Data Visualization Selector
Find the ideal visualization for your research data with this comprehensive selector tool. Features a guided questionnaire that analyzes your data characteristics and research goals to recommend the best chart types, an extensive visualization explorer with 16+ chart types, side-by-side comparison capabilities, and educational resources on visualization best practices. Each visualization includes interactive D3.js previews, implementation guides, and detailed use cases.
Key Features
- Guided visualization recommendation wizard
- Explorer with 16+ visualization types
- Interactive D3.js chart previews
- Side-by-side visualization comparison
- Favorites and history tracking
- Dark/light theme support
- Export recommendations as JSON
- Educational content on best practices
- Implementation code examples
- Complexity and popularity ratings
- Audience-specific recommendations
- Common mistakes and tips section
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Frequently Asked Questions
How do I choose the right chart for my data?
Chart selection depends on your data type and purpose: use bar charts for comparing categories, line charts for trends over time, scatter plots for relationships between variables, pie charts for parts of a whole (use sparingly), histograms for distributions, and box plots for comparing distributions across groups. This tool guides you through questions about your data structure, variables, and visualization goals to recommend the most effective chart type.
What is the difference between a bar chart and a histogram?
Bar charts display categorical data with gaps between bars, showing frequencies or values for different categories. Histograms display continuous numerical data with no gaps, showing the distribution of values across ranges (bins). Use bar charts for discrete categories (e.g., responses by country) and histograms for continuous measurements (e.g., distribution of test scores).
When should I use a scatter plot versus a line chart?
Use scatter plots to show relationships between two continuous variables (correlation or regression), where each point represents an observation. Use line charts to show trends or changes over time or ordered sequences, where the line connects related data points in sequence. Scatter plots emphasize individual data points and patterns, while line charts emphasize overall trends and direction of change.
Are there visualization best practices I should follow?
Yes! Key principles: (1) Choose appropriate chart types for your data, (2) Use clear, descriptive titles and axis labels, (3) Avoid 3D effects and chartjunk that distort perception, (4) Use color purposefully and accessibly, (5) Start y-axes at zero for bar charts, (6) Include data source and sample size, (7) Keep it simple - one main message per visualization, (8) Consider your audience's statistical literacy.