Radar Chart Generator
Create radar/spider charts for multivariate data comparison.
Reviewed by the ToolNestr Editorial Team — July 2026
Configure your chart above and click Generate.
What is a radar chart?
A radar chart — also called a spider chart, web chart, or polar chart — displays multivariate data in a two-dimensional polar grid. Each variable is assigned its own radial axis that starts from the centre and extends outward. Data values are plotted along each axis and connected by lines to form a polygon. The shape and size of the polygon reveal patterns, strengths, and weaknesses across all variables at a glance.
Radar charts are particularly effective when you need to compare multiple entities across the same set of dimensions. The overlapping polygons make it easy to see which dataset performs better on which axis, and the overall shape gives an intuitive sense of balance or imbalance in the data.
How radar charts work
Each axis in a radar chart represents a separate variable, and all axes share a common scale. The centre point represents the minimum value (typically zero), and the outer edge of the grid represents the maximum value. Values are plotted at the appropriate distance from the centre along each axis, and consecutive points are connected with straight lines to create a closed polygon.
Worked example
How to create a radar chart
Set up your axes
Choose how many axes you need (3–8) and label each one. The labels appear around the chart perimeter. Each axis represents a variable you want to measure.
Enter your datasets
Add up to 3 datasets, each with a unique name and color. Type the values as comma-separated numbers in the same order as your axis labels. Leave a dataset blank to exclude it.
Generate and export
Click Generate to render the radar chart with your data. The chart shows overlapping polygons for easy comparison. Download as PNG for reports and presentations.
Employee Skill Assessments
Plot team members across skills such as communication, technical ability, leadership, creativity, and reliability. Managers can identify training needs and build balanced teams by comparing skill polygons side by side.
Product Feature Comparison
Compare competing products across attributes like price, performance, durability, design, and support. Radar charts make it obvious which product leads in each category and where trade-offs exist.
Sports Player Analysis
Evaluate athletes across stats like speed, endurance, strength, agility, and accuracy. Coaches use radar charts to compare players, scout opponents, and design training programs targeting weak areas.
User Experience Evaluation
Measure UX dimensions like learnability, efficiency, memorability, error recovery, and satisfaction. Map the experience of different user groups or compare before-and-after redesigns on a single chart.
Tips for effective radar charts
Keep axes independent
Each axis should measure a distinct dimension. Avoid combining correlated metrics on the same chart — if two axes always move together, consider merging them into a single composite score.
Normalise your scales
All axes must share the same scale range for the polygon shapes to be meaningful. A common approach is to use percentages (0–100) or z-scores. Mixing scales on different axes distorts the visual comparison.
Aim for 5–7 axes
Three axes produce a triangle (minimal information) and more than eight axes create a cluttered, hard-to-read chart. Five to seven axes provide the best balance between detail and readability.
Limit overlapping datasets
Comparing more than three datasets on one radar chart becomes visually confusing. If you need to compare more entities, create separate charts or use small multiples with consistent scales.
When to use a radar chart
Radar charts shine when the goal is to compare multivariate profiles across a small number of entities. Use them for skill gap analysis, competitive benchmarking, product feature comparison, performance reviews, and any scenario where the overall balance across dimensions matters as much as the individual scores. The human eye quickly recognises symmetrical vs skewed polygons, making radar charts excellent for spotting outliers and dominant performers.
However, radar charts are less effective for precise value comparisons — the human eye is better at comparing bar heights or line slopes than polygon vertex positions. Always pair radar charts with exact numeric labels or a table for readers who need absolute figures.
Limitations to consider
Radar charts have a few well-known limitations. The perceived area of a polygon can be misleading — a dataset that scores 100 on every axis creates a large outer polygon, but one that scores 50 on all axes still creates a recognisable shape. Area perception does not scale linearly, so small differences near the centre appear proportionally larger than the same differences near the outer edge.
Ordering of axes also affects how the polygon looks. Changing the axis order rotates the shape and can create a different visual impression even when the data is identical. Always present axes in a logical order (by category, ranked importance, or natural grouping) to avoid accidentally emphasising one axis over another.
Scaling and axis ranges
This tool uses a fixed 0–100 scale for all axes to ensure consistent, readable charts. All values you enter are interpreted relative to this scale. If your raw data has a different range (e.g. 1–5 Likert scores or 0–10 ratings), multiply each value by a factor that maps it onto 0–100. For example, a Likert score of 4 out of 5 becomes 80 on the 0–100 scale. This normalisation ensures that every axis contributes equally to the final chart and that polygon shapes reflect relative performance rather than arbitrary differences in measurement units.
Related tools
- Pie Chart Generator — show proportions of a whole with circular slices
- Scatter Plot Generator — visualise relationships between two numerical variables
- Box Plot Generator — display distribution statistics with quartiles and outliers
Frequently asked questions
What is a radar chart?
A chart that displays multivariate data in a 2D polar grid with each variable on its own axis.
How many datasets can I compare?
Up to 3 datasets, each with a different color, shown as overlapping polygons.
How many axes are supported?
Between 3 and 8 axes. Fewer than 3 is a line; more than 8 gets cluttered.
When should I use a radar chart?
Excellent for comparing skills, product features, performance metrics across categories.