How to Choose the Right Chart Type

In data visualization, choosing the right chart type is the most critical first step. The right chart can make your data story clear and compelling, while the wrong choice can leave readers confused or even misled.

This article will systematically introduce various common chart types and their best use cases, helping you make the right choice in any situation.

Core Principles for Chart Selection

Before diving into each chart type, let's establish a few core principles:

1. Define your purpose: Do you want to show trends, compare data, or reveal relationships?
2. Understand your data: Is it time series, categorical, or numerical data?
3. Consider your audience: What's their data literacy level? How detailed does the information need to be?

Common Chart Types and Use Cases

1. Bar Chart

Best for: Comparing values across different categories

The bar chart is one of the most commonly used chart types. It's intuitive and almost everyone can understand it easily.

When to use a bar chart:

• Compare sales across different products
• Show budget allocation across departments
• Compare user counts across different regions

Best practices:

• Keep categories between 5-12
• Start Y-axis from zero to avoid exaggerating differences
• Arrange by value or logical order

// Example data structure for creating a bar chart with uchart
const barChartData = {
  categories: ['Product A', 'Product B', 'Product C', 'Product D'],
  series: [
    { name: '2024', data: [120, 200, 150, 80] },
    { name: '2025', data: [150, 230, 180, 95] },
  ],
};

2. Line Chart

Best for: Showing data trends over time

The line chart is the gold standard for displaying time series data. It clearly presents rising, falling, and fluctuating trends.

When to use a line chart:

• Track website traffic changes over time
• Display stock price movements
• Analyze seasonal sales trends

Best practices:

• Place time dimension on the X-axis
• Avoid placing more than 4-5 lines in the same chart
• Use different colors or line styles to distinguish data series

3. Pie Chart

Best for: Showing part-to-whole relationships

Pie charts are very effective for displaying simple proportional relationships, but they're also the most commonly misused chart type.

When to use a pie chart:

• Show market share distribution
• Present budget composition
• Display user demographic breakdown

When to avoid pie charts:

• More than 5-6 categories
• Need for precise value comparisons
• Values are too similar

Best practices:

• Limit to fewer than 5 categories
• Start from 12 o'clock, arrange by size
• Avoid 3D effects (they distort perception)

4. Scatter Plot

Best for: Exploring relationships between two variables

Scatter plots are excellent tools for analyzing correlations, revealing patterns and outliers in your data.

When to use a scatter plot:

• Analyze the relationship between ad spend and sales
• Study the correlation between employee tenure and salary
• Explore the relationship between user activity and retention

Best practices:

• Ensure sufficient data points (at least 30+ works better)
• Add trend lines to reveal overall trends
• Use color or size to encode a third dimension

5. Area Chart

Best for: Showing cumulative changes or emphasizing volume

Area charts are similar to line charts, but they fill the area below the line with color, emphasizing quantity.

When to use an area chart:

• Show cumulative sales
• Present resource usage changes
• Emphasize growth magnitude and trends

When to avoid:

• When multiple data series overlap significantly (consider stacked area charts)

Chart Selection Decision Tree

To help you make quick decisions, here's a simple decision flow:

Common Mistakes and How to Avoid Them

Mistake 1: Using 3D Effects

3D charts look cool but severely distort visual perception of data. Always use 2D charts.

Mistake 2: Truncating the Y-Axis

Not starting the Y-axis from zero to make differences look larger misleads readers. If you must truncate, clearly label it.

Mistake 3: Using Too Many Categories in Pie Charts

Pie charts become difficult to read with more than 5-6 slices. Consider using a bar chart or combining small categories into "Other".

Mistake 4: Poor Color Usage

Using too many colors or similar, hard-to-distinguish colors makes charts confusing. Limit color count, ensure sufficient contrast, and consider colorblind users.

Creating Charts Quickly with uchart

After choosing your chart type, you can create professional charts in minutes with uchart:

1. Import data: Supports manual input, pasting Excel data, or uploading CSV files
2. Select chart type: Choose the most suitable type based on this guide
3. Customize style: Adjust colors, fonts, labels, etc., to match your brand
4. Export and share: Supports multiple formats including PNG and SVG

Conclusion

There's no one-size-fits-all rule for choosing the right chart type, but with the principles and best practices introduced in this article, you'll be able to make smart choices in most situations.

Remember: The best chart is one that makes your data story immediately clear. If readers need to spend a long time understanding your chart, it might be time to try a different type.

Ready to start creating your charts? Try uchart now and make data visualization simple and professional!