JSPlots.jl

JSPlots.jl creates interactive, standalone HTML visualizations that work in any browser. Build line charts, scatter plots, 3D visualizations, pivot tables, and more - all from Julia without any coding in JavaScript needed. The closest comparison to what I am trying to build is probably Pluto notebooks or jupyter notebooks. The key difference is that the pages that you generate with JSPlots runnable anywhere with interactive plotting - you do not need Julia or anything else installed except for a webbrowser. This means too that the documents that are generated by JSPlots are intended to be immutable documents to present your data rather than something you develop with (as you get with the notebooks).

You can see below an overview of all of the plots that are available. You can also see a discussion on the coding practices, data formats, etc. All of this documentation was made in JSPlots.jl itself. See here for the script that generates it.

Pages

Coding Practices

General Coding Patterns: This gives an overview of the common coding patterns used across many chart types
Data Storage Formats: This shows the options for including data with the HTML
Creating Multi-Page Reports: Learn how to use JSPlotPage and Pages to create multi-page HTML reports with automatic navigation

Plot Types

Tabular and Text Data: This shows examples of TextBlock, LinkList, CodeBlock, DataTable and PivotTable.
2D Charts: This shows examples of LinePlot, AreaChart, ScatterPlot, Path, and BumpChart.
Distributional Charts: This shows examples of DistPlot, KernelDensity, PieChart and BoxAndWhiskers.
3D Charts: This shows examples of Scatter3D, Surface3D and ScatterSurface3D.
Images and Slides: This shows examples of filtered Pictures and Slides. Pictures can display static images, animated GIFs, or provide interactive filtering between multiple charts.
Variable Relationship Charts: This shows examples of CorrPlot and Graph using the same underlying correlation data. CorrPlot displays correlation matrices with hierarchical clustering dendrograms. Graph visualizes correlations as a network with nodes and edges. Both charts demonstrate different ways to explore the same correlation structure.
Situational Charts: This shows examples of Waterfall, SanKey, and RadarChart. Waterfall charts display cumulative effects of sequential positive and negative values. SanKey (alluvial) diagrams show how entities flow between categories over time. RadarChart (spider chart) displays multi-dimensional data on axes radiating from a central point, ideal for comparing items across multiple metrics.
Financial Charts: This shows examples of financial market visualization charts. CandlestickChart displays candlestick patterns for technical analysis with volume, renormalization, and time range controls.