Interactive Data Viz: Creating Visuals That Respond to User Input
Static data visualizations limit audience engagement. Interactive data visualizations empower people to manipulate and filter data directly through user inputs like hovers, clicks, touches and scrolls. This direct involvement unlocks deeper insights from data exploration.
This guide covers using interactivity to create more engaging, insightful data visualizations. We will explore:
- Benefits of adding interactivity to data visuals
-Types of inputs and behaviors to enable audience participation
- Design principles for intuitive interactive experiences
- Tools and templates for interactive data visualization
- Techniques to maximize flexibility of user control
- Pitfalls of ineffective interactive data viz to avoid
- Real-world examples of impactful interactive visuals
By the end, you will have strategies to craft interactive data stories that respond fluidly to audience interests for a more satisfying, illuminating discovery experience.
Why Add Interactivity to Data Visualizations?
Some key benefits interactivity brings to data visualization include:
Interactions like clicks and toggles engage audiences actively versus passively.
Users can manipulate data freely to pursue insights tailored to their interests.
Design guided stories by revealing data sequentially based on interactions.
Filtering and segmentation focuses data views to individual user needs.
Changes in response to interaction clarify how variables interrelate.
Details on Demand
Hover and click behaviors allow user-driven deeper dives into data details.
Tutorial overlays introduce interactivity and capabilities.
User control over text size, color, pacing improves accessibility.
Adding thoughtfully designed interactivity amplifies data impact through active user participation.
Types of Interactive Behaviors to Incorporate
Consider enabling these interactive inputs:
Display data details, metadata or helper text on hover.
Click/tap to trigger actions like filtering, animating objects, or navigating.
Sliders and Filters
Manipulate data ranges and filter categories via UI controls.
Parallax, timed sequencing, or scroll-driven navigation.
Sorting and Segmenting
Reorder or segment data subsets in response to user input.
Toolbars and Menus
Offer settings and options for manipulating data views.
Zooming and Panning
Zoom into data frames for a focused view.
Enable exporting customized dataset views.
Thoughtfully selected interactive behaviors align to user needs and analysis goals.
Design Principles for Intuitive Interactivity
Some key principles for interactive data visualization design:
Clear Entry Points
Establish obvious triggers for initiating interaction like play buttons or clickable icons.
Leverage familiar input widgets like scrollytelling, drop downs, and search bars.
Show previews on hover and confirm actions visually.
Interactions should have logical, consistent effects users can anticipate.
Limit types of behaviors to avoid overwhelming complexity.
Unfold details across a narrative using a clear information hierarchy. Don’t show everything upfront.
Guidelines and Instructions
Offer subtle tutorials, tooltips and cues guiding how to interact.
Appropriate Interactivity Types
Ensure behaviors align well with analysis and communication goals.
Thoughtful design allows seamless exploration without confusion.
Interactive Data Viz Tools and Templates
Many tools support building interactive data visualizations:
Tableau – Drag and drop workflows enable interactivity like filtering, tooltips and clicking to reveal details.
Google Data Studio – Easy templates for interactive reporting dashboards responding to selections.
Flourish – Build interactive visual stories with scrolling, zooming, filtering and details on demand.
ArcGIS StoryMaps – Mix text narratives, data visuals, video and scrollytelling into engaging interactive stories.
Shorthand Stories – Scrollable stories with embedding interactive data viz from tools like Flourish and Tableau.
RAWGraphs – Web tool for generating responsive, customizable interactive data visualizations.
Microsoft Power BI – Interactive data exploration dashboards with filtering, tooltips, and drilling.
Visme – Interactivity widgets like hotspots, click to reveals, slideshows, and data filtering.
Explore integrated solutions as well as combining lightweight tools for customizability.
Increase Flexibility of User Control
Some techniques to expand user interactivity:
- Allow combining multiple filters and inputs for deep customization
- Use expanding stacked filters or checkboxes to refine many facets
- Incorporate before/after toggles enabling comparison of different views
- Offer data downloads filtered to user specifications
- Allow uploading custom datasets to visualize and manipulate
- Support responsive resizing and full-screen expansion
- Let users adapt color schemes to suit different visual needs
- Enable free zooming, panning, item selection across data space
Flexibility empowers users to mold views to their goals without restrictions.
Pitfalls to Avoid With Interactive Data Viz
Beware these common interactivity missteps:
No Clear First Step
Lacking obvious triggers leaves users unsure how or where to engage.
Interactions should behave consistently with user expectations.
Too many competing interactive elements cause confusion.
Lack of Visual Confirmation
Show previews on hovers and make selections clear.
Avoid unnecessary interactions that don’t aid comprehension.
Constraining combinations of filters inhibits deep exploration.
Don’t assume interactions are self-evident without any help text.
Interactivity Disconnected From Story
Enable behaviors reinforcing key data narratives vs gratuitous motion.
Clean design, smart scope and intuitive controls avoid frustrating users.
Real World Examples of Impactful Interactive Data Viz
Let’s explore some stellar examples of interactivity in data visual storytelling:
The Pudding – Pieces like Changing Body Types Through History use scrollytelling and reactives graphs to guide an interactive data narrative.
New York Times – NYT interactive pieces like Two Decades of Delivering Death From Above mix hovering, filtering and zooming seamlessly.
Nightingale – Visual stories like How Life Has Changed for America’s Children incorporate subtle yet powerful interactivity into charts.
BBC – Interactive explainers like What Happened to House Prices in Your Area enable flexible filtering and comparison.
Gapminder – Hans Rosling’s interactive bubble charts like Health and Wealth of Nations pioneered intuitive hands-on data exploration.
These examples demonstrate how interactivity, when thoughtfully designed, significantly boosts data impact.
Some tips for boosting audience engagement through interactivity:
- Choose interactive behaviors aligned to your data story and audience needs
- Design clear visual triggers and interactions that behave intuitively
- Reveal details gradually across a narrative to avoid overwhelming
- Provide flexible controls for customizing views
- Offer interactive guidance like tooltips and tutorials
- Focus interactivity to highlight insights vs unnecessary spectacle
With the right strategic design, data visualizations can shift from static reporting to engaging exploration.
In summary, interactivity provides a powerful way to put audiences in the driver’s seat of data analysis while revealing insights through an intuitive, guided discovery process. However, beware overusing gratuitous interactions that compromise usability. Thoughtfully selected behaviors that expand flexible data exploration without confusion can elevate engagement and insight. Treat interactivity as a tool that when wisely designed enhances data narratives, but avoid letting it become a distraction. With practice, you can craft living data stories that respond fluidly to audience interests and questions. Make your data visuals intimate experiences instead of passive reports by inviting audiences to engage hands-on.
- 1 Interactive Data Viz: Creating Visuals That Respond to User Input
- 1.1 Introduction
- 1.2 Why Add Interactivity to Data Visualizations?
- 1.3 Types of Interactive Behaviors to Incorporate
- 1.4 Design Principles for Intuitive Interactivity
- 1.5 Interactive Data Viz Tools and Templates
- 1.6 Increase Flexibility of User Control
- 1.7 Pitfalls to Avoid With Interactive Data Viz
- 1.8 Real World Examples of Impactful Interactive Data Viz
- 1.9 Key Takeaways
- 1.10 Conclusion