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Introduction to Statistical Charts

Overview​

Statistical charts are used to visualize data from a table view, helping users better understand information, identify trends, and perform analysis through graphical representation.

In data analysis, statistical charts are commonly used in the following scenarios:

  • Trend analysis: For example, monthly sales growth trends or fluctuations in user activity.
  • Structural analysis: For example, the sales proportion of different product lines or the composition of customer sources.
  • Comparative analysis: For example, performance rankings across regions or employee performance comparisons.
  • Conversion analysis: For example, recruitment funnels or sales opportunity conversion rates.
  • Target monitoring: For example, tracking the progress of annual sales targets.

Supported Chart Types and Use Cases​

HAP supports a wide range of statistical charts. Different chart types are suitable for different data analysis scenarios. You can choose an appropriate chart type based on your analysis objective.

Chart TypeApplicable ScenarioExample Use Case
Bar Chart / Horizontal Bar ChartCompare values across different categoriesAnalyze monthly sales performance of each sales team in the current year
Symmetric Bar ChartCompare two opposing metricsAnalyze annual store revenue versus refunds
Line ChartView trends in data over timeDisplay monthly sales changes throughout the year
Combination ChartDisplay two metrics with different scales simultaneouslyAnalyze daily new purchase order count and order amount
Scatter ChartObserve relationships or correlations between two variablesVisualize population migration distribution across cities
Radar ChartPerform multi-dimensional comparison of metricsAnalyze purchase order sources and order amounts
Pie ChartView the proportional structure of overall dataAnalyze salary cost distribution by department
Funnel ChartAnalyze conversion performance in business processesDisplay the conversion process from sales leads to closed deals
Word CloudDisplay the frequency of text keywordsAnalyze the most popular professional keywords
Number ChartDisplay key metric valuesAnalyze the number of new user feedback entries this week
DashboardMonitor metric status or completion levelMonitor monthly product sales refund amounts
Progress BarDisplay target completion progressDisplay annual sales target completion rate
Ranking ListDisplay ranked data distributionIndividual sales performance ranking
World Map / Administrative DivisionsDisplay data distribution across geographic regionsDisplay product sales distribution by region
Pivot TablePerform multi-dimensional data aggregation analysisSummarize sales data by month, quarter, or year

How to Choose the Right Statistical Chart?​

Before selecting a statistical chart, first clarify your data analysis objective. Different analysis goals require different chart types.

Before configuring a chart, ask yourself:

“What insight do I want readers to gain from this chart?”

Based on analysis intent, common data analysis needs can generally be categorized into the following five types.

1. Comparison: Highlight Differences Across Categories​

If you need to compare values across different items (such as products, departments, or employees), consider the following charts:

  • Short category names with a small number of categories (<10)
    → Bar Chart: Provides the most intuitive visual comparison through height differences.

  • Long category names or a large number of categories
    → Horizontal Bar Chart: Prevents label overlap and is more suitable for viewing rankings.

  • Only need to highlight top performers (Top N)
    → Ranking List: Automatically focuses on leading data points.

  • Need to compare two opposing metrics (such as revenue vs. expenses)
    → Symmetric Bar Chart: Displays metrics back-to-back for clearer comparison.

2. Trend Analysis: Understand Changes Over Time​

If you need to observe how data changes over continuous time periods (year, month, week, or day), use:

  • Track growth or decline trends
    → Line Chart: Clearly reflects trends through changes in the line.

  • Compare two metrics simultaneously (such as order volume vs. order amount)
    → Combination Chart: Uses two axes to address differences in scale.

3. Proportion Analysis: Understand Overall Composition​

If you need to show how each part contributes to the whole (usually totaling 100%), use:

  • Small number of categories (typically <6)
    → Pie Chart: The most intuitive way to present proportions.

  • Evaluate overall performance across multiple dimensions
    → Radar Chart: Suitable for capability assessment or multi-metric comparison.

  • Analyze the popularity or weight of text data
    → Word Cloud: Automatically adjusts font size based on keyword frequency.

Note: When there are many categories, it is not recommended to use a pie chart. Consider using a Bar Chart combined with percentage labels instead.

4. Business-Specific Analysis: Gain Deeper Insights​

If the analysis involves clear business logic or spatial dimensions, consider the following charts:

  • Analyze conversion rates within business processes
    → Funnel Chart: Commonly used in sales funnels or recruitment process analysis.

  • View business distribution across geographic regions
    → World Map / Administrative Divisions: Highlights regional differences using bubbles or color intensity.

  • Analyze the correlation between two metrics
    → Scatter Chart: Helps determine whether a relationship exists between variables.

5. Key Metrics: Quickly View Critical Results​

If complex analysis is not required and you only need a quick view of key indicators, use:

  • View current key values (such as today’s sales)
    → Number Chart: Ideal for displaying critical metrics on dashboards.

  • Track target completion or current progress
    → Dashboard / Progress Bar: Visual scales clearly indicate completion status.

  • Perform multi-dimensional data aggregation analysis
    → Pivot Table: Supports cross-tabulation analysis similar to Excel.