Dashboard Data Visualization in R
Building Interactive, Decision-Ready Dashboards for Health, Business & Research Analytics
Rolling Basis – Course offering for new cohort begins every 5 weeks
Live via Zoom
Dashboard Data Visualization in R (12-Day Weekend Program)
Duration: 8 days (4 weeks)
Schedule: Saturday/Sunday, 8:30am-10:00am ET (New York Time)
Registration closes: October 31, 2025
Format: Live via Zoom
Payment Options: Bank Transfer, Venmo, or Zelle accepted.
Course Fee: $250 (limited scholarships and variable pricing available based on Country of participant)
About the Training
This course provides an applied introduction to building interactive dashboards in R, from linked charts and tables through fully deployed, reactive Shiny applications.
Participants begin with interactive charting and linked views, progress through Quarto dashboards and Shiny’s reactive programming model, and conclude with a production-grade, deployed dashboard.
The course emphasizes dashboard design and usability for real decision-makers, not just technical implementation, so the output is something stakeholders actually use — in business analytics, health systems, and research reporting contexts.
- Instructor-led, live Zoom sessions
- Real business, health, and research datasets
- Linked charts → Quarto dashboards → reactive Shiny progression
- Strong focus on dashboard design & stakeholder usability
- Capstone: a deployed, production-grade interactive dashboard
Who Should Register
Analysts and researchers new to R, R users who are new to dashboarding, Experienced analysts wanting production-grade Shiny and deployment skills
- Basic R and statistics familiarity
- No prior ML experience required
- Conceptual, not purely mathematical approach
Learning Objectives
- Build interactive charts and linked visualizations using plotly, DT, and crosstalk that work without a Shiny server.
- Design and publish a Quarto dashboard combining KPI value boxes, charts, and tables in a shareable, static HTML layout.
- Apply Shiny's reactive programming model to build dashboards that respond to user input in real time.
- Combine plotly, DT, and crosstalk inside a Shiny app with bslib theming to produce a polished, production-grade interface.
- Deploy an interactive dashboard to a live environment (e.g., shinyapps.io) that a non-technical stakeholder can use without opening R.
Apply machine learning workflows in R for real-world datasets.
Build, tune, and evaluate predictive and unsupervised models.
Implement feature engineering and interpret model outputs.
Communicate findings using reproducible R Markdown or Shiny dashboards.
Design and present an end-to-end ML project.
Weekly Breakdown
Each week includes:
🎓 Lecture slides (PowerPoint)
💻 R lab exercises (shiny + plotly)
📊 Synthetic retail/business datasets
Computing & Tools
Quantitative concepts are illustrated in R using packages like pwr, dagitty, broom, and ggplot2, though the course is designed to be software-agnostic — every method translates directly to SPSS or Stata if that’s your team’s primary tool.
All case studies, discussion materials, and R illustrations are downloadable from the course portal.