Activity charts display GitHub-style heatmaps that show daily activity patterns over time. They’re perfect for visualizing daily summaries of events, user activity, or any metric that occurs on a daily basis.

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When to use activity charts

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Perfect for:

  • Daily activity patterns: User signups, logins, or events per day
  • GitHub-style heatmaps: Visual representation of daily activity intensity
  • Habit tracking: Daily occurrences of specific events
  • Activity streaks: Identifying periods of consistent activity
  • Daily summaries: Aggregated daily metrics

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Not ideal for:

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Example queries

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User signups

Show me user signups over time in an activity chart

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Daily activity

Display daily user logins as an activity chart

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Event tracking

Create an activity chart of daily order completions

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Habit tracking

Show me daily exercise sessions in an activity chart

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Best practices

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Data preparation

  • Daily aggregation: Always group by DATE() for daily summaries
  • Handle missing days: Include zero values for days with no activity
  • Consistent time periods: Use consistent date ranges (typically 90 days to 1 year)
  • Single metric focus: Focus on one type of activity per chart

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Visual design

  • Color intensity: Darker colors indicate higher activity levels
  • Clear date labels: Include month/year labels for context
  • Legend: Provide clear legend for activity intensity scale
  • Grid layout: Days of week on x-axis, weeks on y-axis

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Common use cases

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User activity tracking

  • Daily user signups
  • Daily login activity
  • Daily feature usage
  • Daily engagement metrics

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Habit and behavior tracking

  • Daily exercise sessions
  • Daily study sessions
  • Daily task completions
  • Daily goal achievements

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Business metrics

  • Daily order completions
  • Daily customer interactions
  • Daily content creation
  • Daily system events

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Advanced features

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Activity streaks

Identify consistent patterns:
  • Consecutive days of activity
  • Longest activity streaks
  • Break patterns in activity
  • Consistency metrics

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Seasonal patterns

Identify recurring patterns:
  • Weekly patterns (weekend vs weekday)
  • Monthly patterns
  • Seasonal variations
  • Holiday impacts

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Intensity analysis

Understand activity levels:
  • High activity periods
  • Low activity periods
  • Activity distribution
  • Peak activity identification

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Common pitfalls

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Avoid these mistakes:

  1. Sub-daily granularity: Activity charts only work with daily data
  2. Multiple metrics: Focus on one activity type per chart
  3. Missing days: Include zero values for days with no activity
  4. Poor date ranges: Use appropriate time periods (90 days to 1 year)
  5. Complex aggregations: Keep aggregations simple and daily-focused

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Data quality issues:

  • Non-daily data: Ensure data is aggregated to daily level
  • Missing dates: Fill in missing dates with zero values
  • Inconsistent activity definition: Clearly define what counts as activity
  • Too short time periods: Use sufficient history for meaningful patterns

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Example scenarios

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User signups

Show me user signups over time in an activity chart for the past year

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Daily logins

Display daily user login activity as an activity chart

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Order completions

Create an activity chart of daily order completions for the last 6 months

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Feature usage

Show me daily feature usage activity in an activity chart

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Exercise tracking

Display daily exercise sessions as an activity chart

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Interpreting activity charts

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Pattern identification

  • Activity streaks: Identify consecutive days of activity
  • Gaps: Find periods with no activity
  • Weekly patterns: Understand weekend vs weekday patterns
  • Seasonal trends: Spot long-term activity patterns

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Business insights

  • User engagement: Understand daily user activity patterns
  • Growth trends: Identify periods of increased activity
  • Habit formation: Track consistent daily behaviors
  • Seasonal impacts: Understand how seasons affect activity