This page demonstrates 3D scatter plots in JSPlots with advanced interactive features.
Dimension selection: Choose which variables to display on x, y, and z axes
Eigenvector visualization: Show principal components (PC1, PC2, PC3) to understand data structure
Color coding: Customize visualization by category
Filtering: Filter data with interactive dropdowns (shown in gray "Filters" box)
Faceting: Create multiple plots by categorical variables (facet wrap or grid)
Synchronized camera: When using facets, camera rotation is automatically synchronized across all plots
Interactive 3D controls: Rotate, zoom, and pan to explore from all angles
Click and drag to rotate the 3D plots. Use scroll wheel to zoom!
Example 1: Basic 3D Scatter with Eigenvectors
A simple 3D scatter plot showing data points in three dimensions. The eigenvectors (PC1, PC2, PC3) show the principal components of the data,
indicating the directions of maximum variance. Toggle them on/off to see how they align with your data.
Basic 3D Scatter with Eigenvectors
Red arrow = PC1 (most variance), Green = PC2, Blue = PC3
Plot Attributes
Data: df1
Example 2: Multiple Dimensions with Axis Selection
This example has 6 dimensions. Use the dropdown menus to choose which dimensions to display on each axis.
This is perfect for exploring high-dimensional data from different perspectives!
6-Dimensional Data Explorer
Use the dropdowns to select which dimensions to visualize on each axis
Plot Attributes
Data: df2
Example 3: 3D Scatter with Filtering
Filter the data using dropdown filters to focus on specific subsets. The eigenvectors update dynamically to show
the principal components of the filtered data!
3D Scatter with Filtering
Use the filters to filter by temperature and region - eigenvectors update with the data
Filters
15 - 35
Plot Attributes
Data: df3
Example 4: Clustering Visualization
Visualize clustering results in 3D. Each color represents a different cluster.
The eigenvectors show the principal axes of variation across all clusters.
Clustering Visualization
Three distinct clusters - eigenvectors show overall data structure
Plot Attributes
Data: df4
Example 5: Time Series Visualization in 3D
Visualize temporal data in 3D space. Color represents different time periods or categories.
This is useful for understanding how multivariate time series evolve in 3D space.
Time Series in 3D Space
A spiral trajectory through 3D space - filter by time to see different segments
Filters
0 - 31.42
Plot Attributes
Data: df5
Example 6: Faceting with Synchronized Camera
When using facets, the camera view is automatically synchronized across all plots.
Rotating one plot rotates them all - perfect for comparing similar data across categories from the same perspective!
Faceted 3D Scatter with Synchronized Camera
Camera rotation is automatically synchronized across all faceted plots
Plot Attributes
Faceting
Data: df6
Example 7: Comprehensive Example
This example demonstrates all features together: multiple dimensions for axis selection, multiple color options,
filtering by continuous and categorical variables, faceting, and eigenvector visualization. Try different combinations!
Comprehensive 3D Scatter Example
All features: dimension selection, color options, filtering, faceting, and eigenvectors
Filters
15 - 35
980 - 1029.5
Plot Attributes
Faceting
Data: df7
Key Features Summary
Interactive 3D controls: Click and drag to rotate, scroll to zoom, shift+drag to pan
Organized UI: Controls are organized in sections - Filters (gray), Plot Attributes (blue), and Faceting (orange)
Dimension selection: Choose which variables to display on x, y, and z axes (dropdowns appear when 2+ dimensions available)
Eigenvector visualization: Toggle principal components on/off to understand data structure
PC1 (Red): Direction of maximum variance
PC2 (Green): Second direction of maximum variance (orthogonal to PC1)
PC3 (Blue): Third direction of maximum variance (orthogonal to PC1 and PC2)
Color selection: Switch between different categorical variables for coloring
Data filtering: Interactive multi-select dropdowns for both continuous and categorical variables
Faceting: Split your data into multiple plots by categorical variables (facet wrap or grid)
Camera synchronization: When faceting is enabled, camera rotation is automatically synchronized across all plots
Dynamic updates: All settings update immediately - eigenvectors recalculate when you change any setting
Scientific applications: Perfect for clustering visualization, dimensionality reduction, multivariate analysis, and exploratory data analysis
Tip: Hover over points to see exact coordinates! Eigenvectors are scaled for visibility.
Note: Eigenvector calculation uses a simplified power iteration method suitable for visualization purposes.