Interactive Data Storytelling: Swapping Horizontal Scrolling for Dynamic Zone Visibility

My previous work with The Heart Disease UCI Dataset focused on the foundational layer—utilizing descriptive statistics in PostgreSQL to build robust architectures and ensure every query returned reliable, high-integrity results. However, once the data is structured, the challenge shifts from backend logic to user experience (UX). For this phase, I wanted to focus on the end-user’s journey, moving beyond the static design patterns I had previously relied on.

In earlier projects (Sepsis), my standard approach for managing multiple visualizations was horizontal scrolling. While functional, it often required the reader to mentally juggle data points that were no longer visible on the screen. This created an "analytical gap" where the user had to choose between seeing the big picture or the granular details. To evolve this experience, I decided to master Dynamic Zone Visibility (DZV)—transforming a traditional dashboard into a responsive, "app-like" clinical tool that keeps all relevant context within a single, focused frame.

The Vision: Complexity through Simplicity

I intentionally chose simple, familiar chart types—like donut charts, box plots, and heatmaps—rather than chasing complex visuals for the sake of novelty. In a clinical environment, clarity is paramount. By using simple representations, I want to ensure that the data remained the focal point, allowing the Dynamic Zone Visibility to handle the heavy lifting of organization and navigation.

What is Dynamic Zone Visibility (DZV)?

DZV allows you to control the visibility of an entire Dashboard Zone—be it a single sheet or a vertical/horizontal container. Unlike older sheet-swapping tricks, when a zone is hidden via DZV, it is effectively removed from the dashboard’s rendering cycle. The space it occupied is completely reclaimed, allowing active zones to expand and fill the void perfectly.

The Basic Criteria for DZV

To implement this from scratch, a dashboard must meet two strict criteria:

• Boolean Logic: Visibility must be controlled by a Boolean (True/False) calculation or a single-value parameter.

• Independence: The field must be independent of the viz's level of detail; it must be a constant value across the dashboard.

The Interactive Workspace: A Deep Dive into Clinical Evidence

I organized the visualizations into five distinct thematic views, each designed to answer a specific clinical question.

1. High Risk Analysis: Mapping the Patient Journey

This view pairs a Sankey Chart (Risk Categorization Map) with a KPI Cholesterol donut chart.

• The Insight: The Sankey reveals the "pathway" of risk, showing how patients flow from their initial stress levels into final cholesterol categories. It allows a practitioner to see if high-stress patients inevitably end up in the high-cholesterol bracket.

• The Benchmark: The donut chart provides an immediate anchor, highlighting the Average Cholesterol (246.26) in the center. This "Big Number" approach gives the user a baseline before they begin exploring the individual flows of the Sankey.

  
  

  

2. Clinical Details: The Source of Truth

Transparency is the backbone of clinical storytelling. This view provides a comprehensive Clinical Table that functions as the dashboard's evidence locker.

• The Insight: While other charts show trends, this table shows the people. It allows for a row-by-row deep dive into individual patient profiles, listing Avg. Chol, Avg. Resting BP, and Avg. Stress Indicators.

• Validation: By grouping data by Target (Disease group vs. No Disease), gender, and age, it ensures that every visual trend seen in the other views can be immediately verified by the raw data, building trust with the clinical user.

3. Statistical Distribution: Visualizing Variance

This view shifts the focus from totals to distribution using Box-and-Whisker Plots and Jittered Scatter Plots.

• The Insight: The Box-and-Whisker plot proves that the diseased group doesn't just have a higher average cholesterol; they have a significantly higher concentration of individuals at the extreme upper end of the scale.

• Clinical Danger Zones: By plotting individual points against a "Typical Healthy Range," the Jitter plot visually isolates the "outliers"—those patients sitting in the clinical danger zone who might be missed in a simple average.

  

4. Symptom Heat Map: A Prevalence Study

This squared highlight table quantifies symptom frequency with surgical precision.

• The Insight: It provides an immediate visual answer to "What is the most common warning sign?" In this dataset, it clearly shows that "Typical Angina" is the dominant red flag for the high-risk group, accounting for 104 reported cases. The color intensity allows the user to rank symptoms by severity at a single glance.

  
  

5. Correlation: Measuring Physiological Capacity

This scatter plot explores the relationship between biological age and cardiovascular performance.

• The Insight: This confirms the clinical reality that heart rate capacity decreases as patients age.

• Technical Refinement: By unchecking "Include Zero" on the Y-axis, I "zoomed in" on the 110–200 BPM range. This makes the negative trend line much more impactful, illustrating the steady rate of physiological decline without the wasted white space of a 0-baseline.

  
  

  

  
  

Technical Deep-Dive: Engineering the no scroll interface:

To transition from a traditional scrolling layout to a dynamic clinical app, I built a custom logic engine using Dynamic Zone Visibility (DZV). This system ensures that only the relevant data view is rendered at any given time, allowing the active chart to reclaim 100% of the dashboard's space without leaving "ghost" white space.

Step 1: The Master Parameter

The foundation of this interaction is a String Parameter titled Dashboard View Select. This parameter acts as the central command, holding a static list of the five distinct views I engineered for the dashboard:

• High Level Risks

• Clinical Details

• Statistical Distribution

• Symptom Heatmap

• Correlation

Step 2: The Boolean Logic

Once the "Brain" (the parameter) was built, I needed a way to translate those text values into a language the dashboard containers could understand. Tableau’s Dynamic Zone Visibility requires a Boolean (True/False) result to trigger a zone’s appearance.

Here are the five calculated fields used for this dashboard:

• Show Overview: [Dashboard View Select] = "High Level Risks"

• Show Details: [Dashboard View Select] = "Clinical Details"

• Show Stats: [Dashboard View Select] = "Statistical Distribution"

• Show Heatmap: [Dashboard View Select] = "Symptom Heatmap"

• Show Correlation: [Dashboard View Select] = "Correlation"

Step 3: The Layout Implementation

The final step involved linking the visual containers to the logic in the Layout Tab:

1. I organized each chart into a dedicated vertical or horizontal container.

2. I selected the container and enabled the "Control visibility using value" option.

3. I assigned the corresponding Boolean calculated field to that container.

When the parameter changes, Tableau instantly evaluates which calculation is TRUE and collapses all FALSE containers, seamlessly swapping the view.

While Dynamic Zone Visibility has been a part of the Tableau toolkit for a few years and has been explored by many, I really wanted to dive in and see how it could be used to solve the "analytical gap" in clinical data. I haven't invented a new feature here; rather, I wanted to share my journey of exploring this powerful tool to show how it can help a doctor or researcher find answers without getting lost in a sea of scrolling charts.

By combining the "behind-the-scenes" power of PostgreSQL with this interactive logic, I’ve explored a workflow that I’m truly proud of. It allows you to see the big picture of heart health while still being able to zoom in on the specific details that matter most.

I loved the process of exploring how these established features can make data feel more intuitive. If you haven't tried DZV yet, I highly recommend giving it a go—it’s a fantastic way to make complex data accessible for everyone, and I’m excited to keep learning and building more tools like this 😊

References:

• Tableau Learning Resources:

  • Dynamic Zone Visibility (DZV): Explored via Tableau’s official documentation and community tutorials on reclaiming dashboard space.

  • Sankey Mapping: Built using Tableau viz extension.

• Video Tutorials & Documentation:

  • Tableau YouTube Community: Highlighting techniques for parameter-driven navigation and "app-style" interfaces.

  • Online Clinical Analytics Articles: Insights on using Box Plots and Heatmaps for clearer medical data storytelling.