Q&A 6 What are the most effective plots for comparing values across categories?
6.1 Explanation
Before diving into specific charts, it’s helpful to understand the landscape of visualization tools available when comparing a numeric variable across different groups (i.e., categorical levels).
Depending on what insight you’re after — distribution shape, summary statistics, or raw values — the best choice will vary.
🔹 Common Visualization Types
| Plot Type | Shows Distribution | Best For |
|---|---|---|
| Bar Plot | ❌ No | Comparing total counts or group means |
| Histogram | ✅ Yes (1 group) | Viewing frequency distribution |
| Density Plot | ✅ Yes | Smooth shape of distribution |
| Box Plot | ✅ Yes | Spread, center, and outliers |
| Violin Plot | ✅ Yes | Shape + quartiles |
| Ridge Plot | ✅ Yes | Comparing shapes across many groups |
| Strip Plot | ✅ Yes | Raw points (ideal for small datasets) |
| Swarm Plot | ✅ Yes | Raw data with spacing |
| Dot Plot | ❌ Summary only | Central tendency with optional error bars |
| Bar + Error Bars | ❌ Summary only | Means with uncertainty (CI/SE bars) |
📌 Choosing the Right Plot
Ask yourself:
- How many groups are you comparing?
- Is the dataset small or large?
- Do you want to show individual points or a summary?
- Is distribution shape important?
Start with simple visuals like bar plots and box plots. Use richer plots (like violin or ridge) when you want to uncover deeper patterns.
✅ Takeaway: Choosing the right visual makes patterns across categories easier to see — and ensures your insights are both accurate and easy to communicate.