# Getting Started with bidux

## Introduction

The `{bidux}` package helps Shiny developers create more effective dashboards using the Behavioral Insight Design (BID) Framework. If you’ve ever wondered why users struggle with your carefully crafted dashboards, or why your beautifully visualized data doesn’t drive the decisions you expected, this package is for you.

The core insight: Technical excellence ≠ User success. Even the most sophisticated analysis can fail if users can’t quickly understand and act on it.

The BID framework bridges this gap by integrating behavioral science, UX best practices, and data storytelling techniques into a systematic 5-stage process. Think of it as applying the same rigor you use for data validation to user experience design.

    library(bidux)
    library(dplyr)

## The BID Framework: A Data-Driven Approach to UX

The BID framework consists of 5 sequential stages that mirror how you might approach a data analysis project:

1. Interpret the User’s Need - Like defining your research question and understanding your data
2. Notice the Problem - Like identifying data quality issues or analytical bottlenecks
3. Anticipate User Behavior - Like checking for statistical biases that could skew results
4. Structure the Dashboard - Like choosing the right visualization or model architecture
5. Validate & Empower the User - Like testing your model and ensuring stakeholders can act on results

Key insight for data professionals: Just as you wouldn’t skip exploratory data analysis before modeling, don’t skip understanding user cognition before building interfaces.

Each stage builds on insights from previous stages, creating a systematic approach to dashboard design that’s both evidence-based and user-centered.

## Exploring the Concept Dictionary

The BID framework is built on established science and design principles. To explore these concepts, use `bid_concepts()` to list all available concepts, or search for specific terms:

    # List all concepts
    all_concepts <- bid_concepts()
    head(select(all_concepts, concept, category, description), 3)

    # Search for specific concepts
    bid_concepts("cognitive") |>
      select(concept, description, implementation_tips)

For detailed information about a specific concept, use `bid_concept()`:

    # Get information about a specific concept
    bid_concept("Processing Fluency") |>
      select(concept, description, implementation_tips)

The `bid_concept()` function supports case-insensitive and partial matching:

    # Case-insensitive matching
    bid_concept("hick's law") |>
      select(concept, description)

    # Partial matching
    bid_concept("proximity") |>
      select(concept, description)

You can also explore concepts that are new to the BID framework:

    # Explore new concepts from user-centric design
    bid_concepts("visual hierarchy|breathable|gherkin") |>
      select(concept, description)

## Documenting a Dashboard Project with BID

Let’s walk through a complete example of using the BID framework to document and improve a dashboard project.

### Stage 1: Interpret the User’s Need

Start by clarifying the central question your dashboard needs to answer and structure the data story:

    # Document the user's need using new_data_story() with flat API (recommended)
    interpret_result <- bid_interpret(
      central_question = "How are our marketing campaigns performing across different channels?",
      data_story = new_data_story(
        hook = "Recent campaign performance varies significantly across channels",
        context = "We've invested in 6 different marketing channels over the past quarter",
        tension = "ROI metrics show inconsistent results, with some channels underperforming",
        resolution = "Identify top-performing channels and key performance drivers",
        audience = "Marketing team and executives",
        metrics = "Channel ROI, Conversion Rate, Cost per Acquisition",
        visual_approach = "Comparative analysis with historical benchmarks"
      ),
      # Recommended: use data.frame for personas (cleaner, more explicit)
      user_personas = data.frame(
        name = c("Marketing Manager", "CMO"),
        goals = c(
          "Optimize marketing spend across channels",
          "Strategic oversight of marketing effectiveness"
        ),
        pain_points = c(
          "Difficulty comparing performance across different metrics",
          "Needs high-level insights without technical details"
        ),
        technical_level = c("intermediate", "basic"),
        stringsAsFactors = FALSE
      )
    )

    interpret_result |>
      select(central_question, hook, tension, resolution)

The function evaluates our data story elements and provides suggestions for improvement (in the `suggestions` column). We’ve also added user personas to better target our design.

### Stage 2: Notice the Problem

Now identify the specific problems users are encountering with your dashboard or interface:

    # Document the problem
    notice_result <- bid_notice(
      previous_stage = interpret_result,
      problem = "Users are overwhelmed by too many filter options and struggle to find relevant insights",
      evidence = "User testing shows 65% of first-time users fail to complete their intended task within 2 minutes"
    )

    notice_result |>
      select(problem, theory, evidence)

Notice that the function automatically selected an appropriate theory based on our problem description. It also provides suggestions for addressing cognitive load which you can access from the `suggestions` column.

### Stage 3: Anticipate User Behavior

Next, identify potential cognitive biases that might affect how users interpret your dashboard:

    # Document bias mitigation strategies
    anticipate_result <- bid_anticipate(
      previous_stage = notice_result,
      bias_mitigations = list(
        anchoring = "Include previous period performance as reference points",
        framing = "Provide toggle between ROI improvement vs. ROI gap views",
        confirmation_bias = "Highlight unexpected patterns that contradict common assumptions"
      )
    )

    anticipate_result |>
      select(bias_mitigations)

The function evaluates our bias mitigation strategies, providing implementation suggestions.

### Stage 4: Structure the Dashboard

Now determine the layout and key design principles to implement:

    # Document the dashboard structure
    structure_result <- bid_structure(previous_stage = anticipate_result)

    structure_result |>
      select(layout, concepts, suggestions)

The function automatically selects an appropriate layout based on the content from previous stages and provides ranked, actionable suggestions organized by UX concepts. The layout selection is transparent with clear rationale for why a particular layout was chosen.

#### Working with Suggestions: Nested vs Flattened Format

The `bid_structure()` function returns suggestions in two formats for maximum flexibility:

Nested Format (`$suggestions`): Organized by behavioral science concepts, ideal for understanding the theoretical foundation:

    # Access nested suggestions (grouped by concept)
    structure_result$suggestions[[1]]$concept
    structure_result$suggestions[[1]]$suggestions[[1]]$title

Flattened Tibble Format (`$suggestions_tbl`): A tidy tibble with one row per suggestion, ideal for filtering and analysis:

    # Access flattened tibble format
    suggestions_flat <- structure_result$suggestions_tbl[[1]]

    # View all suggestions sorted by score
    suggestions_flat |>
      select(concept, title, score, difficulty, category) |>
      head(5)

    # Filter by implementation difficulty
    easy_wins <- suggestions_flat |>
      filter(difficulty == "Easy") |>
      select(title, details, components)

    # Filter by category
    layout_suggestions <- suggestions_flat |>
      filter(category == "Layout") |>
      select(title, details, rationale)

    # Find high-impact suggestions
    high_impact <- suggestions_flat |>
      filter(score >= 0.85, difficulty %in% c("Easy", "Medium")) |>
      arrange(desc(score)) |>
      select(title, score, difficulty, components)

The tibble format includes these columns:

- `concept`: Behavioral science concept (e.g., “Cognitive Load Theory”)
- `title`: Brief actionable description
- `details`: Specific implementation guidance
- `components`: Comma-separated Shiny/bslib component names
- `rationale`: Why this suggestion is relevant
- `score`: Relevance score (0-1, higher is more relevant)
- `difficulty`: Implementation difficulty (“Easy”, “Medium”, “Hard”)
- `category`: Category grouping (“Layout”, “Navigation”, “Content”, etc.)

### Stage 5: Validate & Empower the User

Finally, document how you’ll ensure users leave with clear insights and the ability to collaborate:

    # Document validation approach
    validate_result <- bid_validate(
      previous_stage = structure_result,
      summary_panel = "Executive summary highlighting top and bottom performers, key trends, and recommended actions for the next marketing cycle",
      collaboration = "Team annotation capability allowing marketing team members to add context and insights to specific data points",
      next_steps = c(
        "Review performance of bottom 2 channels",
        "Increase budget for top-performing channel",
        "Schedule team meeting to discuss optimization strategy",
        "Export findings for quarterly marketing review"
      )
    )

    validate_result |>
      select(summary_panel, collaboration, next_steps)

The validate function acknowledges our implementation of the Peak-End Rule through next steps and provides suggestions for refining our approach.

## Generating Implementation Suggestions

Once you’ve documented your dashboard with the BID framework, you can generate concrete suggestions for implementing the principles using common R packages:

    # Get {bslib} component suggestions
    bid_suggest_components(structure_result, package = "bslib") |>
      select(component, description) |>
      head(2)

    # Get {reactable} suggestions for showing data
    bid_suggest_components(structure_result, package = "reactable") |>
      select(component, description) |>
      head(2)

    # Get suggestions from all supported packages
    all_suggestions <- bid_suggest_components(validate_result, package = "all")
    table(all_suggestions$package)

## Creating a Complete BID Report

You can generate a complete report summarizing all stages of your BID process:

    # Generate a text report (default)
    report <- bid_report(validate_result)
    cat(report)

    # Generate an HTML report
    html_report <- bid_report(validate_result, format = "html")

    # Generate a markdown report
    md_report <- bid_report(validate_result, format = "markdown")

## Using BID in Your Shiny Development Workflow

Here’s how to integrate the BID framework into your development process:

1. Planning Phase

- Use the BID framework to document your design decisions before writing code
- Identify key user needs and potential friction points
- Define user personas to guide your design choices
- Consider accessibility requirements early

2. Development Phase

- Reference your BID documentation to implement appropriate UI patterns
- Use `bid_suggest_components()` to get package-specific implementation ideas
- Implement bias mitigation strategies in your interface
- Build in progressive disclosure for complex interfaces

3. Testing Phase

- Validate that your implementation addresses the issues identified in Stage 1
- Test with actual users representing your defined personas
- Specifically test bias mitigation strategies and accessibility features
- Gather feedback on the effectiveness of your validation approach

4. Iteration Phase

- Update your BID documentation as you refine the dashboard
- Use `bid_report()` to maintain comprehensive documentation
- Focus improvements on areas with the greatest impact on user experience
- Continue to apply BID principles as you add new features

## Conclusion

The `{bidux}` package makes it easier to apply behavioral science and UX best practices to your Shiny dashboards. By following the 5-stage BID framework, you can create applications that are more intuitive, engaging, and effective for your users.

Future versions of `{bidux}` will include:

- User stories workflow integration following the Gherkin method
- Enhanced design patterns library
- Accessibility framework integration with WCAG guidelines
- A UI component library implementing BID principles
- Testing and validation tools for dashboard evaluation

## Integrating Telemetry Data (New in 0.3.1)

If you have telemetry data from user interactions (e.g., from the `{shiny.telemetry}` package), `{bidux}` can help transform it into actionable BID insights by automatically detecting UX friction patterns.

### Understanding the Two Telemetry Functions

`{bidux}` provides two complementary approaches to telemetry analysis:

`bid_telemetry()` - Modern Tidy API (Recommended) - Returns a clean tibble of issues for analysis - Best for new workflows and data exploration - Integrates seamlessly with `dplyr` pipelines - Introduced in version 0.3.1

`bid_ingest_telemetry()` - Legacy Compatible API - Returns a hybrid object that works as both a list and enhanced object - Maintains backward compatibility with pre-0.3.1 code - Provides same analysis as `bid_telemetry()` with additional list interface - Will be soft-deprecated in 0.4.0

Both functions analyze the same telemetry patterns: - Unused inputs - UI controls rarely or never used - Delayed interactions - Users taking too long to engage - Error patterns - Recurring errors affecting users - Navigation drop-offs - Pages/tabs with low visit rates - Confusion patterns - Rapid repeated changes suggesting uncertainty

### Using Sensitivity Presets (New in 0.3.2)

The `bid_telemetry_presets()` function provides three pre-configured sensitivity levels, making it easy to adjust how aggressively issues are detected without manually tuning thresholds:

    # STRICT: Detects even minor issues - use for critical applications
    strict_issues <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("strict")
    )
    # - Flags inputs used by < 2% of sessions
    # - Flags delays > 20 seconds to first action
    # - Flags errors in > 5% of sessions
    # - Flags pages visited by < 10% of users

    # MODERATE: Balanced default - appropriate for most applications
    moderate_issues <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("moderate")
    )
    # - Flags inputs used by < 5% of sessions (default)
    # - Flags delays > 30 seconds to first action (default)
    # - Flags errors in > 10% of sessions (default)
    # - Flags pages visited by < 20% of users (default)

    # RELAXED: Only detects major issues - use for mature, stable dashboards
    relaxed_issues <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("relaxed")
    )
    # - Flags inputs used by < 10% of sessions
    # - Flags delays > 60 seconds to first action
    # - Flags errors in > 20% of sessions
    # - Flags pages visited by < 30% of users

### Comparing Preset Sensitivities

Different presets can identify different numbers of issues from the same data:

    # Analyze with all three presets
    strict <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("strict")
    )

    moderate <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("moderate")
    )

    relaxed <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("relaxed")
    )

    # Compare issue counts
    data.frame(
      preset = c("strict", "moderate", "relaxed"),
      total_issues = c(nrow(strict), nrow(moderate), nrow(relaxed)),
      critical_issues = c(
        sum(strict$severity == "critical"),
        sum(moderate$severity == "critical"),
        sum(relaxed$severity == "critical")
      )
    )

    # Strict preset typically finds 2-3x more issues than relaxed
    # Use strict during initial development, relaxed for stable dashboards

### Modern Telemetry Workflow with `bid_telemetry()`

The recommended approach for new projects:

    # 1. Analyze telemetry with appropriate sensitivity
    issues <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("moderate")
    )

    # 2. Triage and review issues (returns organized summary)
    print(issues)

    # 3. Filter to high-priority issues using dplyr
    library(dplyr)
    critical_issues <- issues |>
      filter(severity %in% c("critical", "high")) |>
      arrange(desc(impact_rate))

    # 4. Convert top issues to Notice stages for BID workflow
    notices <- bid_notices(
      issues = critical_issues,
      previous_stage = interpret_result,
      max_issues = 3
    )

    # 5. Extract telemetry flags for informed decisions
    flags <- bid_flags(issues)
    flags$has_critical_issues # TRUE/FALSE
    flags$has_navigation_issues # TRUE/FALSE
    flags$session_count # Number of sessions analyzed

    # 6. Use flags to inform Structure stage
    structure_result <- bid_structure(
      previous_stage = anticipate_result,
      telemetry_flags = flags
    )

### Legacy Workflow with `bid_ingest_telemetry()`

For backward compatibility with existing code:

    # Returns hybrid object that works as both list and enhanced object
    legacy_issues <- bid_ingest_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("moderate")
    )

    # Legacy list interface (backward compatible)
    length(legacy_issues) # Number of issues as list length
    legacy_issues[[1]] # First issue as bid_stage object
    names(legacy_issues) # Issue identifiers

    # Enhanced features (new in 0.3.1)
    as_tibble(legacy_issues) # Get tidy issues view
    bid_flags(legacy_issues) # Extract global flags
    print(legacy_issues) # Shows organized triage summary

    # Both interfaces work on same object

### Complete Telemetry-Informed BID Example

Here’s a full example showing how telemetry analysis integrates with the BID framework:

    # Step 1: Analyze telemetry to identify friction points
    issues <- bid_telemetry(
      "path/to/telemetry.sqlite",
      thresholds = bid_telemetry_presets("strict") # Catch everything during development
    )

    # Step 2: Start BID workflow with central question
    interpret_result <- bid_interpret(
      central_question = "How can we reduce user friction identified in telemetry?",
      data_story = new_data_story(
        hook = "Telemetry shows multiple UX friction points",
        context = glue::glue("Analysis of {bid_flags(issues)$session_count} user sessions"),
        tension = "Users struggling with specific UI elements and workflows",
        resolution = "Systematically address high-impact issues using BID framework"
      )
    )

    # Step 3: Address highest-impact issue first
    top_issue <- issues |>
      arrange(desc(impact_rate)) |>
      slice(1)

    notice_result <- bid_notices(
      issues = top_issue,
      previous_stage = interpret_result
    )[[1]]

    # Step 4: Anticipate biases related to the issue
    anticipate_result <- bid_anticipate(
      previous_stage = notice_result,
      bias_mitigations = list(
        anchoring = "Provide clear default values based on common use cases",
        confirmation_bias = "Show data that challenges user assumptions"
      )
    )

    # Step 5: Structure with telemetry-informed decisions
    structure_result <- bid_structure(
      previous_stage = anticipate_result,
      telemetry_flags = bid_flags(issues) # Informs layout selection
    )

    # Step 6: Validate with telemetry references
    validate_result <- bid_validate(
      previous_stage = structure_result,
      summary_panel = "Dashboard improvements based on analysis of real user behavior patterns",
      next_steps = c(
        "Address remaining high-severity telemetry issues",
        "Re-run telemetry analysis after changes to measure improvement",
        "Monitor key metrics: time-to-first-action, error rates, navigation patterns"
      )
    )

### When to Use Which Function

Use `bid_telemetry()` when you: - Are starting a new project or workflow - Want clean, tidy data for analysis and visualization - Prefer working with tibbles and `dplyr` - Don’t need backward compatibility

Use `bid_ingest_telemetry()` when you: - Have existing code from bidux < 0.3.1 - Need the legacy list interface for compatibility - Want both list and tibble access in the same object

Note: Both functions perform identical telemetry analysis and support the same presets and thresholds. The only difference is the return format.

Visit github.com/jrwinget/bidux for updates and to contribute to the package development. We welcome feedback and suggestions to help make the BID framework even more effective for Shiny developers.

Remember that good dashboard design is an iterative process that benefits from continuous user feedback. The BID framework provides structure to this process while ensuring common principles are incorporated throughout your development workflow.