Understanding Line Plots

In this module, we’ll explore line plots - an essential visualization tool for showing how values change over time or sequence. Line plots help us see trends, patterns, and relationships in data that unfolds over a sequence.

A line plot connects data points with lines where:

  • Each point represents a value at a specific time or sequence position
  • Points are connected by lines to show continuity
  • The horizontal axis typically shows time or sequence
  • The vertical axis shows the measured values

Think of it like tracing your finger along a path that goes up and down, where each position tells you a value at that moment.

Best suited for:

  1. Time series data (values over time)
  2. Continuous sequences
  3. Showing trends and patterns
  4. Comparing multiple related series

Not suitable for:

  1. Categorical comparisons (use bar plots instead)
  2. Unordered data
  3. Discontinuous data

Components of a Line Plot

  1. Points:
    • Mark actual data measurements
    • Show exact values at specific times
  2. Lines:
    • Connect points to show continuity
    • Indicate trend between measurements
  3. Axes:
    • X-axis: Time or sequence (horizontal)
    • Y-axis: Values being measured (vertical)
  4. Legend (for multiple lines):
    • Identifies different data series
    • Explains line colors or patterns

5. Example: Temperature Over Time

Let’s explore daily temperatures for a week:

  • Monday: 20°C
  • Tuesday: 22°C
  • Wednesday: 19°C
  • Thursday: 23°C
  • Friday: 25°C
  • Saturday: 24°C
  • Sunday: 21°C

What the Line Plot Shows:

  1. Temperature fluctuations day to day
  2. Highest point (peak) on Friday at 25°C
  3. Lowest point (valley) on Wednesday at 19°C
  4. Overall pattern: slightly increasing then decreasing

Common Patterns to Listen For

  1. Trends:
    • Upward: Values generally increasing
    • Downward: Values generally decreasing
    • Flat: Values staying stable
  2. Cycles:
    • Regular patterns that repeat
    • Example: Seasonal temperature changes
  3. Fluctuations:
    • Short-term ups and downs
    • Variation around the trend

Accessibility Considerations

  1. Line Differentiation:
    • Use different patterns (solid, dashed, dotted)
    • Varying thickness
    • Clear color contrast
  2. Data Points:
    • Distinct markers for each series
    • Clear labels where needed
  3. Clear Labels:
    • Axes titles
    • Units of measurement
    • Legend for multiple lines

Reflection and Exploration

Think about things you measure over time:

  • Daily steps
  • Weekly spending
  • Monthly rainfall

Try describing the pattern:

  • “My steps increase during weekdays and drop on weekends”
  • “Spending peaks at the start of each month”
  • “Rainfall shows a seasonal pattern”