The RadarChart (also known as spider chart or web chart) displays multivariate data on axes starting from the same point.
Each axis represents a different variable, and values are plotted as a polygon.
Use cases:
Product comparison: Compare products across multiple features
Performance evaluation: Show strengths and weaknesses across different metrics
Nutritional analysis: Display nutritional content of foods
Skills assessment: Visualize competency levels across different skills
Portfolio analysis: Compare investment options across multiple criteria
Interactive features:
Select specific items to display
Filter variables to show
Group related variables together
Color by category
Facet for comparison
Example 1: Smartphone Comparison
Compare different smartphone models across key specifications.
Features demonstrated:
Basic radar chart with multiple axes
Multiple items displayed
Item selector to choose which phones to compare
Try this: Select different phones to compare their specifications.
Smartphone Specifications Comparison
Scores normalized to 0-100 scale. Higher values indicate better performance.
Hold Ctrl (Cmd on Mac) to select multiple
Data: phones_data
Example 2: Food Nutritional Profile with Grouped Axes
Analyze nutritional content of different foods with axes grouped by category (similar to the image in the request).
Try this: Select different foods to see their nutritional and sustainability profiles.
Food Nutritional and Sustainability Profile
Axes are grouped by category: Nutrition (blue labels), Sensory, Economics, and Sustainability. Variable limits are set to prevent outliers from dominating the scale (e.g., Vitamin C capped at 20). Higher values are better.
Hold Ctrl (Cmd on Mac) to select multiple
Data: foods_data
Example 3: Skills Assessment with Variable Selector
Evaluate employee skills across different competencies with the ability to select which skills to display.
Features demonstrated:
Variable selector for choosing which axes to display
Many variables available (only subset shown at once)
Department-based coloring
Try this: Use the variable selector to choose which skills to compare. You need to select at least 3 variables.
Employee Skills Assessment
Select which skills to compare. Colored by department.
Hold Ctrl (Cmd on Mac) to select multiple
Hold Ctrl (Cmd on Mac) to select multiple
Data: skills_data
Example 4: University Rankings with Scenarios
Compare universities across different ranking systems using scenario selection.
Features demonstrated:
Scenario selector to switch between different ranking methodologies
Multiple universities displayed simultaneously
Color-coded by region
Try this: Switch between "Overall Rankings" and "Research Focus" scenarios to see how university scores change based on different criteria.
University Rankings Comparison
Compare top universities across key metrics with two ranking methodologies. Switch scenarios to see how different ranking systems affect scores. 'Overall Rankings' provides a balanced view, while 'Research Focus' emphasizes research output and faculty quality.
Hold Ctrl (Cmd on Mac) to select multiple
Data: universities_data
Example 5: Investment Portfolio Analysis
Analyze different investment options across risk, return, and other factors.
Features demonstrated:
Financial metrics visualization
Risk/return tradeoffs
Asset class coloring
Try this: Compare different investment options to understand their risk/return profiles.
Investment Options Analysis
Compare investment options across multiple criteria. Higher values are better.
Hold Ctrl (Cmd on Mac) to select multiple
Data: investments_data
Example 6: Car Comparison from Struct Data Source
Compare different car models using data stored in a struct.
Features demonstrated:
Using a struct containing DataFrames as data source
Referencing struct fields via dot notation
Multiple DataFrames in one struct (specs and pricing)
Car Comparison from Struct Data Source
This radar chart references data from a CarData struct containing specifications and pricing DataFrames. Charts access struct fields via Symbol("cars.specifications").
Hold Ctrl (Cmd on Mac) to select multiple
Data: cars.specifications
Summary
The RadarChart provides powerful multi-dimensional visualization with these key capabilities:
Data Requirements
Row structure: Each row represents one item (product, person, entity)
Value columns: Numeric columns that become radar axes
Label column: Identifies each item
Optional categorical columns: For coloring and faceting
Key Features
Grouped axes: Group related metrics together with labels
Variable limits: Set maximum values per variable to prevent outliers from dominating
Scenarios: Switch between different data scenarios
Variable selector: Choose which axes to display
Item selector: Choose which items to compare
Color coding: Color items by category
Flexible scaling: Auto-scale or specify maximum value
Best Practices
3-12 axes: Too few is uninformative, too many is cluttered
Similar scales: Works best when all metrics are on similar scales
Normalized data: Consider normalizing to 0-100 or 0-10 for clarity
Limited items: Show 1-4 items per chart for readability