A comprehensive visual vocabulary of 72 data visualization types, organized into 10 categories by purpose. Choose the right chart type for your data story - from bar charts and line graphs to treemaps and sankey diagrams.
Based on the FT Visual Vocabulary • 72 chart types across 10 categories
Select the best data visualization based on what you want to communicate. Match your data type and goal to one of these 10 chart categories.
Visualize trends, patterns, and temporal changes in your data. Ideal for tracking progress, identifying seasonality, or forecasting future values.
Compare sizes and quantities across categories. Perfect for showing relative differences and making straightforward value comparisons.
Show how individual components contribute to a complete picture. Best for composition analysis, percentage breakdowns, and hierarchical data.
Highlight variations from a reference point like zero, average, or target. Useful for showing positive/negative performance and sentiment analysis.
Discover patterns and connections between two or more variables. Essential for statistical analysis, trend identification, and hypothesis testing.
Display items sorted by value to show relative position. Great for leaderboards, top/bottom lists, competitive analysis, and performance rankings.
Understand the frequency and spread of data points. Critical for statistical analysis, identifying outliers, and understanding data shape.
Track movement, transfers, and connections between entities. Perfect for showing processes, migrations, user journeys, and resource allocation.
Display data with geographic context on maps. Essential for regional analysis, location-based insights, and understanding geographic distribution patterns.
Some data stories require unique chart types that don't fit traditional categories. These specialized charts serve specific analytical and organizational needs.
Common questions about data visualization types and how to choose the right chart
The Atlas of Charts is a comprehensive visual reference guide featuring 72 data visualization types organized into 10 categories. It was created to help data analysts, business professionals, and designers quickly identify the most appropriate chart type for their specific data story. Think of it as a periodic table for data visualization.
Charts are organized into 10 functional categories based on the analytical question they answer: Change over Time (13 charts), Magnitude (10), Part-to-whole (10), Deviation (4), Correlation (5), Ranking (6), Distribution (9), Flow (4), Spatial (8), and Other (3). This organization helps you start with your data question and find the right visualization.
ChartGen.ai currently supports 9 chart types with full AI-powered generation: Line Chart, Bar Chart, Pie Chart, Area Chart, Scatter Plot, Heatmap, Combo Chart, Waterfall Chart, and Funnel Chart. These charts are marked with a green 'LIVE' badge in the atlas. We're continuously adding support for more chart types.
Charts marked with a green 'LIVE' badge are fully supported by ChartGen.ai - you can click on them to see a preview and then create your own with your data. Charts with a 'Coming Soon' label are part of our development roadmap and will be added in future updates. You can still learn about these chart types in the atlas.
Both show trends over time, but they serve different purposes. Use a Line Chart when you want to emphasize the rate of change and compare multiple series clearly. Use an Area Chart when you want to emphasize the cumulative magnitude or show part-to-whole relationships over time. Area charts work best with fewer data series to avoid visual clutter.
Use a Bar Chart when comparing values across categories, especially when you have many categories or need precise comparisons. Use a Pie Chart only when showing parts of a whole with 2-5 categories and when the proportions are meaningfully different. If your pie slices would be similar in size, a bar chart is usually more effective.
A Waterfall Chart (also called a bridge chart) shows how an initial value is affected by a series of positive and negative changes to reach a final value. It's commonly used in financial analysis to show how revenue becomes profit, explain budget variances, or break down the components of change between two time periods.
Use a Scatter Plot when you want to show the relationship between two continuous variables and identify individual data points, outliers, or clusters. Use a Heatmap when you have categorical data on both axes and want to show the intensity or frequency of combinations. Heatmaps are great for showing patterns across time periods or categories.
Spatial charts (Choropleth, Cartogram, Flow Map, etc.) are used when geographic location is a key dimension of your data. They help answer questions like 'Where are sales highest?', 'How do values vary by region?', or 'What are the movement patterns between locations?'. ChartGen.ai is working on adding map-based visualizations in future updates.
We're always expanding ChartGen.ai's capabilities based on user needs. You can share your chart type requests with us through the Ada.im platform. Popular requests are prioritized in our development roadmap. Currently, we're working on adding Donut, Treemap, Radar, and more statistical chart types.