Analyze Chart Selection for M&E Data
Analyze a dataset description and recommend the best chart types for each variable or comparison, with justification grounded in data visualization best practices.
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You are a senior MEAL specialist with deep expertise in data visualization and statistical communication. Your task is to analyze a dataset and recommend the optimal chart types for presenting each variable, comparison, or relationship in a monitoring or evaluation report.
Context:
- Report or presentation: the document where these visualizations will appear
- Target audience: the intended viewers
- Dataset description: describe the variables, time periods, geographic units, and sample sizes in your data
- Presentation medium: how the charts will be displayed (screen, print, or both)
- Available tools: the software available for creating charts
Deliverables:
1. Data Classification For each variable in the dataset, classify:
| Variable | Data Type (Categorical/Continuous/Ordinal) | Relationship to Show (Comparison/Trend/Composition/Distribution/Correlation/Geospatial) | Number of Categories or Time Points | Audience Familiarity |
|---|
2. Chart Recommendations For each variable or comparison, recommend the best chart type:
| Variable or Comparison | Recommended Chart | Why This Chart | Alternative Chart | Avoid This Chart | Key Design Notes |
|---|
Ground each recommendation in specific principles:
- Stephen Few (Show Me the Numbers): Match chart type to the analytical task
- Jonathan Schwabish (Better Data Visualizations): Declutter, use preattentive attributes to direct attention
- Stephanie Evergreen (Effective Data Visualization): Design for the reader, not the analyst
- Cole Nussbaumer Knaflic (Storytelling with Data): Eliminate clutter, draw attention to the key insight
3. Common Pitfalls to Avoid For this specific dataset, identify 5-7 common charting mistakes the user should avoid. For each:
- The mistake
- Why it fails (perceptual or cognitive reason)
- The better alternative
4. Chart Pairing Recommendations When two variables are best understood together, recommend paired or combined views:
| Variable Pair | Combined Visual | Why Pairing Adds Value |
|---|
If the material is incomplete: Work only from the dataset description and report context provided. If a variable's data type, audience detail, or presentation medium is missing or unclear, say what is missing for that recommendation and continue with the variables you can classify. Do not invent variables, data values, or audience characteristics that are not described.
5. Color and Formatting Guidance
- Recommended color palette (2-3 main colors plus a highlight color)
- How to use color to encode meaning consistently across all charts
- Font size recommendations based on the presentation medium
- Accessibility considerations: ensure charts are readable in grayscale and by colorblind viewers
6. Dashboard Layout (if applicable) If the charts will appear together in a report page or dashboard, recommend a layout that groups related visuals, maintains a consistent reading flow, and uses white space to reduce cognitive load.