRStudio Server enables you to provide a browser based interface to a version of R running on a remote Linux server, bringing the power and productivity of the RStudio IDE to server-based deployments of R.
A Quick introduction to RStudioRStudio is not R or a “type” of R. It is a program that runs R and provides extra tools that are helpful when writing R code, kind of like how your operating system can run a web browser. This workshop will assume you are using RStudio to interact with R, although everything here can be done without RStudio. Most R users seem to use RStudio and we like it, so we recommend using it.
Installing RStudio for WindowsA guide to installing RStudio for Windows
Setup an RStudio Server in UbuntuA concise step-by-step guide to setup a Rstudio Server in Ubuntu Linux. The assumption is made that the server is already setup.
Setup a Shiny Server in AWS (Amazon)A step-by-step guide to setup a Shiny Server in AWS along with a method that makes publishing apps easier
RStudio QuickstartExperience RStudio Team using a virtual machine on your desktop. RStudio Team QuickStart VM makes it quick and easy to learn through hands-on experience.
Learning Analytic Administration through a SandboxIt all starts with sandboxes. Development sandboxes are dedicated safe spaces for experimentation and creativity. A sandbox is a place where you can go to test and break things, without the ramifications of breaking the real, important things. If you’re an analytic administrator who doesn’t have access or means to get a sandbox, I recommend that you consider advocating to change that. Here are just some of the arguments for why sandboxes are a powerful tool for the R admin that you may find helpful.
TidyverseThe tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
RStudio CloudCreated to make it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R.
RStudio Cloud CheatsheetsCheatsheets for working with popular R packages
RStudio Cloud GuideGuide to using RStudio Cloud
R Start HereA Guide to some of the most useful R Packages
GGPLOT ToolboxPlotting System for R
RadiantRadiant is an open-source platform-independent browser-based interface for business analytics in R. The application is based on the Shiny package and can be run locally or on a server.
bioCancer: Interactive Multi-OMICS Cancers Data Visualization and AnalysisbioCancer is a platform-independent interface for dynamic interaction with cancer genomics data. The web is implemented in the R language and based on the Shiny package. It runs on any modern Web browser and requires no programming skills, increasing the accessibility to the huge, complex and heterogeneous cancer genomic data.
GGPLOT GUIThis package allows users to visualize their data using an online graphical user interface (GUI) that makes use of R's visualization package ggplot. There are two ways of using this functionality: 1) online, where users can upload their data and visualize it without needing R, by visiting this link:; 2) from within the R-environment (by using the ggplot_shiny() function). In either case, R-code will be provided such that the user can recreate the graphs within the R-environment.
Managing libraries for Rstudio ServerR users have access to thousands of community contributed packages. Most users rely on dozens if not hundreds of packages. Organizing these packages can take a fair amount of administrative effort, especially when multiple versions of R exist across multiple servers. This document lays out a simple strategy for managing packages for a team of analysts on a server.
R Installation and AdministrationThis is a guide to installation and administration for R. This manual is for R, version 3.6.2 (2019-12-12)
Strategy MapsStrategies to Reproduce Environments Over Time
CrandashA live visualization of the most popular R packages
Navigating the R Package UniverseThere are more than 11,000 packages on CRAN, and R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use. Our session centered on this issue, with three themes in our discussion.
The lazy and easily distracted report writer: Using rmarkdown and parameterised reportsMike K Smith presents : My brain is lazy, shallow and easily distracted. Learn how I use notebooks to keep my present-self organised, my future-self up to speed with what I was thinking months ago, and also how I use parameterised reports to share results for both quantitative and non-quantitative audiences across multiple endpoints. I can update and render outputs for a variety of outputs from a single markdown notebook or report. I’ll show you how I organise my work using the tidyverse, use child documents with parameterisation and also how this is served out to my colleagues via RStudio Connect.
A Gentle Guide to Tidy Statistics in R (Part 1)Thomas Mock
A Gentle Guide to Tidy Statistics in R (Part 2)Thomas Mock
Installing Older Versions of PackagesYou may need to install an older version of a package if the package has changed in a way that is incompatible with the version of R you have installed, or with your R code. You may also need to use an older version of a package if you are deploying an application to a location such as, Shiny Server , or RStudio Connect where the environment may not allow you to run the latest version of the package. Here are instructions on several methods you can use:
How to install a package of a particular version in RHow to install a package of a particular version in R
sessioninfoQuery and print information about the current R session. It is similar to utils::sessionInfo(), but includes more information about packages, and where they were installed from.
Multiple versions of RData scientists prefer using the latest R packages to analyze their data. To ensure a good user experience, you will need a recent version of R running on a modern operating system. If you run R on a production server – and especially if you use RStudio Connect – plan to support multiple versions of R side by side so that your code, reports, and apps remain stable over time. You can support multiple versions of R concurrently by building R from source. Plan to install a new version of R at least once per year on your servers.
tidylogTidylog provides feedback about dplyr and tidyr operations. It provides simple wrapper functions for the most common functions, such as filter, mutate, select, and group_by.
xposexpose was designed as a ggplot2-based alternative to xpose4. xpose aims to reduce the post processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.
CRAN Task View: Clinical Trial Design, Monitoring, and AnalysisThis task view gathers information on specific R packages for design, monitoring and analysis of data from clinical trials. It focuses on including packages for clinical trial design and monitoring in general plus data analysis packages for a specific type of design. Also, it gives a brief introduction to important packages for analyzing clinical trial data.
The Shiny Cheat SheetQuick reference guide for building shiny apps
Shiny Function ReferenceFunction Reference version 1.4.0
Shiny Widgets GalleryShiny Widgets Gallery
How to build a Shiny appLet’s walk through the steps of building a simple Shiny application. A Shiny application is simply a directory containing an R script called app.R which is made up of a user interface object and a server function. This folder can also contain any any additional data, scripts, or other resources required to support the application.
Build a user InterfaceHow to build a user interface in Shiny
GalleryShiny User Showcase
Scoping Rules for Shiny appsWhere you define objects will determine where the objects are visible. There are three different levels of visibility that you’ll want to be aware of when writing Shiny apps. Some objects are visible within the server code of each user session; other objects are visible in the server code across all sessions (multiple users could use a shared variable); and yet others are visible in the server and the ui code across all user sessions. This document describes how scoping works within a single R process.
The awesomeness that is the global.R file. Or how to clean up your shiny appDescription and use of the global.R file
Build a dynamic UI that reacts to user inputShiny apps are often more than just a fixed set of controls that affect a fixed set of outputs. Inputs may need to be shown or hidden depending on the state of another input, or input controls may need to be created on-the-fly in response to user input.
More Shiny ExamplesMore Shiny Examples
Enterprise-ready dashboardsDesign a Shiny Dashboard
Shiny dashboardDesign a Shiny Dashboard
shinymaterialMaterial design in Shiny apps
Alternative Design for ShinyMost Shiny apps out there have a similar design style. It is usually easy for a seasoned Shiny developer to tell the difference between a Shiny app and a standard website. Why is this? Shiny apps ARE websites for all intents and purposes. Why do they not vary as greatly as the rest of the sites we encounter when surfing the web?
shinydashboardPlusshinydashboardPlus is based on the idea of ygdashboard, the latter not compatible with shinydashboard (you cannot use shinydashboard and ygdashboard at the same time). With shinydashboardPlus you can still work with the shinydashboard classic functions and enrich your dashboard with all additional functions of shinydashboardPlus!
shinyMixRThe shinyMixR package is initially developed as a graphical interface for the nlmixr package. The package include a shiny (dashboard) interface and helps in managing, running, editing and analysing nlmixr models. Although the main focus was to build an interface, many of the package functions are also directly available for usage in an interactive R session
HTML TemplatesIn most cases, the best way to create a Shiny application’s user interface is to build it with R code, using functions like fluidPage(), div(), and so on. Sometimes, though, you may want to integrate Shiny with existing HTML, and starting with Shiny 0.13 (and htmltools 0.3), this can be done with the HTML templates. Templates can be used to generate complete web pages, and they can also be used to generate the HTML for components that are included in a Shiny app.
RinteRfaceRinteRface aims at bringing the most famous open source HTML templates to R
ggedit – interactive ggplot aesthetic and theme editorggedit is a package that helps users bridge the gap between making a plot and getting all of those pesky plot aesthetics just right, all while keeping everything portable for further research and collaboration.
Intro to RStudio Addins and Shiny GadgetsR is a powerful programming language for statistical computing with many packages and tools. The goal of this article is to arm you with tools and techniques for using addins and gadgets.
ggthemeassistA RStudio addin for ggplot2 theme tweaking
esqisseRStudio add-in to make plots with ggplot2
Shiny DebuggingDebugging with Shiny
Debugging Shiny applicationsDebugging Shiny applications can be challenging. Because Shiny is reactive, code execution isn’t as linear as you might be used to, and your application code runs behind a web server and the Shiny framework itself, which can make it harder to access. The goal of this article is to arm you with tools and techniques for debugging in Shiny specifically. If you’re interested in tools for debugging R more generally, we recommend reading Debugging with RStudio instead. The Debugging and Exceptions chapter in Hadley Wickham’s excellent book Advanced R is also extremely helpful if you’re new to debugging in R.
Profiling with RStudioGuide to Profiling with RStudio
Debugging in RStudioDebugging techniques in RStudio
Building Shiny Apps: With Great Power Comes Great ResponsibilityPresentation on building and testing Shiny Apps
Getting started with shinytestAfter you get your Shiny application to a state where it works, it’s often useful to have an automated system that checks that it continues to work as expected.
Testing Shiny applications with ShinytestTesting Shiny applications with Shinytest
ArticlesList of RStudio Shiny Articles that go through start to finish of development
Shiny v1.3.2Shiny v1.3.2
Reactivity Pt 1Reactive programming is at the heart of the Shiny framework, and thinking reactively is one of the most difficult yet most rewarding aspects of learning Shiny. This tutorial will go beyond the basics, explaining the philosophy behind Shiny’s reactive programming framework and exploring patterns and techniques for using it well.
Reactivity Pt 2Reactive programming is at the heart of the Shiny framework, and thinking reactively is one of the most difficult yet most rewarding aspects of learning Shiny. This tutorial will go beyond the basics, explaining the philosophy behind Shiny’s reactive programming framework and exploring patterns and techniques for using it well.
Shiny in production: Principles, practices, and toolsShiny is a web framework for R, a language not traditionally known for web frameworks, to say the least. As such, Shiny has always faced questions about whether it can or should be used “in production”. In this talk we’ll explore what “production” even means, review some of the historical obstacles and objections to using Shiny for production purposes, and discuss practices and tools that can help your Shiny apps flourish.
Effective use of Shiny modules in application developmentAs a Shiny application grows in scale, organizing code into reusable and streamlined components becomes vital to manage future enhancements and avoid unnecessary duplication.
Load Testing Shiny ApplicationsThe shinyloadtest package and the accompanying shinycannon software enable load testing deployed Shiny applications.
golemA Framework for Building Robust Shiny Apps
shinymetaThe shinymeta R package provides tools for capturing logic in a Shiny app and exposing it as code that can be run outside of Shiny (e.g., from an R console). It also provides tools for bundling both the code and results to the end user
shinymetaRecord and expose Shiny app logic using metaprogramming
Shiny's Holy Grail: Interactivity with reproducibilityPresentation by Joe Cheng useR! 2019
medplotFunctions for drawing graphs in R visualizing medical information
drakeAn R-focused pipeline toolkit for reproducibility and high-performance computing
renvProject environments for R
[ Packrat}Packrat is a dependency management system for R
Up to Bat with PackratGuide to packrat
Lite Intro to Docker / Rocker for R Analysis via Windows BoxLite Intro to Docker / Rocker for R Analysis via Windows Box
Running RStudio with Docker containersRunning RStudio with Docker containers
Using Docker images with RStudio Server Pro, Launcher, and KubernetesUsing Docker images with RStudio Server Pro, Launcher, and Kubernetes
It's a Nonlinear World - Interactive Dashboard"I created a quick plot, that then turned into a R Notebook, that then turned into an interactive dashboard."
R MarkdownR Markdown
Package VignettesHow to build package vignettes with knitr
rticlesLaTeX Journal Article Templates for R Markdown
Job Scheduling R Markdown Reports via RIntro to Job Scheduling R Markdown Reports via R
rmd2pptContains examples of R Markdown input and PPT output
Distill for R MarkdownDistill for R Markdown is based on the Distill web framework, which was originally created for use in the Distill Machine Learning Journal. Distill for R Markdown combines the technical authoring features of Distill with R Markdown, enabling a fully reproducible workflow based on literate programming.
xaringanAn R package for creating slideshows with remark.js through R Markdown. The package name xaringan comes from Sharingan, a dōjutsu in Naruto with two abilities: the "Eye of Insight" and the "Eye of Hypnotism". A presentation ninja should have these basic abilities, and I think remark.js may help you acquire these abilities, even if you are not a member of the Uchiha clan
The R Markdown Cheat sheetThe R Markdown cheat sheet is a quick reference guide for writing reports with R Markdown
Flexdashboard for Rflexdashboard: Easy interactive dashboards for R
rmdcssCSS templates for R Markdown documents
Markdown CSSA Collection of stylesheets to make generated markdown, or raw HTML, look beautiful
prettydocCreating Pretty HTML From R Markdown
scottishsnowMany reports from 1 RMarkdown file
radix for R MarkdownRadix for R Markdown
Distill for R MarkdownDistill for R Markdown is a web publishing format optimized for scientific and technical communication
Creating a WebsiteShare a set of Distill articles as a website
Word Up - Gotta Get Up To Get BookdownR is a powerful programming language for statistical computing with many packages and tools. The goal of this article is to arm you with tools and techniques for using bookdown and generating word output.
nlmixrnlmixr: an R package for population PKPD modeling
nlmixrdevelopmentRunning PK models with nlmixr
Combining Shiny and R MarkdownR is a powerful programming language for statistical computing with many packages and tools. The goal of this article is to outline some ways to combine Shiny & R Markdown.
gtEasily generate information-rich, publication-quality tables for R
learnrGuide to Create Interactive Tutorials from R Markdown documents
Leaflet for RLeaflet is one of the most popular open-source JavaScript libraries for interactive maps. It’s used by websites ranging from The New York Times and The Washington Post to GitHub and Flickr, as well as GIS specialists like OpenStreetMap, Mapbox, and CartoDB.
rpivotTableA R wrapper for the great library pivottable
DTDT: An R interface to the DataTables library
Learning D3Learning D3
r2d3: R Interface to D3 Visualizationsr2d3: R Interface to D3 Visualizations
Package DevelopmentPackage Development for D3
Interactive Plots in ShinyInteractive Plots in Shiny
RStudio Portfolio Training ExercisesThis document will guide you through a series of exercises that will introduce Shiny, Flexdashboards, R Markdown, parameterized reports, and Plumber APIs. These artifacts will be explored in the context of RStudio Connect.
RStudio 1.2 Preview: Reticulated PythonRStudio 1.2 Preview: Reticulated Python
reticulateR Interface to Python
Reticulated ShinyReticulated Shiny
KerasKeras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Using Python with RStudioDescription of using Python with Rstudio
reticulateR Interface to Python
Reticulated ShinyReticulated Shiny
The Ultimate guide to AI, Data Science & Machine Learning, Articles, Cheatsheets and Tutorials ALL in one placeThis is a carefully curated compendium of articles & tutorials covering all things AI, Data Science & Machine Learning for the beginner to advanced practitioner. I will be periodically updating this document with popular topics from time to time. My hope is that you find something of use and/or the content will generate ideas for you to pursue.
Machine Learning MasteryCollection of Links and tutorials for Machine Learning
Git and GitHub
Happy Git with RInstall Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE. Develop a few key workflows that cover your most common tasks. Integrate Git and GitHub into your daily work with R and R Markdown.
Git HandbookHandbook for Git
Git Cheat SheetsReference sheets covering Git commands, features, SVN migrations, and bash. Available in a multiple languages.
Learn Version Control with Git: A step-by-step course for the complete beginner ebookVersion control is an essential tool if you want to be successful in today's web & software world. This book will help you master it with ease.
Git Cheat SheetDownload our popular cheat sheet for the Git version control system
Git for Subversion UsersSometimes, prior knowledge can be a disadvantage. For example when you're starting with Git - while trying to approach it like a new Subversion. You'll have to let go of a couple of old concepts before you can understand the new ones. Our cheat sheet compares the most important tasks in both systems - and helps you make the switch to Git!
Workflow of Version ControlUnderstand the basic workflow of version control with Git
Xcode Cheat SheetKnowing your tools inside out is paramount - because you spend countless hours with them. If Xcode is one of the tools you use, we have something for you: we created a nice cheat sheet that we would like to share with you. On the front you can find all the essential keyboard shortcuts. On the back, our "tips & tricks" help you get the most out of Xcode.
Command Line Cheat SheetFor many, the command line belongs to long gone days: when computers were controlled by typing mystical commands into a black window; when the mouse possessed no power. But for many use cases, the command line is still absolutely indispensable! Our cheat sheet not only features the most important commands. On the back, it also explains some tips & tricks that make working with the CLI a lot easier.
Bourbon, Bitters, and NeatBourbon, Neat, and Bitters help you produce awesome CSS - with less code and in less time. The popular Sass framework family is the lightweight alternative to whoppers like Bootstrap & Foundation. Our cheat sheet presents the most important mixins and helpers at a glance. On the back, we added a step-by-step instruction on how to set up your projects.
Website Optimization Cheat Sheet25% of users abandon a web page if it takes more than four little seconds to load. This cheat sheet helps you keep the visitors you worked so hard to attract. Let's get your website in front of your users as fast as possible!
Books to Read
Mastering ShinyHadley Wickham
R for Data ScienceHadley Wickham
R Graphics Cookbook, 2nd EditionWinston Chang
An Introduction to Statistical Learning with Applications in RGareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Applied Predictive ModelingMax Kuhn and Kjell Johnson
R Markdown: the Definitive GuideYihui Xie, J. J. Allaire, and Garrett Grolemund
Text Mining with R A Tidy ApproachJulia Silge and David Robinson
Advanced RHadley Wickham
bookdown: Authoring Books and Technical Documents with R MarkdownYihui Xie
Learn Version Control with Git: A step-by-step course for the complete beginner ebookTobias Günther
Examples, Case Studies, Papers, and Presentations from the Clinical Research Industry and Regulators
Pharmacometrics: Some Shiny applicationsExample of Live Data Visualization Pharma Tool / App
ICGC Pancreatic Cancer (Ductal Adenocarcinoma) - Genome ViewerExample of Live Data Visualization Pharma Tool / App
Visualisations of proteomics data using R and BioconductorExample of Live Data Visualization Pharma Tool / App
CanvaseXpressExample of Live Data Visualization Pharma Tool / App
Advanced Visual Analytics of Safety Data from Different Data SourcesPresentation by Melvin S. Munsaka, PHD, AbbVie
Bayesian TutorialThis tutorial is a reasonably self-contained tutorial and documentation source about trasitioning to bayesian analysis from traditional first order estimation techniques for nonlinear-mixed-effects modeling. The overarching objective is to provide a resource for some of the additional complexity that bayesian analysis suggests/requires (mu-modeling, additional estimation tuning, etc) and to compare output under various scenarios to that of FOCE-based estimation. As a secondary objective, this project should serve as a case study to managing a collaborative project using git, github and other ‘modern’ tooling for reproducible science.
Adverse Event Data gRaphicallyPaper DV11 PHUSE EU Connect 2018, Lina Rajput and Prajakta Chitale, Cytel
Adverse Event Data gRaphicallyPresentation DV11 PHUSE EU Connect 2018, Lina Rajput and Prajakta Chitale, Cytel
How do I select an R Package for my clinical workflow?Paper TT11 PHUSE US Connect 2019, Sean Lopp and Phil Bowsher, RStudio
AEPLOTR Package for summarizing Adverse Event Data
Modernizing the Clinical Trial Analysis Pipeline with R and JavaScriptThis repo contains the slides and abstract for the ePoster presented at Rstudio::conf 2019
safetyGraphics: Clinical Trial Safety Graphics with RThe safetyGraphics package provides a framework for evaluation of clinical trial safety in R. It includes several safety-focused visualizations to empower clinical data monitoring. Chief among these is the Hepatic Explorer, based on the Evaluation of the Drug-Induced Serious Hepatotoxicity (eDish) visualization.
Infrastructure for World-Wide Clinical Trials: The BeiGene Case StudyPharmaSUG China 2018, Alan Hopkins PhD, BeiGene
CEDR Application 208573Orig1s000Submission of Venetoclax by AbbVie using R
CEDR Application 209296Orig1s000Submission of Cinvanti by Heron Therapeutics using R
Using R in a regulatory environment: FDA experiencePresentation by Paul Schuette, FDA
R: Regulatory Compliance and Validation Issues A Guidance Document for the Use of R in Regulated Clinical Trial EnvironmentsR: Regulatory Compliance and Validation Issues A Guidance Document for the Use of R in Regulated Clinical Trial Environments
RValidationSlides from R in Pharma Conference
Using R in a Regulatory Environment: some FDA perspectivesPDF file form Paul Schuette (FDA) on using R in a Regulatory Environment
Build your PK modelMarc Lavielle, Live example of Shiny in Pharma
Pharmacometrics: some Shiny applicationsMarc Lavielle, These applications require the mlxR package for the simulation and visualization of longitudinal data.
medplotA Web Application for Dynamic Summary and Analysis of Longitudinal Medical Data Based on R
Bioequivalence v0.3Live example of Shiny in Pharma
Application Development Framwork for R/ShinyPharmaSUG 2018, Ashok Guguganti, Pfizer
Empowering Users By Creating Data Visualization Applications In R/ShinyPharmaSUG 2016, Sudhir Singh, Brian Munneke, Amulya Bista, Jeff Cai, Pharmacyclics LLC
Dynamic Display of Patient ProfilesPaper PP26, PHUSE CSS 2015, Rebeka Tabbey and Wei Wang, Eli Lilly and Company
Reimagining Statistical Reports with R ShinyPaper AD048, PharmaSUG 2019, Sudharsan Dhanavel and Harinarayan Gopichandran, Cognizant Technology Solutions
Why SAS Programmers Should Learn Python TooPaper AD12, PharmaSUG 2018, Michael Stackhouse, Covance
Simplify and Streamline Using PythonPharmaSUG 2018, Michael Stackhouse, Covance
Ensuring Programming Integrity with Python: Dynamic Code Plagiarism DetectionPaper TT04, PHUSE US Connect 2019, Michael Stackhouse, Covance
Tame Your SHARE with a PYTHON and SASPaper PP13, PHUSE US Connect 2019, Michael Stackhouse and Terek Peterson, Covance
Cluster Analysis: What it is and How to Use ItPaper ST183, PharmaSUG 2019, Alyssa Wittle and Michael Stackhouse, Covance
Impact of HIV Pre-Exposure Prophylaxis among MSM in the United StatesLive Shiny Development example
CDC Zika DataLive Shiny Development example
IMMUNOGENICITYLive Shiny Development example
Developing and deploying large scale Shiny applications for non-life insuranceVideo presentation of deploying large scale Shiny applications using HTMLWidgets and HTMLTemplates
Integrate Shiny with existing HTMLLive Example of Shiny HTML Templates
rpharma-demoThis repo contains an example Shiny app that demonstrates some features that may be particularly useful to pharma
FDA Adverse Event DashboardLive Example of Shiny Dashboard
Principles and Guidelines for Reporting Preclinical ResearchNotes on Reproducibility in Pharma
The Economics of Reproducibility in Preclinical ResearchNotes on Reproducibility in Pharma
Reproducible research is still a challengeNotes on Reproducibility in Pharma
The reproducibility crisis in science and prospects for RNotes on Reproducibility in Pharma
A statistical definition for reproducibility and replicabilityNotes on Reproducibility in Pharma
statwonkA dashboard to explore, monitor and learn about OpenFDA data.
Resources Links on Using R in Regulated Clinical Trial EnvironmentsA large list of Resources Links on Using R in Regulated Clinical Trial Environments
Using Flexdashboards to Monitor Clinical ResearchUsing Flexdashboards to Monitor Clinical Research
Cancer prediction using caret (from Ch. 3 of ‘Machine Learning with R’)Cancer prediction using caret (from Ch. 3 of ‘Machine Learning with R’)
TCGA PRADTCGA prostate cancer differential expression by race
GLMMGLMM with various R packages
A not so short review on survival analysis in RThe aim of this document is to give a short but yet comprehensive review on how to conduct survival analysis in R. The literature on the topic is extensive and only a limited number of (common) problems/features will be covered. The amount of R packages available reflects the extent of the research on the topic. A broad (yet not complete) task view presenting useful R packages for different aspects of survival analysis can be found on the dedicated CRAN Task View at
Access to Hospital Care DashboardAccess to Hospital Care Dashboard
Comparative Protein Structure Analysis with Bio3DBio3D is an R package that provides interactive tools for the analysis of bimolecular structure, sequence and simulation data. The aim of this document, termed a vignette in R parlance, is to provide a brief task-oriented introduction to facilities for analyzing protein structure data with Bio3D
Survival AnalysisSurvival Analysis
Keynote EARL London 2018 - Garrett Grolemund, RstudioKeynote EARL London 2018 - Garrett Grolemund, Rstudio
R Markdown for MedicineA four-hour workshop that will take you on a tour of how to get from data to manuscript using R Markdown
openfdaConvenient access to the OpenFDA API
openfda-dashboardOpenFDA Dashboard
openFDALive OpenFDA Dashboard
BigQuery public datasetsBigQuery public datasets
clinical-drugsRxNorm was created by the U.S. National Library of Medicine (NLM) to provide a normalized naming system for clinical drugs, defined as the combination of {ingredient + strength + dose form}
Gene Expression BiclusteringLive example of Flexdashboard
Iris K-Means ClusteringLive example of Flexdashboard
[1]Live example of Flexdashboard
Population Health Data Science with RLive example of bookdown
Visualizing US Clinical TrialsVisualizing U.S. Clinical Trials
Creating Simplified TS.XPT FilesGuidance from the FDA on creating TS.XPT files using R or Python with instructions and code

Disclaimer: The educational material/courses provided above are for information sharing purpose only and not for any promotional intent. OSTCR, the PHUSE Working Group project, does not recommend or rank any courses in anyway and are only listed to provide information about their availability. If you would like to provide information about training options you know about please email

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