Modularity is what lets an analysis survive its second request

How structuring an R Shiny application into modules keeps an analytics tool maintainable as stakeholders ask for more.
R Shiny
Software Engineering
Dashboard Design
Case Study
Author

Victor Mandela

Published

23 September 2026

Keywords

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

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Victor Mandela, M&E and data science consultant
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Instead of one large Shiny script, I structured the application into modules:

Modularity is what lets an analysis survive its second request — infographic

Analytical applications grow — predictably

A project starts with one chart. Then: “Can we filter by district?” “Can we add rainfall?” “Can we predict yield?” “Can we map the trials?” “Can users download results?”

In a monolithic script, every one of those raises the chance of breaking something unrelated — and the fear of breaking things is what eventually stops the tool being improved at all.

With modules, components can be developed, tested and replaced independently. The objective isn’t just to produce an analysis. It’s to build something still being used a year later.

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