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Course Outline

Introduction to Shiny

  • Overview of Shiny and its underlying mechanics
  • Installation procedures and basic configuration
  • Exploring Shiny examples and the gallery

UI and Server Architecture

  • Comprehending the ui.R and server.R components
  • Utilizing fluidPage(), sidebarLayout(), and other layout functions
  • Structuring inputs and outputs

Reactivity and Dynamic Interactions

  • Reactive expressions and observers
  • Managing application behavior through reactive inputs
  • Debugging reactivity-related issues

Data Visualization and Reporting

  • Integrating ggplot2 and plotly into Shiny apps
  • Creating reactive tables using DT or reactable
  • Producing downloadable reports with rmarkdown

Advanced UI and Customization

  • Incorporating tabs, conditional panels, and modals
  • Applying custom CSS and themes
  • Leveraging Shiny modules for code reusability

Deployment and Hosting

  • Publishing apps to Posit Cloud or Shinyapps.io
  • Running apps locally and on Shiny Server
  • Handling dependencies and version control

Case Study and Application Design

  • Constructing a full-featured dashboard from the ground up
  • Implementing interactive filters and user-driven insights
  • Strategies for performance, security, and scalability

Summary and Next Steps

Requirements

  • A solid understanding of R programming
  • Experience in data analysis or visualization
  • Basic familiarity with HTML and CSS is beneficial but not mandatory

Target Audience

  • Data analysts and scientists
  • R developers looking to create interactive dashboards
  • Researchers and educators presenting data for public or internal audiences
 14 Hours

Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793

Testimonials (3)

a multitude of points

Joanna - Instytut Ekonomiki Rolnictwa i Gospodarki Zywnosciowej-PIB
Course - Statistical Analysis with Stata and R

knowledge of the trainer, tailor based, all topics covered

eleni - EUAA
Course - Forecasting with R

The real life applications using Statcan and CER as examples.

Matthew - Natural Resources Canada
Course - Data Analytics With R

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