Introduction

In today’s world, visualizing data is a critical skill for both analysts and decision-makers. R is a powerful language for statistical computing and graphics, and when combined with Shiny, it allows users to create interactive, web-based visualizations and dashboards. This advanced course is designed to teach participants how to leverage R and Shiny to build sophisticated, dynamic visualizations and real-time data dashboards. By the end of the course, you will have the skills to design and deploy interactive data visualizations that can transform the way users interact with data.

Objectives

By the end of this course, participants will:

• Understand the fundamentals of data visualization using R and Shiny.
• Learn how to build interactive visualizations and dashboards with Shiny and other R visualization libraries such as ggplot2 and plotly.
• Explore how to work with dynamic, real-time data inputs in Shiny applications.
• Master customizations and optimizations for building user-friendly data dashboards.
• Integrate Shiny applications into web servers and deploy them for sharing with stakeholders.
• Learn best practices for creating interactive, effective data visualizations that engage users.

Who Should Attend?

This course is ideal for:

• Data scientists, analysts, or anyone who wants to build interactive data visualizations.
• R developers and users looking to expand their skills in data visualization and web applications.
• Business intelligence professionals interested in creating dashboards for decision-making.
• Anyone interested in learning how to deploy web-based visualizations and applications using R and Shiny.

Day 1: Introduction to Data Visualization with R

Morning Session: Getting Started with R

• Introduction to R: A brief overview of R language and IDEs like RStudio
• Essential R packages for data visualization: ggplot2, plotly, and others
• Working with data in R: Importing, cleaning, and transforming data with dplyr and tidyr
• Introduction to ggplot2: Creating static visualizations (scatter plots, bar charts, histograms)

Afternoon Session: Fundamentals of Data Visualization

• Principles of good data visualization: Clarity, simplicity, and accuracy
• Choosing the right chart type for different datasets and objectives
• Customizing visualizations in R: Themes, colors, labels, and legends
• Hands-on: Creating basic visualizations using ggplot2 and fine-tuning them for better presentation

Day 2: Interactive Data Visualizations with Shiny

Morning Session: Introduction to Shiny

• What is Shiny? Overview of its capabilities for building interactive web applications
• Understanding the basic structure of a Shiny app: UI (user interface) and server components
• Hands-on: Building a simple Shiny app with basic inputs and outputs (e.g., text, numeric inputs)
• Understanding reactivity in Shiny: Reactive inputs and outputs

Afternoon Session: Creating Interactive Visualizations

• Introduction to interactive visualization in Shiny with ggplot2
• Adding interactivity: Dynamic inputs like sliders, dropdowns, and checkboxes to control visualizations
• Displaying plots dynamically with reactive expressions
• Hands-on: Building an interactive scatter plot that updates based on user input

Day 3: Advanced Shiny Techniques and Customizations

Morning Session: Working with Plotly and Interactive Charts

• Introduction to Plotly for R: Creating interactive visualizations in Shiny apps
• Creating and customizing interactive plots (line charts, bar charts, 3D plots)
• Adding interactive features: Hover information, tooltips, and zoom controls
• Hands-on: Building a dynamic line chart with Plotly in a Shiny app

Afternoon Session: Building Interactive Dashboards with Shiny

• Introduction to dashboard layouts in Shiny: shinydashboard and flexdashboard
• Organizing and arranging plots, tables, and widgets into a cohesive dashboard
• Adding interactivity to dashboards: Linking multiple plots and inputs for dynamic updates
• Hands-on: Designing and building a basic Shiny dashboard with multiple visualizations and interactivity

Day 4: Real-Time Data and Advanced Customization in Shiny

Morning Session: Working with Real-Time Data in Shiny

• Connecting Shiny apps to real-time data streams (e.g., live API data, databases, or files)
• Handling dynamic data updates and automatic refreshes
• Displaying and visualizing real-time data in Shiny: Updating plots and outputs without refreshing the entire page
• Hands-on: Building a Shiny app that pulls real-time data from an API and updates the visualization in real-time

Afternoon Session: Advanced Customization and Optimization

• Customizing the appearance of Shiny apps: Themes, CSS, and HTML integration
• Performance optimization: Efficient handling of large datasets and reactive expressions
• Best practices for improving the user experience: UI/UX tips and responsive design
• Hands-on: Customizing the look and feel of a Shiny app and optimizing it for performance

Day 5: Deploying and Sharing Shiny Applications

Morning Session: Sharing Shiny Applications

• Introduction to Shiny Server and ShinyApps.io: Deploying Shiny apps to the web
• Setting up and deploying Shiny apps on a server
• Introduction to cloud services for hosting Shiny apps (e.g., AWS, Azure, Google Cloud)
• Hands-on: Deploying a Shiny app to ShinyApps.io or a local Shiny Server

Afternoon Session: Final Project and Course Wrap-Up

• Final project: Participants will build an interactive dashboard or Shiny app using real-world datasets
  • The project will include multiple visualizations, dynamic inputs, and real-time data features
• Peer review and feedback: Presenting and critiquing participants’ final projects
• Q&A session and advice for future development in Shiny and R
• Closing remarks and next steps for further learning and professional development

Materials and Tools:

• Required tools: R, RStudio, ggplot2, plotly, shinydashboard, Shiny, shinydashboard, ShinyServer
• Access to sample datasets and APIs for real-time data exploration
• Example codebases and templates for building Shiny apps
• Recommended reading on R programming, data visualization, and interactive web applications