A 100% stacked area chart is a powerful visualization tool that allows you to display the relative contribution of multiple data series over a given time period, showing how each series contributes to the total. This type of chart is particularly useful when you want to emphasize the percentage share of each category while tracking changes over time.
Key Concepts
- Normalization: In a 100% stacked area chart, the total of all series for a given category (e.g., time period) is normalized to 100%. This means that the height of the stacked areas represents the proportion of each category relative to the total.
- Time Series Data: This chart is often used with time series data, where the x-axis represents time intervals (days, months, years) and the y-axis represents the percentage of total values.
- Multiple Series Representation: Each area in the chart corresponds to a different category or data series. The areas are stacked on top of each other, making it easy to compare the contributions of each series to the total.
- Visual Impact: The use of colors and areas can help viewers quickly grasp the trends and shifts in data composition over time.
Example Scenario
Consider a company that sells three different products: A, B, and C. Over the past year, the company wants to visualize how the sales percentages of these products have changed monthly.
Data
Here’s a simplified dataset:
| Month | Product A | Product B | Product C |
|---|---|---|---|
| January | 30 | 50 | 20 |
| February | 40 | 30 | 30 |
| March | 50 | 20 | 30 |
| April | 20 | 60 | 20 |
| May | 10 | 70 | 20 |
| June | 30 | 50 | 20 |
Visualization with Visual Paradigm Online Chart
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Conclusion
A 100% stacked area chart provides a clear and effective way to visualize how different data series contribute to a total over time. By focusing on percentage contributions rather than absolute values, it allows for easy comparison and analysis of trends. Using tools like Visual Paradigm online Chart makes it straightforward to create interactive and responsive visualizations that enhance data understanding.
