Methods for implementing custom charts

ECharts provides custom chart functionality primarily through the custom series type. Developers can set the `type` to `custom` in the `series` configuration and use the `renderItem` function to define the drawing logic for graphic elements, supporting various basic shapes such as circles, rectangles, composite graphics, and SVG paths. Data mapping and coordinate transformation are handled via the `api` object. Custom series support complete animation and interactive effects. For handling large datasets, incremental rendering and other optimization techniques can be used to simplify graphic elements. The article demonstrates a custom dashboard case, showing how to combine standard charts with custom series, and provides responsive design and debugging tips to help developers achieve highly tailored data visualization effects.

Parallel coordinate system (Parallel) implements

Parallel coordinate systems are a powerful tool in ECharts for visualizing multidimensional data. They represent different dimensions through a set of parallel axes, with data items connected by polylines, making them ideal for analyzing high-dimensional relationships. The article provides a detailed explanation of the basic configuration of parallel coordinate systems, including the usage of `parallel` and `parallelAxis`, as well as rich axis configuration options such as type, range, and color. It also covers data mapping and styling settings, including line width, opacity, and color. Interactive features support axis expansion, center point settings, and expansion count. Real-world examples demonstrate the application to car performance data. Advanced topics include integration with other charts, performance optimization strategies for handling large datasets, custom visual mapping for data-driven color encoding, and multi-series comparison methods to showcase different categories of data.

The Sankey diagram (Sankey) achieves this.

A Sankey diagram is a specific type of flow diagram used to depict the movement of energy, materials, or costs between different nodes. In the diagram, nodes are connected by arrows, with the width of the lines proportional to the flow volume. ECharts provides comprehensive support for Sankey diagrams, allowing the creation of complex flow relationship charts through simple configuration. The basic implementation requires defining two core data structures: nodes and links. Nodes support rich style customization, including colors, label display, etc., while links can be styled with gradients, transparency, and other visual effects. ECharts Sankey diagrams also offer extensive interactive features, such as node dragging and click event handling. For multi-level data, proper organization of node relationships is essential. The article further covers global style configuration, responsive design, dynamic data updates, performance optimization techniques, and practical applications like website user behavior analysis and energy flow analysis. Finally, it explains advanced interactive features, integration with other chart types, and methods for data format conversion.

The Sunburst chart implements this.

A Sunburst chart is a visualization that displays hierarchical data through concentric rings, representing parent-child relationships. ECharts supports Sunburst chart configuration, with its basic structure consisting of hierarchical data and visual mappings. The data adopts a tree-like format, including attributes such as name, value, and child nodes. It supports color mappings and value-based size mappings to enhance expressiveness. Interactive features like click-to-drill-down, highlighting, and linkage are provided. Advanced configurations include custom sector gaps, multi-level labels, and more. The chart supports dynamic data updates and optimizes performance for large datasets through techniques like chunked rendering and simplified visuals. Practical applications include e-commerce sales data analysis. It can be linked with other charts for responsive design, adapting to various screen sizes, and its functionality can be extended via custom series.

Tree Chart

The ECharts tree diagram is a type of visualization chart that displays hierarchical data, suitable for representing parent-child relationships or organizational structures. With simple configuration, it can present complex tree structures. The tree diagram data must adhere to a specific nested format, including attributes such as name, value, and children. It supports multiple layout orientations, including horizontal, vertical, and radial layouts. The chart offers rich interactive features like node expansion, collapse, zooming, and panning. Users can customize node styles, connector lines, and label displays. It also supports animation effects and optimizations for large datasets. Additionally, it can be combined with other chart types and allows dynamic data updates.

Boxplot

A boxplot is a statistical chart that displays data distribution. ECharts provides powerful boxplot visualization capabilities, supporting various configurations and interactions. The basic boxplot implementation requires data in a specific format containing statistical values across multiple dimensions. Multi-series boxplot configuration allows displaying multiple series in the same chart for comparative analysis. Boxplot styles can be customized through extensive options, including colors and border widths. In practical applications, boxplots are often combined with scatter plots to show raw data points. ECharts boxplots support various interactive features such as zooming, restoring, and saving. When handling large datasets, optimization strategies like setting `largeThreshold` can be enabled. They also support dynamic data updates, facilitating real-time monitoring scenarios.

Funnel Chart implementation

A funnel chart is a data visualization tool used to display conversion rates or quantity changes at various stages of a process. ECharts provides robust support for funnel charts, allowing flexible configuration of styles, labels, and interactive effects. The basic setup involves setting the series type to "funnel" and defining the data series. Style customization includes color settings, label display, and multi-series comparison. It supports dynamic sorting and rich interactions like highlighting and data filtering. Funnel charts can also be combined with other chart types for richer data presentation. Advanced applications include custom shapes, animation effects, and data processing techniques. Performance optimization measures, such as handling large datasets and progressive rendering, ensure smooth operation. Additionally, support for internationalization and responsive design adapts the chart to different language environments and device sizes.

Dashboard (Gauge) implementation

The ECharts dashboard is a data visualization component used to display the current value of a single indicator within a specific range. By setting the `type` of the `series` to `gauge`, a basic dashboard can be implemented, consisting of three core elements: the dial, the pointer, and the numerical display. Key configuration options include dial settings, pointer style customization, and numerical display adjustments. The dashboard supports multiple pointers, enabling the creation of progress-bar-style dashboards, and features rich animation effects and interactive event handling. It can be combined with other chart types, supports dynamic data updates and custom shapes, and can also be integrated with maps to showcase regional indicators, meeting diverse data presentation needs across various scenarios.

Heatmap implementation

A heatmap is a visualization method that displays data density through color variations. ECharts provides a powerful heatmap component that supports both Cartesian and geographic coordinate systems. The basic heatmap configuration requires three key settings: the x-axis, y-axis, and data points. The data format processing section explains how to convert raw data into the required format for heatmaps. The visual mapping configuration details how to map data values to colors. The geographic heatmap demonstrates how to achieve heatmap effects on a map. Performance optimization techniques include using progressive rendering, reducing data precision, and adjusting heat point sizes. The interaction enhancement section introduces data filtering, region zooming, and custom tooltips. Advanced application examples showcase heatmaps combined with scatter plots. The dynamic data update section explains how to achieve real-time data display. Custom rendering effects and multi-heatmap linkage provide more personalized configuration options.

Map implements the translation of this sentence into English.

ECharts maps support geographic coordinate systems and map series, accommodating both GeoJSON and TopoJSON data formats. It includes built-in simplified maps of China and the world. Before use, map data must be registered via JSON files or online resources. Visual mapping transforms data into visual elements like color and size, offering both continuous and segmented mapping methods. Maps support interactive features such as zooming, panning, and region highlighting. They can be combined with chart types like heatmaps and scatter plots for richer visualizations. Customization options include adjusting region colors and border styles. Multi-map linkage enables synchronized displays across different map levels. Dynamic data updates facilitate real-time visualization, and integration with a timeline can showcase data trends. The ECharts GL extension allows for 3D map creation, delivering stereoscopic effects.