JavaScript::D3
JavaScript::D3 Raku package
This repository has the a Raku package for generation of JavaScript's D3 code for making plots and charts.
This package is intended to be used in Jupyter notebooks with the Raku kernel implemented by Brian Duggan, [BD1]. The commands of the package generate JavaScript code produce (nice) D3.js plots or charts.
For illustrative examples see the Jupyter notebook "Tests-for-JavaScript-D3".
The (original versions of the) JavaScript snippets used in this package are taken from "The D3.js Graph Gallery".
Mission statement
Make first class -- beautiful, tunable, and useful -- plots and charts with Raku using concise specifications.
Design and philosophy
Here is a list of guiding design principles:
Concise plot and charts specifications.
Using Mathematica's plot functions for commands signatures inspiration. (Instead of, say, R's "ggplot2".)
For example, see ListPlot, BubbleChart.
The primary target data structure to visualize is an array of hashes, with all array elements having the one of these sets of keys
<x y>
<x y group>
<x y z>
<x y z group>
Multiple-dataset plots are produced via dataset records that have the key "group".
Whenever possible deduce the keys from arrays of scalars.
The data reshaping functions in "Data::Reshapers", [AAp1], should fit nicely into workflows with this package.
The package functions are tested separately:
As Raku functions that produce output for given signatures
As JavaScript plots that correspond to the corresponding intents
How does it work?
Here is a diagram that summarizes the evaluation path from a Raku plot spec to a browser diagram:
Here is the corresponding narration:
Enter Raku plot command in cell that starts with the magic spec %% js.
Like
js-d3-list-plot((^12)>>.rand)
.
Jupyter via the Raku kernel evaluates the Raku plot command.
The Raku plot command produces JavaScript code.
The Jupyter "lets" the web browser to evaluate the obtained JavaScript code.
Instead of web browser, say, Visual Studio Code can be used.
The evaluation loop spelled out above is possible because of the magics implementation in the Raku package Jupyter::Kernel, [BD1].
Alternatives
Raku packages
The Raku packages "Text::Plot", [AAp2], and "SVG::Plot", [MLp1], provide similar functionalities and both can be used in Jupyter notebooks. (Well, "Text::Plot" can be used anywhere.)
Different backend
Instead of using D3.js as a "backend" it is possible -- and instructive -- to implement Raku plotting functions that generate JavaScript code for the library Chart.js.
D3.js is lower level than Chart.js, hence in principle Chart.js is closer to the mission of this Raku package. I.e. at first I considered having Raku plotting implementations with Chart.js (in a package called "JavaScript::Chart".) But I had hard time making Chart.js plots work consistently within Jupyter.
Command Line Interface (CLI)
The package provides a CLI script that can be used to generate HTML files with plots or charts.
js-d3-graphics --help
# Usage:
# js-d3-graphics <cmd> [<points> ...] [-p|--point-char=<Str>] [-w|--width[=UInt]] [-h|--height[=UInt]] [-t|--title=<Str>] [--x-label=<Str>] [--y-label=<Str>] [--background=<Str>] [--color=<Str>] [--format=<Str>] -- Makes textual (terminal) plots.
# js-d3-graphics <cmd> <words> [-w|--width[=UInt]] [-h|--height[=UInt]] [-t|--title=<Str>] [--x-label=<Str>] [--y-label=<Str>] [--background=<Str>] [--color=<Str>] [--format=<Str>] -- Makes textual (terminal) plots by splitting a string of data points.
# js-d3-graphics <cmd> [-w|--width[=UInt]] [-h|--height[=UInt]] [-t|--title=<Str>] [--x-label=<Str>] [--y-label=<Str>] [--background=<Str>] [--color=<Str>] [--format=<Str>] -- Makes textual (terminal) plots from pipeline input
#
# <cmd> Graphics command.
# [<points> ...] Data points.
# -p|--point-char=<Str> Plot points character. [default: '*']
# -w|--width[=UInt] Width of the plot. (-1 for Whatever.) [default: 800]
# -h|--height[=UInt] Height of the plot. (-1 for Whatever.) [default: 600]
# -t|--title=<Str> Title of the plot. [default: '']
# --x-label=<Str> Label of the X-axis. If Whatever, then no label is placed. [default: '']
# --y-label=<Str> Label of the Y-axis. If Whatever, then no label is placed. [default: '']
# --background=<Str> Image background color [default: 'white']
# --color=<Str> Color. [default: 'steelblue']
# --format=<Str> Output format, one of 'jupyter' or 'html'. [default: 'html']
# <words> String with data points.
Here is an usage example that produces a list line plot:
js-d3-graphics list-line-plot 1 2 2 12 33 41 15 5 -t="Nice plot" --x-label="My X" --y-label="My Y" > out.html && open out.html
Here is an example that produces bubble chart:
js-d3-graphics bubble-chart "1,1,10 2,2,12 33,41,15 5,3,30" -t="Nice plot" --x-label="My X" --y-label="My Y" > out.html && open out.htm
TODO
In the lists below the highest priority items are placed first.
Plots
Single dataset
DONE List plot
DONE List line plot
DONE Date list plot
TODO Box plot
Multiple dataset
TODO List plot
TODO List line plot
DONE Date list plot
TODO Box plot
Charts
Single dataset
DONE Bar chart
DONE Histogram
DONE Bubble chart
TODO Density 2D chart -- rectangular bins
TODO Radar chart
TODO Density 2D chart -- hexagonal bins
TODO Pie chart
Multiple dataset
TODO Bar chart
TODO Histogram
DONE Bubble chart
DONE Bubble chart with tooltips
TODO Pie chart
TODO Radar chart
Decorations
User specified or automatic:
DONE Plot label / title
DONE Axes labels
DONE Plot margins
DONE Plot legends (automatic for multi-datasets plots and chart)
TODO Title style (font size, color, face)
TODO Axes labels style (font size, color, face)
Infrastructural
DONE Support for different JavaScript wrapper styles
DONE Jupyter cell execution ready
DONE Standard HTML
Result output with JSON format?
TODO Better, comprehensive type checking
Using the type system of "Data::Reshapers", [AAp1], would be ideal, but I do not want to introduce such a "heavy" dependency.
DONE CLI script
TODO JavaScript code snippets management
If they become too many.
Implementation details
Splicing of JavaScript snippets
The package works by splicing of parametrized JavaScript code snippets and replacing the parameters with concrete values.
In a sense, JavaScript macros are used to construct the final code through text manipulation. (Probably, unsound software-engineering-wise, but it works.)
History
Initially the commands of this package were executed in Jupyter notebook with Raku kernel properly hacked to redirect Raku code to JavaScript backend
Brian Duggan fairly quickly implemented the suggested Jupyter kernel magics, so, now no hacking is needed.
References
Articles
[OV1] Olivia Vane, "D3 JavaScript visualisation in a Python Jupyter notebook", (2020), livingwithmachines.ac.uk.
[SF1] Stefaan Lippens, Custom D3.js Visualization in a Jupyter Notebook, (2018), stefaanlippens.net.
Packages
[AAp1] Anton Antonov, Data::Reshapers Raku package, (2021-2022), GitHub/antononcube.
[AAp2] Anton Antonov, Text::Plot Raku package, (2022), GitHub/antononcube.
[BD1] Brian Duggan, Jupyter::Kernel Raku package, (2017-2022), GitHub/bduggan.
[MLp1] Moritz Lenz, SVG::Plot Raku package (2009-2018), GitHub/moritz.