## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = FALSE ) ## ----setup-------------------------------------------------------------------- # library(bidux) # library(dplyr) ## ----concepts----------------------------------------------------------------- # # 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) ## ----concept_detail----------------------------------------------------------- # # Get information about a specific concept # bid_concept("Processing Fluency") |> # select(concept, description, implementation_tips) ## ----concept_matching--------------------------------------------------------- # # Case-insensitive matching # bid_concept("hick's law") |> # select(concept, description) # # # Partial matching # bid_concept("proximity") |> # select(concept, description) ## ----new_concepts------------------------------------------------------------- # # Explore new concepts from user-centric design # bid_concepts("visual hierarchy|breathable|gherkin") |> # select(concept, description) ## ----interpret---------------------------------------------------------------- # # 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) ## ----notice------------------------------------------------------------------- # # 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) ## ----anticipate--------------------------------------------------------------- # # 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) ## ----structure---------------------------------------------------------------- # # Document the dashboard structure # structure_result <- bid_structure(previous_stage = anticipate_result) # # structure_result |> # select(concepts, suggestions) ## ----structure-nested--------------------------------------------------------- # # Access nested suggestions (grouped by concept) # structure_result$suggestions[[1]]$concept # structure_result$suggestions[[1]]$suggestions[[1]]$title ## ----structure-tibble--------------------------------------------------------- # # Access flattened tibble format # suggestions_flat <- structure_result$suggestions_tbl[[1]] # # # View all suggestions sorted by score # suggestions_flat |> # select(concept, title, score, difficulty, category) |> # head(5) # # # Filter by implementation difficulty # easy_wins <- suggestions_flat |> # filter(difficulty == "Easy") |> # select(title, details, components) # # # Filter by category # layout_suggestions <- suggestions_flat |> # filter(category == "Layout") |> # select(title, details, rationale) # # # Find high-impact suggestions # high_impact <- suggestions_flat |> # filter(score >= 0.85, difficulty %in% c("Easy", "Medium")) |> # arrange(desc(score)) |> # select(title, score, difficulty, components) ## ----validate----------------------------------------------------------------- # # Document validation approach # validate_result <- bid_validate( # previous_stage = structure_result, # summary_panel = "Executive summary highlighting top and bottom performers, key trends, and recommended actions for the next marketing cycle", # collaboration = "Team annotation capability allowing marketing team members to add context and insights to specific data points", # next_steps = c( # "Review performance of bottom 2 channels", # "Increase budget for top-performing channel", # "Schedule team meeting to discuss optimization strategy", # "Export findings for quarterly marketing review" # ) # ) # # validate_result |> # select(summary_panel, collaboration, next_steps) ## ----suggestions-------------------------------------------------------------- # # Get {bslib} component suggestions # bid_suggest_components(structure_result, package = "bslib") |> # select(component, description) |> # head(2) # # # Get {reactable} suggestions for showing data # bid_suggest_components(structure_result, package = "reactable") |> # select(component, description) |> # head(2) # # # Get suggestions from all supported packages # all_suggestions <- bid_suggest_components(validate_result, package = "all") # table(all_suggestions$package) ## ----report, eval=FALSE------------------------------------------------------- # # 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") ## ----telemetry_presets-------------------------------------------------------- # # 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 ## ----preset_comparison-------------------------------------------------------- # # 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$severity == "critical") # ) # ) # # # Strict preset typically finds 2-3x more issues than relaxed # # Use strict during initial development, relaxed for stable dashboards ## ----telemetry_workflow_modern------------------------------------------------ # # 1. Analyze telemetry with appropriate sensitivity # issues <- bid_telemetry( # "path/to/telemetry.sqlite", # thresholds = bid_telemetry_presets("moderate") # ) # # # 2. Triage and review issues (returns organized summary) # print(issues) # # # 3. Filter to high-priority issues using dplyr # library(dplyr) # critical_issues <- issues |> # filter(severity %in% c("critical", "high")) |> # arrange(desc(impact_rate)) # # # 4. Convert top issues to Notice stages for BID workflow # notices <- bid_notices( # issues = critical_issues, # previous_stage = interpret_result, # max_issues = 3 # ) # # # 5. Extract telemetry flags for informed decisions # flags <- bid_flags(issues) # flags$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 # ) ## ----telemetry_workflow_legacy------------------------------------------------ # # 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_example----------------------------------------------- # # 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" # ) # )