Mastering Shiny : build interactive apps, reports, and dashboards powered by R

Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. * **Get started:** Discover how the major pieces of a Shiny app fit together * **Put Shiny in action:** Explore Shiny functionality with a focus on code samples, example apps, and useful techniques * **Master reactivity:** Go deep into the theory and practice of reactive programming and examine reactive graph components * **Apply best practices:** Examine useful techniques for making your Shiny apps work well in production Cover Copyright Table of Contents Preface What Is Shiny? Who Should Read This Book? What Will You Learn? What Won’t You Learn? Prerequisites Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments How This Book Was Built Part I. Getting Started Chapter 1. Your First Shiny App Introduction Create App Directory and File Running and Stopping Adding UI Controls Adding Behavior Reducing Duplication with Reactive Expressions Summary Exercises Chapter 2. Basic UI Introduction Inputs Common Structure Free Text Numeric Inputs Dates Limited Choices File Uploads Action Buttons Exercises Outputs Text Tables Plots Downloads Exercises Summary Chapter 3. Basic Reactivity Introduction The Server Function Input Output Reactive Programming Imperative Versus Declarative Programming Laziness The Reactive Graph Reactive Expressions Execution Order Exercises Reactive Expressions The Motivation The App The Reactive Graph Simplifying the Graph Why Do We Need Reactive Expressions? Controlling Timing of Evaluation Timed Invalidation On Click Observers Summary Chapter 4. Case Study: ER Injuries Introduction The Data Exploration Prototype Polish Tables Rate Versus Count Narrative Exercises Summary Part II. Shiny in Action Chapter 5. Workflow Development Workflow Creating the App Seeing Your Changes Controlling the View Debugging Reading Tracebacks Tracebacks in Shiny The Interactive Debugger Case Study Debugging Reactivity Getting Help Reprex Basics Making a Reprex Making a Minimal Reprex Case Study Summary Chapter 6. Layout, Themes, HTML Introduction Single-Page Layouts Page Functions Page with Sidebar Multirow Exercises Multipage Layouts Tabsets Navlists and Navbars Bootstrap Themes Getting Started Shiny Themes Plot Themes Exercises Under the Hood Summary Chapter 7. Graphics Interactivity Basics Clicking Other Point Events Brushing Modifying the Plot Interactivity Limitations Dynamic Height and Width Images Summary Chapter 8. User Feedback Validation Validating Input Canceling Execution with req() req() and Validation Validate Output Notifications Transient Notification Removing on Completion Progressive Updates Progress Bars Shiny Waiter Spinners Confirming and Undoing Explicit Confirmation Undoing an Action Trash Summary Chapter 9. Uploads and Downloads Upload UI Server Uploading Data Download Basics Downloading Data Downloading Reports Case Study Exercises Summary Chapter 10. Dynamic UI Updating Inputs Simple Uses Hierarchical Select Boxes Freezing Reactive Inputs Circular References Interrelated Inputs Exercises Dynamic Visibility Conditional UI Wizard Interface Exercises Creating UI with Code Getting Started Multiple Controls Dynamic Filtering Dialog Boxes Exercises Summary Chapter 11. Bookmarking Basic Idea Updating the URL Storing Richer State Bookmarking Challenges Exercises Summary Chapter 12. Tidy Evaluation Motivation Data-Masking Getting Started Example: ggplot2 Example: dplyr User-Supplied Data Why Not Use Base R? Tidy-Selection Indirection Tidy-Selection and Data-Masking parse() and eval() Summary Part III. Mastering Reactivity Chapter 13. Why Reactivity? Introduction Why Do We Need Reactive Programming? Why Can’t You Use Variables? What About Functions? Event-Driven Programming Reactive Programming A Brief History of Reactive Programming Summary Chapter 14. The Reactive Graph Introduction A Step-by-Step Tour of Reactive Execution A Session Begins Execution Begins Reading a Reactive Expression Reading an Input Reactive Expression Completes Output Completes The Next Output Executes Execution Completes, Outputs Flushed An Input Changes Invalidating the Inputs Notifying Dependencies Removing Relationships Re-execution Exercises Dynamism The Reactlog Package Summary Chapter 15. Reactive Building Blocks Reactive Values Exercises Reactive Expressions Errors on.exit() Exercises Observers and Outputs Isolating Code isolate() observeEvent() and eventReactive() Exercises Timed Invalidation Polling Long-Running Reactives Timer Accuracy Exercises Summary Chapter 16. Escaping the Graph Introduction What Doesn’t the Reactive Graph Capture? Case Studies One Output Modified by Multiple Inputs Accumulating Inputs Pausing Animations Exercises Antipatterns Summary Part IV. Best Practices Chapter 17. General Guidelines Introduction Code Organization Testing Dependency Management Source Code Management Continuous Integration/Deployment Code Reviews Summary Chapter 18. Functions File Organization UI Functions Other Applications Functional Programming UI as Data Server Functions Reading Uploaded Data Internal Functions Summary Chapter 19. Shiny Modules Motivation Module Basics Module UI Module Server Updated App Namespacing Naming Conventions Exercises Inputs and Outputs Getting Started: UI Input and Server Output Case Study: Selecting a Numeric Variable Server Inputs Modules Inside of Modules Case Study: Histogram Multiple Outputs Exercises Case Studies Limited Selection and Other Wizard Dynamic UI Single Object Modules Summary Chapter 20. Packages Converting an Existing App Single File Module Files A Package Benefits Workflow Sharing Extra Steps Deploying Your App-Package R CMD check Summary Chapter 21. Testing Testing Functions Basic Structure Basic Workflow Key Expectations User Interface Functions Workflow Code Coverage Keyboard Shortcuts Workflow Summary Testing Reactivity Modules Limitations Testing JavaScript Basic Operation Case Study Testing Visuals Philosophy When Should You Write Tests? Summary Chapter 22. Security Data Compute Resources Chapter 23. Performance Dining at Restaurant Shiny Benchmark Recording Replay Analysis Profiling The Flame Graph Profiling R Code Profiling a Shiny App Limitations Improve Performance Caching Basics Caching a Reactive Caching Plots Cache Key Cache Scope Other Optimizations Schedule Data Munging Manage User Expectations Summary Index About the Author Colophon Master The Shiny Web Framework--and Take Your R Skills To A Whole New Level. Shiny Helps You Create Fully Interactive Web Apps For Data Analyses By Letting You Move Beyond Static Reports, Tables, And Graphs. Your Users Will Be Able To Jump Between Datasets, Explore Different Subsets, Run Models With Parameter Values Of Their Choosing, Customize Visualizations, And Much More. Hadley Wickham From Rstudio Shows Data Scientists, Data Analysts, Statisticians, And Scientific Researchers With No Knowledge Of Html, Css, Or Javascript How To Create Complex Shiny Apps. Shiny Is Easy To Learn, But Even Intermediate Users Often Wonder What They've Missed. This In-depth Introduction Provides A Learning Path That You Can Follow With Confidence. Getting Started: Begin With A Tutorial-style Exploration Of The Basics Shiny In Action: Explore Shiny Functionality With A Focus On Code Samples And Example Apps Best Practices: Learn Techniques For Managing Complexity And Ensuring Correctness, And Explore Ways To Measure And Improve Your Shiny App's Scalability Mastering Reactivity: Learn The Underlying Theory Of Reactivity To Improve Your Ability To Reason About Complex Shiny Apps