vega-lite
echarts
vega-lite | echarts | |
---|---|---|
17 | 18 | |
4,522 | 59,258 | |
1.0% | 0.5% | |
9.2 | 8.6 | |
8 days ago | 3 days ago | |
TypeScript | TypeScript | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
vega-lite
- FLaNK-AIM Weekly 06 May 2024
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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.
echarts
- Ask HN: What's the best charting library for customer-facing dashboards?
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A web crawler program for crawling Echarts official website examples implemented by Puppeter
import puppeteer from "puppeteer"; import fs from "node:fs"; import { storiesTpl, storiesArgs, generOptions, generOptionsWithFn, } from "./template.mjs"; const ECHARTS_BASE_URL = "https://echarts.apache.org/examples/en/index.html"; function capitalizeFirstLetter(str) { if (!str || str.length === 0) { return ""; } str = str.toLowerCase(); return str.charAt(0).toUpperCase() + str.slice(1); } (async function () { const browser = await puppeteer.launch(); const page = await browser.newPage(); // Navigate the page to a URL await page.goto(ECHARTS_BASE_URL); // Set screen size await page.setViewport({ width: 1080, height: 1024 }); // Type into search box // const examples = await page.type([".example-list-panel"]); const searchResultSelector = ".example-list-panel > div"; const results = await page.$$(searchResultSelector); for (const element of results) { // gener namespace const ele = await element.$(".chart-type-head"); const title = await ele.evaluate((el) => el.textContent); let namespace = title.split(" ").filter(Boolean); namespace = namespace.slice(0, namespace.length - 1); namespace = namespace .map((item) => item.replace("\n", "").replace("/", "")) .filter(Boolean) .join(""); console.log(`${namespace} start`); const instances = await element.$$(".row .example-list-item"); const components = []; for (const instance of instances) { // title const titleElement = await instance.$(".example-title"); const subTitle = await titleElement.evaluate((el) => el.textContent); const titles = subTitle .split(" ") .filter(Boolean) .map((item) => item .replace(/\+/g, "") .replace(/\(/g, "") .replace(/\)/g, "") .replace(/-/g, "") ); const title = titles.map((item) => capitalizeFirstLetter(item)).join(""); const link = await instance.$(".example-link"); const newPagePromise = new Promise((resolve) => { browser.on("targetcreated", async (target) => { if (target.type() === "page") { const targetPage = await target.page(); const url = await targetPage.url(); if (url.includes("editor")) { resolve(targetPage); } } }); }); await link.click(); const newPage = await newPagePromise; await newPage.setViewport({ width: 40000, height: 20000 }); await newPage.waitForSelector(".ace_text-layer"); await new Promise((resolve) => { setTimeout(() => { resolve(); }, 3000); }); let content = await newPage.evaluate( () => document.querySelector(".ace_text-layer").innerText ); content = content .replace(/\[\]/g, "[] as any") .replace(//g, "") .replace(/var/g, "let"); let options; if (content.includes("myChart")) { options = generOptionsWithFn({ options: content }); } else { options = generOptions({ options: content }); } components.push({ options, title }); await newPage.close(); } const args = components .filter(({ options }) => { if (options.includes("$")) return false; return true; }) .map(({ options, title }) => storiesArgs({ options: options, name: title }) ) .join("\r\n"); const scripts = storiesTpl({ namespace: `Charts/${namespace}`, components: args, }); fs.writeFileSync(`./bots/assests/${namespace}.stories.ts`, scripts); console.log(`${namespace} end`); } })();
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Show HN: Paisa – Open-Source Personal Finance Manager
I want to know where my money goes. I like to look at stacked-area (or column) charts of the categories of spending. To make this work I have some software I made ~20 years ago that does double-entry book-keeping. At the end of the month, I import statements from financial service providers (eg: Wells Fargo, Chase, PayPal, Stripe, etc). Lots of stuff is repeat purchases (eg: Shell Gas) and my software automatically categorises. Some transactions I have to categorise manually. Each category / vendor becomes an expense-account and my banks and CCs exist as assets and liabilities.
Once the import and reconciliation is done I pull up a my column chart that shows where the money went -- and can compare over time -- see a full year of movement. I've been through various charting libraries with it and most recently moved to ECharts[0] -- so I'm planning to expand with Treemap and Sankey style visuals.
The import process, which I do monthly takes maybe an hour. I'm importing from like 5 bank accounts, 3 payment processors, 4 CC providers. The part that takes the longest is signing into their slow sites, navigating past pop-up/interstitial, getting to their download page and waiting for it to download. Loads of these sites (WF, Chase) have been "modernised" and have some real bullshit UI/UX going on -- lags, no keyboard, elements jump around, forms can't remember state, ctrl+click won't open in a new page cause that damned link isn't actually a link but some nested monster of DIVs with 19 event listeners on each one -- and somehow still all wrong.
I think the most-best feature would be to have some tool automatically get all my transactions from all these providers into one common format. Gimmee some JSON with like 10 commonly-named fields for the normal stuff and then 52 other BS fields that each provider likes to add (see a PayPal CSV for example). Does that exist and I just don't know?
[0] https://echarts.apache.org/
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Personal Sträva Activity Statistics
Coded mainly in Perl and Gnuplot, recently extended by Python Pandas and JavaScript Tabulator and ECharts
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Build complex SPAs quickly with vue-element-admin
Dashboards have a lot of charts for different forms and data. This is another common requirement. This template recommends Apache ECharts, a powerful, easy-to-use, and flexible JavaScript visualization library.
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Using Apache ECharts in React Native - wrn-echarts
We have developed an open source graphics library for react native APP, which is based on Apache ECharts and uses RNSVG or RNSkia for rendering in a way that is almost identical to using it in the web, and can satisfy most graphics situations. The project source code is available at https://github.com/wuba/wrn-echarts .
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Best practice for UI design in scientific app
apache-echarts for charting system (it has 3d chart anyway)
- [OC] The crude birth rate in European Union from 1960 to 2020
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Use types (which import other types that reference the DOM) inside a web-worker!
How are you importing the definition? Assuming you are using "apache/echarts" and not some other lib named "echarts", you should be able to import DatasetModel directly and let tree shaking trim out what you're not using.
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Pulling and visualizing data from a database client side
ECharts -- open source js lib for enterprise-grade charts
What are some alternatives?
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic
Chart.js - Simple HTML5 Charts using the <canvas> tag
vega-tooltip - Tooltip Plugin for Vega-Lite
d3 - Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
lightning - High performance, interactive statistical graphics engine for the web.
Highcharts JS - Highcharts JS, the JavaScript charting framework
py4cl2 - Call python from Common Lisp
vega - A visualization grammar.
Frappe Gantt - Open Source Javascript Gantt
ggplot2 - An implementation of the Grammar of Graphics in R
apexcharts.js - 📊 Interactive JavaScript Charts built on SVG