Area and Line Chart

Use me when you want to watch your data flow like a river over time or across continuous data. Line charts show the path of change (are we going up or down?), while area charts add visual weight to show volume and magnitude. Perfect for stock prices, website traffic, sales trends, or anything that moves through time.

I turn your temporal data into a story with a clear plot. Comparing the growth (or decline) to standard curves like logarithmic, square, or linear growth can be helpful - add a separate function or trendline to see how your data measures up. Some curves benefit from upper and lower boundaries that show confidence intervals for predictions or thresholds indicating when values are in range or exceed limits. You can compare flows across different categories using subplots, or simply change the marker style per category and view everything together.

Overview

Area and line charts display data as continuous lines connecting data points, with area charts filling the space below the line. They excel at showing trends, patterns, and changes over time or across a continuous range. Line charts emphasize the path and direction of change, while area charts add visual weight to emphasize magnitude and cumulative values.

Best used for:

Tracking trends over time (you can also use our dedicated Time Series plot for this)
Comparing multiple data series
Showing continuous data progression
Highlighting changes and patterns
Visualizing cumulative totals (area charts)
Emphasizing magnitude differences (area charts)
Showing confidence intervals for values
Comparing changes to standard growth curves like square, logarithmic, or linear

Common Use Cases

Time Series Analysis

Stock prices and market trends
Website traffic over time
Sales performance tracking
Temperature and weather patterns
Resource usage monitoring
Growth metrics and KPIs

Performance Tracking

Revenue and profit trends
Customer acquisition over time
Project progress and milestones
System performance metrics
Error rates and uptime
User engagement metrics

Comparative Analysis

Multiple product lines
Regional performance comparison
Actual vs. predicted values
Before/after scenarios
Competing metrics side-by-side
Treatment vs. control groups

Line Chart vs. Area Chart

Line Chart

When to use:

Emphasizing trend direction and rate of change
Comparing multiple overlapping series
When exact values matter more than magnitude
Showing volatile or noisy data
5+ series need to be compared

Characteristics:

Clean, minimal visual weight
Easy to distinguish multiple lines
Focus on the path of change
Less cluttered with many series

Area Chart

When to use:

Emphasizing magnitude and volume
Showing cumulative totals
Highlighting the "weight" of data
Single series or stacked comparisons
Part-to-whole relationships (stacked)

Characteristics:

Adds visual weight and emphasis
Better for showing volume
Can be stacked to show totals
More dramatic visual impact

Chart Variations

Single Series

Simple line or area showing one variable over time.

Multiple Series

Comparing 2-5 different variables or groups.

Stacked Area

Areas stacked on top of each other to show cumulative total and individual contributions.

Overlapping Lines

Multiple lines on same axes for direct comparison.

Orientation

Decide if horizontal or vertical.

Design Best Practices

Most of the design best practices are pre sets that you do not have to change.

1. Choose the Right Type

Line chart: For emphasis on trends and multiple comparisons
Area chart: For emphasis on magnitude and volume
Stacked area: For part-to-whole relationships over time

2. Limit Number of Series

1-3 series: Optimal, easy to distinguish
4-5 series: Acceptable with good colors
6+ series: Consider splitting into multiple charts or using small multiples

3. Color Strategy

Use distinct, accessible colors for each series
Ensure sufficient contrast with background
Consider colorblind-friendly palettes that are preconfigured for you
Use lighter fills for area charts to maintain readability
Keep line thickness readable (2-3px typically)

4. Axis Configuration

Start y-axis at zero for area charts (shows true magnitude)
Consider starting above zero for line charts if appropriate (emphasizes changes)
Use consistent intervals
Label axes clearly
Include units in axis titles

5. Legend and Labels

Place legend where it doesn't obscure data
Use direct labeling when possible (label lines directly)
Keep legend order consistent with visual order
Include units and context in labels

Tips for Effective Visualization

Time Series Specific

Choose appropriate time intervals:
- Match interval to data granularity
- Avoid too many or too few data points
- Consider aggregation for long time ranges
Handle gaps and missing data:
- Decide on interpolation strategy
- Make gaps visible if meaningful
- Document data quality issues
Add context markers:
- Annotate significant events
- Mark policy changes or launches
- Highlight anomalies or outliers
- Show seasonal patterns

Comparison Guidelines

Use consistent scales across compared series
Align time periods for fair comparison
Consider normalization (percentages, indices) for different scales
Add reference lines (averages, targets, benchmarks)

Common Patterns and Interpretations

Trends

Upward trend: Increasing values over time
Downward trend: Decreasing values over time
Flat trend: Stable, consistent values
Cyclical pattern: Regular up-and-down cycles
Seasonal pattern: Recurring patterns at specific intervals

Changes

Sharp spike: Sudden increase (event, anomaly)
Sharp drop: Sudden decrease (problem, intervention)
Gradual change: Slow increase or decrease
Step change: Sudden level shift that persists
Volatility: High variation and noise

Relationships

Convergence: Lines moving toward each other
Divergence: Lines moving apart
Correlation: Lines moving together
Leading/lagging: One series predicts another

When to Use Alternative Visualizations

Instead of line/area chart, consider:

Bar chart: For discrete categories or small number of time periods
Scatter plot: When emphasizing individual data points or correlations
Heatmap: For many time series or dense temporal data
Box plot: To show distribution changes over time
Waterfall chart: For cumulative effects and contributions

Complement with:

Summary statistics: Mean, median, trends
Control charts: Statistical process control
Forecast lines: Predicted future values
Confidence intervals: Uncertainty bands

Example Scenarios

Stock Market Performance

Comparing multiple stock prices over time with line chart. Shows relative performance and volatility.

Website Traffic Analysis

Area chart showing visitor volume over months. Stacked areas show traffic sources (organic, paid, referral).

Sales Forecast

Line chart with actual sales (solid line) and predicted sales (dashed line). Shows accuracy of predictions.

Temperature Trends

Line chart showing daily average temperatures across seasons. Includes record high/low reference bands.

Revenue Breakdown

Stacked area chart showing revenue by product category. Total height shows overall revenue, colors show contributions.

Regression

Line chart showing actual data points with regression line and confidence interval bands. The shaded area represents prediction uncertainty, helping visualize model fit quality and identify outliers.

Troubleshooting

Issue: Lines overlap and are hard to distinguish

Solution: Use distinct colors, increase line width (2-3px), reduce number of series, or consider small multiples (separate charts per series).

Issue: Stacked areas show misleading totals

Solution: Verify data is appropriate for stacking (all positive values), check aggregation method, ensure time alignment is correct.

Issue: Area fills obscure data behind them

Solution: Reduce opacity (try 30-50%), use line chart instead, or reorder series (put smallest on top, largest on bottom).

Issue: Too many data points create jagged lines

Solution: Smooth lines using spline interpolation, aggregate data to fewer points (e.g., daily to monthly), or apply moving average.

Issue: Gaps in the line where data is missing

Solution: Enable "Connect Gaps" to bridge missing values, or explicitly show gaps to indicate missing data. Choose based on context.

Issue: Can't see trend due to high variability

Solution: Add trend line or moving average, smooth data before plotting, or use dual axis with raw + smoothed data.

Issue: Seasonal patterns dominate the view

Solution: Detrend or deseasonalize data first, add reference lines for seasonality, or use time series decomposition plots.

Issue: Legend blocks important data

Solution: Reposition legend (outside plot area), use direct line labeling instead, or make legend semi-transparent.

Issue: Need to show confidence intervals

Solution: Use area chart with fill between upper and lower bounds, reduce opacity to show uncertainty range.

After creating a line or area chart, consider:

Bar chart - Compare totals across periods
Scatter plot - Analyze correlations between variables
Box plot - Show distribution statistics over time
Heatmap - Visualize patterns in dense time series
Time Series - Separate trend, seasonality, and noise with dedicated time series tools