plot
vega-lite
plot | vega-lite | |
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3 | 16 | |
28 | 4,477 | |
- | 0.8% | |
4.4 | 9.2 | |
4 months ago | 6 days ago | |
Common Lisp | TypeScript | |
Microsoft Public License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
plot
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[S] Examples from Chapter 1 of the Introduction to the Practice of Statistics
The examples from the first chapter of the Introduction to the Practice of Statistics, In Lisp-Stat, are complete and on github. This chapter is mostly about data visualisation, and anyone who uses PLOT might find the additional examples useful.
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Plotting
But, that's part of the reason for PLOT -- to hide that ugliness and make it easier to work with from Common Lisp. Have you found something specific that PLOT won't let you do? If so, open an issue and I'll take a look.
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Uncle Stats Wants You
If you want to learn Lisp using a real-world problem, consider enhancing the stem-and-leaf plots. This is a good way to learn Common Lisp basics. It uses looping, printing and other basic programming constructs with text output. Specifically we need split stems and back-to-back stem plots.
vega-lite
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Ask HN: What's the best charting library for customer-facing dashboards?
I like Vega-Lite: https://vega.github.io/vega-lite/
It’s built by folks from the same lab as D3, but designed as “a higher-level visual specification language on top of D3” [https://vega.github.io/vega/about/vega-and-d3/]
My favorite way to prototype a dashboard is to use Streamlit to lay things out and serve it and then use Altair [https://altair-viz.github.io/] to generate the Vega-Lite plots in Python. Then if you need to move to something besides Python to productionize, you can produce the same Vega-Lite definitions using the framework of your choice.
- Vega-Lite – A Grammar of Interactive Graphics
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Vega-Altair: Declarative Visualization in Python
Box zoom would need to be added to Vega-Lite first, and there has been some discussion around it in https://github.com/vega/vega-lite/issues/4742. Bottom line is that there's nothing blocking its implementation, someone just needs to do the work in Vega-Lite. And once released in Vega-Lite, Altair would pick it up automatically with how we generate the Altair API from the Vega-Lite schema.
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Gnuplotlib: Non-Painful Plotting for NumPy
I also have difficulties with Gnuplot and Matplotlib. I like Vega that allows me to create visualisations in a declarative way. If I really need something special I go with d3.js, which had a really steep learning curve but with ChatGPT it should have become easier for beginners.
[1] https://vega.github.io/vega-lite/
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Elixir Livebook is a secret weapon for documentation
To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).
In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.
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Observable Plot: The JavaScript library for exploratory data visualization
Nice, would be nice to have it integrated in GitHub markdown.
Looks similar to Vega or Vega-lite(https://vega.github.io/vega-lite/). Definitely as rich as D3.js but gets the job done for simple visualisations.
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[AskJS] Javascript statistics library with period selection
Vega-lite can do this https://vega.github.io/vega-lite/
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2022 FIFA World Cup finishing position probability per team [OC]
The underlying data is from an online betting site. Data analysis was done in Python and I used Vega/Altair for the visualisation.
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Plotting
I have a bunch of data that I want to plot. I'm using lisp-stat, which is pretty good, for data frames and analysis. However, lisp-stat uses vega-lite for plotting and to put it mildly, vega-lite is fucking awful.
What are some alternatives?
clog-plotly - CLOG Plugin for Plotly.js
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic
weir - (deprecated) A system for making generative systems
vega-tooltip - Tooltip Plugin for Vega-Lite
cl-statistics - Updated (somewhat) version of Larry Hunter's CL-Statistics library
py4cl2 - Call python from Common Lisp
numerical-utilities - Utilities for numerical programming
lightning - High performance, interactive statistical graphics engine for the web.
xls-archive - Statistics routines in Common Lisp and XLispStat
plot - A node library to display charts in popup windows and save them as pngs. Supports observablehq/plot, vega-lite and plotly out of the box.
py4cl - Call python from Common Lisp
d3 - Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada: