AgentSkillsCN

data-visualization

运用清晰明了、精准无误且富有视觉冲击力的最佳实践，打造高效的数据可视化方案，精准传达洞察信息。

SKILL.md
--- frontmatter
name: data-visualization
description: Create effective data visualizations using best practices for clarity, accuracy, and visual communication of insights

Data Visualization Skill

When creating data visualizations, follow these principles to ensure clear and effective communication:

Core Principles

1. Choose the Right Chart Type

  • •Line Charts: Trends over time, continuous data
  • •Bar Charts: Comparing categories, discrete data
  • •Scatter Plots: Relationships between variables, correlations
  • •Pie Charts: Parts of a whole (use sparingly, max 5-6 segments)
  • •Heatmaps: Patterns in large datasets, correlations
  • •Box Plots: Distribution statistics, outlier detection

2. Design Guidelines

Clarity

  • •Use clear, descriptive titles and labels
  • •Include units of measurement
  • •Add a legend when multiple series are present
  • •Ensure adequate contrast and readability

Accuracy

  • •Start y-axis at zero for bar charts (unless good reason)
  • •Use consistent scales across related charts
  • •Avoid distorting data through inappropriate scaling
  • •Label data points when precision matters

Simplicity

  • •Remove chart junk and unnecessary decorations
  • •Use color purposefully, not decoratively
  • •Limit the number of colors (5-7 max)
  • •Ensure accessibility (colorblind-friendly palettes)

3. Color Best Practices

  • •Sequential: Use for ordered data (light to dark)
  • •Diverging: Use for data with a meaningful midpoint
  • •Categorical: Use for unordered categories
  • •Highlight: Use accent colors to draw attention
  • •Test accessibility with colorblind simulators

4. Storytelling with Data

  • •Lead with the insight, not the data
  • •Use annotations to highlight key findings
  • •Arrange charts in logical flow
  • •Provide context and comparisons
  • •Include data sources and timestamp

Visualization Workflow

  1. •

    Understand the Data

    • •Explore data structure and distributions
    • •Identify key variables and relationships
    • •Determine the message to communicate
  2. •

    Select Visualization Type

    • •Match chart type to data characteristics
    • •Consider audience and use case
    • •Plan for interactivity if needed
  3. •

    Design the Visualization

    • •Create initial draft
    • •Apply design principles
    • •Optimize for clarity and impact
  4. •

    Refine and Validate

    • •Get feedback from stakeholders
    • •Test on target audience
    • •Iterate based on feedback
    • •Verify accuracy

Common Mistakes to Avoid

  • •Using 3D charts unnecessarily (adds confusion)
  • •Too many colors or visual elements
  • •Missing or unclear axis labels
  • •Truncated y-axis to exaggerate differences
  • •Using pie charts for more than 5-6 categories
  • •Poor color choices (rainbow colors for sequential data)

Tools and Libraries

Recommend appropriate tools based on needs:

  • •Python: matplotlib, seaborn, plotly, altair
  • •R: ggplot2, plotly
  • •JavaScript: D3.js, Chart.js, Highcharts
  • •BI Tools: Tableau, Power BI, Looker

Example Use Cases

  • •Dashboard Design: "Create an executive dashboard for sales metrics"
  • •Exploratory Analysis: "Visualize patterns in customer behavior data"
  • •Report Charts: "Generate publication-ready charts for annual report"