Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Shiny
- Overview of Shiny and its core functionality.
- Installation and initial setup procedures.
- Exploration of Shiny examples and the gallery.
UI and Server Architecture
- Understanding the ui.R and server.R components.
- Utilizing fluidPage(), sidebarLayout(), and other layout functions.
- Designing inputs and outputs.
Reactivity and Dynamic Interactions
- Working with reactive expressions and observers.
- Controlling application behavior through reactive inputs.
- Debugging reactivity issues.
Data Visualization and Reporting
- Integrating ggplot2 and plotly into Shiny apps.
- Creating reactive tables using DT or reactable.
- Generating downloadable reports with rmarkdown.
Advanced UI and Customization
- Incorporating tabs, conditional panels, and modals.
- Applying custom CSS and themes.
- Utilizing Shiny modules for code reuse.
Deployment and Hosting
- Deploying applications to Posit Cloud or Shinyapps.io.
- Running apps locally and on Shiny Server.
- Managing dependencies and versions.
Case Study and Application Design
- Building a full-featured dashboard from the ground up.
- Implementing interactive filters and user-driven insights.
- Best practices for performance, security, and scalability.
Summary and Next Steps
Requirements
- A solid understanding of R programming.
- Experience in data analysis or visualization.
- Familiarity with HTML and CSS is beneficial, though not mandatory.
Target Audience
- Data analysts and scientists.
- R developers looking to create interactive dashboards.
- Researchers and educators who visualize data for internal or public audiences.
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.
