A dashboard should answer a question someone is stuck on

Why the maize yield app runs ANOVA, Tukey HSD and prediction live inside the dashboard instead of displaying pre-calculated charts.
Dashboard Design
R Shiny
Statistics
Case Study
Author

Victor Mandela

Published

18 September 2026

Keywords

agricultural analytics, R Shiny dashboard, maize yield, Rwanda, dashboard design, r shiny, statistics

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For the maize yield project, I designed the Shiny application around four analytical questions:

  1. Which urea treatment performed best?
  2. What factors are driving maize yield?
  3. Can yield be predicted?
  4. Where were the trials, and how did yields vary spatially?

This meant embedding actual statistical analysis inside the application, not just displaying pre-calculated charts.

Workflow: ANOVA → Tukey HSD → Yield drivers → Prediction → Interactive mapping

A dashboard should answer a question someone is stuck on — infographic

The difference shows the moment a user filters

If the statistics are live, a filtered comparison is still a valid comparison — with its own test and its own uncertainty. If they’re pre-calculated, filtering produces something that looks like analysis but isn’t one.

A good dashboard is an interface for a decision, not a gallery of charts.

▶ Try it yourself — live R Shiny demo. Open the Rwanda Maize Yield Analyzer · Username: test · Password: test

Part 7 of 10 — The Maize Yield Case Study. Read the full case study and launch the dashboard › · Work with me on a similar project ›