This page demonstrates 2D scatter plots with marginal distributions in JSPlots.
Multi-Dimensional Health Data
Use the X and Y dropdowns to explore different dimension combinations. Try: mass vs height, age vs bmi, etc. Marginal distributions show on both axes.
Plot Attributes
Axes
1.0(0.25 - 2.5)
Data: df1
Multiple Styling Options
Use the Color dropdown to change visual encoding. Try different combinations to highlight different aspects of the data.
Plot Attributes
Axes
1.0(0.25 - 2.5)
Data: df2
Faceting Example - Weather Data
Use the 'Facet by' dropdown to split the data. Notice: Marginal distributions appear when no faceting is selected, but disappear when a facet is applied to save space.
Plot Attributes
Axes
Facets
1.0(0.25 - 2.5)
Data: df3
Two-Dimensional Faceting - Student Performance
Uses Facet 1 and Facet 2 to create a grid of subplots. Default shows grade × major. Try different combinations like semester × major. Set either facet to 'None' to reduce to single facet or no faceting.
Plot Attributes
Axes
Facets
1.0(0.25 - 2.5)
Data: df4
Complex Multi-Dimensional Exploration
Demonstrates all features together: 4 dimensions for X/Y axes, multiple color options, and faceting. Explore different combinations to find interesting patterns.
Plot Attributes
Axes
Facets
1.0(0.25 - 2.5)
Data: df5
Time Series Scatter with Filters
Use date and quarter filters to focus on specific time periods. Points are colored by portfolio to show different investment trajectories.
Filters
1 - 2
Plot Attributes
Axes
1.0(0.25 - 2.5)
Data: df6
Struct Data Source Example
This scatter plot uses data from a struct containing multiple DataFrames.
The ExperimentData struct holds both measurements and metadata.
Charts reference the measurements DataFrame using Symbol("experiment.measurements").
Experiment Measurements from Struct Data Source
This example shows how to use a struct as a data source. The ExperimentData struct contains measurements and metadata DataFrames. Access struct fields via dot notation.