react-material-admin VS echarts

Template, starter kit, theme, boilerplate – these are the words with a very similar meaning – a set of files and folders that serve as a starting point for an application. There are various examples, such as free templates on Github like create-react-app or react-material-admin, ThemeForest with lots of WordPress themes, Template Monster, Creative-Tim and Flatlogic with premium react, angular, vue templates, and other resources that specialize in web templates, and so on. Flatlogic alone sold at least 20,000 licenses since its launch in 2013. Metronic has more than 100,000 sales. The number of usages of free templates is even higher and is doubtly countable. How many users use create-react-app on a daily basis?

Demo GitHub Price: $149

Rating: 1k stars on GitHub for a limited free version of the template

Take a look at this: https://github.com/flatlogic/react-material-admin. In my opinion, the best admin dashboard under the MIT license. If someone has something better, share the link please.

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`); } })();

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/

Coded mainly in Perl and Gnuplot, recently extended by Python Pandas and JavaScript Tabulator and ECharts

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.

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 .

apache-echarts for charting system (it has 3d chart anyway)

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.

ECharts -- open source js lib for enterprise-grade charts