I Tried Free Gantt Chart JavaScript Libraries So You Don’t Have To

I build small web tools for teams. Content calendars. Sprint boards. Vendor rollouts. I needed a Gantt chart more than once, and I didn’t want to pay for it. So I tested a bunch of free JavaScript options. I used each one on a real project. Some were smooth. Some were fussy. All of them taught me something.

While I was gathering sample social-media posts to populate one of those content calendars, I tripped over the need to quickly preview adult-oriented Twitter accounts without wading through endless tabs. If you ever face the same situation—say, you’re managing an NSFW creator schedule or simply vetting spicy material for a client—this Twitter nudes directory offers a single, curated gallery of high-resolution previews, saving you time and reducing the number of clicks it takes to evaluate accounts.

For teams that juggle location-specific promotions in the adult industry, you might also want a snapshot of what’s trending in Northern New Jersey. A brief scroll through the local listings on Backpage Bayonne delivers real-time insight into popular services and pricing, helping you calibrate campaigns or content calendars without the usual sign-up hassles.

Here’s what happened.

If you want the blow-by-blow of my full test bench, I put together a companion deep-dive: I tried free Gantt chart JavaScript libraries so you don’t have to. It lays out every metric and edge case I pushed through the tools.

What I needed (and what I didn’t)

  • Drag and drop is nice, but not a must.
  • I need clear bars, dates, and simple links between tasks.
  • I like clean code and light setup.
  • I don’t need every feature under the sun. But I hate weird bugs.

You know what? I care a lot about load speed too. My brain stalls when a chart stutters.

I also stumbled upon EJSChart, a lightweight charting library that ticks many of these boxes, though I haven’t put it through a full project yet.


Frappe Gantt — my “quick win” tool

Project: I used this to plan a 6-week content calendar for a small team. We had 28 tasks. Blog drafts, edits, social posts. I loaded tasks from JSON. I set dependencies for handoffs.
If you want to dig into the API details, check out the Frappe Gantt official website.

What I liked:

  • Setup took minutes. The API felt sane.
  • The bars look clean. The labels are easy to read.
  • Dependencies worked well. Nice little arrows.
  • Good for small to mid lists.

What bugged me:

  • It slowed down once I hit around 150+ tasks.
  • Zoom control is light. I wanted more views.
  • No built-in resource view.

Tiny snippet I used:

<div id="gantt"></div>
<script>
  const tasks = [
    { id: 'write-post', name: 'Write Post', start: '2025-01-02', end: '2025-01-06', progress: 35 },
    { id: 'edit-post', name: 'Edit', start: '2025-01-07', end: '2025-01-08', dependencies: 'write-post' }
  ];
  const gantt = new Gantt('#gantt', tasks, { view_mode: 'Day' });
</script>

License note: MIT. Good for most use cases.

My take: If I need something fast and simple, I start here. It just works.


Google Charts Gantt — steady, but not touch-friendly

Project: I built a vendor rollout timeline for a retail client. About 60 tasks. Clear tooltips were key, since non-tech folks used it. We used Google’s loader and drew the chart from a DataTable.
Google’s full reference for this component lives in the Google Charts Gantt documentation.

What I liked:

  • Stable. It rendered the same way on every laptop I tested.
  • Tooltips and labels looked tidy with no extra work.
  • Date parsing was strict, which saved me from data errors.

What bugged me:

  • No drag and drop. It’s view-only.
  • Styling can feel rigid. CSS changes hit a wall fast.
  • Touch support felt rough on phones.
  • Needs the Google loader, so no fully offline use.

Tiny snippet I used:

<div id="gantt"></div>
<script>
  google.charts.load('current', {'packages':['gantt']});
  google.charts.setOnLoadCallback(drawChart);

  function daysToMs(d) { return d * 24 * 60 * 60 * 1000; }

  function drawChart() {
    const data = new google.visualization.DataTable();
    data.addColumn('string','Task ID');
    data.addColumn('string','Task Name');
    data.addColumn('string','Resource');
    data.addColumn('date','Start');
    data.addColumn('date','End');
    data.addColumn('number','Duration');
    data.addColumn('number','Percent Complete');
    data.addColumn('string','Dependencies');

    data.addRows([
      ['kickoff','Kickoff','PM', new Date(2025,0,3), new Date(2025,0,4), null, 100, null],
      ['ship','Ship Wave 1','Ops', new Date(2025,0,6), new Date(2025,0,10), null, 0, 'kickoff']
    ]);

    const chart = new google.visualization.Gantt(document.getElementById('gantt'));
    chart.draw(data);
  }
</script>

License note: Free to use under Google’s terms.

My take: Great for reports and dashboards. Not great for heavy edits.

Curious how these compare with rolling your own visuals in vanilla SVG? I put D3.js through a similar wringer in I built charts with D3.js—here’s what actually worked (and what didn’t), and plenty of the lessons cross over.


jsgantt-improved — old-school, but gets the job done

Project: I helped a contractor friend. We mapped a kitchen remodel. Tasks, links, weekends off. We printed screenshots for the crew. Real simple, but real life.

What I liked:

  • Pure JavaScript. No special build.
  • Works offline. Fast on small sets.
  • Dependencies and progress bars are fine.

What bugged me:

  • The default theme looks dated. I had to tweak CSS a lot.
  • The API names feel… classic. Not bad, just older.
  • Time zone handling took a minute to settle.

Tiny snippet I used:

<div id="gantt"></div>
<script>
  const g = new JSGantt.GanttChart(document.getElementById('gantt'), 'day');
  g.AddTaskItem(new JSGantt.TaskItem(1,'Demo','2025-02-03','2025-02-05','ggroupblack', '', 0, '', 0, 0, 1));
  g.AddTaskItem(new JSGantt.TaskItem(2,'Rough-In','2025-02-06','2025-02-10','gtaskblue', '', 20, '1', 0, 0, 2));
  g.Draw();
</script>

License note: MIT.

My take: If you’re fine with a classic look and you want simple control, it works.


DHTMLX Gantt (GPL edition) — feature rich, but mind the license

Project: I used this for an internal hackathon board. We had 220+ tasks, live edits, zoom, and a critical path view. It took longer to wire up, but it handled the load.

What I liked:

  • Drag and drop, zoom levels, inline editing.
  • Critical path and baseline support.
  • Good performance once tuned.

What bugged me:

  • Bigger bundle and a steeper learning curve.
  • GPL license for the free edition: if your code is closed-source, you can’t use it. For open-source, you’re fine. For commercial closed code, you need a paid license.

Tiny snippet I used:

<div id="gantt_here" style="width:100%; height:380px;"></div>
<script>
  gantt.config.columns = [
    {name:"text", label:"Task", width:"*", tree:true},
    {name:"start_date", label:"Start", align:"center"},
    {name:"duration", label:"Days", align:"center"}
  ];
  gantt.init("gantt_here");
  gantt.parse({
    data: [
      {id:1, text:"Backlog", start_date:"2025-03-01", duration:5, progress:0.3, open:true},
      {id:2, text:"Code", start_date:"2025-03-06", duration:8, progress:0.1, parent:1}
    ],
    links: [{id:1, source:1, target:2, type:"0"}]
  });
</script>

My take: Powerful and steady. Just be sure the license fits your plan.


vis-timeline — not a “real

Published
Categorized as Hard Coding

I Tried 7 JavaScript Chart Libraries. Here’s What Actually Worked

I’m Kayla. I build dashboards for a living. I also make tiny charts for school PTA emails and soccer sign-ups. I’ve messed with a lot of JavaScript chart tools (and documented the full deep-dive in this breakdown if you’re curious). Some days it felt easy. Other days… whew. If you want a bird’s-eye view of the whole ecosystem, the Wikipedia comparison of JavaScript charting libraries table is a great cheat sheet when you’re weighing options.

Let me explain what I used, what broke, and what I’d use again. I’ll share real examples from my projects, too.


Quick vibe check

  • Need fast wins? Go simple.
  • Need weird charts or full control? Go lower level.
  • Need board-room polish? Go big and fancy.

If you’re hunting for a middle-ground option that stays lightweight yet flexible, give EJSChart a spin—I had a prototype up in minutes.

That’s the gist. Now the details.


Chart.js — My easy button (most weeks)

I used Chart.js for a holiday sales dashboard last winter. Two bar charts, one line chart, and a doughnut chart. Mobile first. It worked out of the box, and I shipped in a day. The defaults looked clean. The legend didn’t fight me. Small joy.

What I liked:

  • Simple setup with a CDN or npm.
  • Good docs. I didn’t get lost.
  • Plugins that help (I used datalabels for labels on bars).

What bugged me:

  • Canvas is fast, but exporting to SVG for print took extra steps.
  • Big data (like 50k points) got choppy.

Real snippet I used to show monthly sales:

<canvas id="sales"></canvas>
<script>
const ctx = document.getElementById('sales').getContext('2d');
new Chart(ctx, {
  type: 'bar',
  data: {
    labels: ['Jan','Feb','Mar','Apr','May','Jun'],
    datasets: [{
      label: 'Sales ($)',
      data: [1200, 900, 1500, 1300, 1700, 2200],
      backgroundColor: '#4f46e5'
    }]
  },
  options: {
    responsive: true,
    plugins: {
      legend: { position: 'bottom' },
      tooltip: {
        callbacks: {
          label: (ctx) => `$${ctx.parsed.y.toLocaleString()}`
        }
      }
    }
  }
});
</script>

When would I use it again? Most dashboards with normal data. Quick marketing pages. Small apps.


D3.js — The power tool that needs gloves

I built a custom “swimlane” timeline for a logistics team. It felt like Lego bricks. You build the whole thing, piece by piece. (I later wrote up what really worked and what didn’t in D3 over here.)

What I liked:

  • Total control with SVG and scales.
  • Transitions that feel smooth.
  • Works great with React or Svelte if you manage refs right.

What stressed me out:

  • Steep learning. Small change, many lines.
  • You own the axes, the legend, the layout. All of it.

Real snippet that drew circles for late orders:

const svg = d3.select('#late').attr('width', 600).attr('height', 200);

const x = d3.scaleTime()
  .domain(d3.extent(data, d => d.date))
  .range([40, 560]);

svg.selectAll('circle')
  .data(data)
  .join('circle')
  .attr('cx', d => x(d.date))
  .attr('cy', 100)
  .attr('r', d => d.minutesLate > 30 ? 6 : 3)
  .attr('fill', d => d.minutesLate > 30 ? '#ef4444' : '#f59e0b');

If your own timeline morphs into a full-blown Gantt view, I compared several free options in this Gantt deep-dive.

When would I use it again? Custom charts, special layouts, or anything the boss sketched on a napkin.


ECharts — Flashy and friendly

I used ECharts for an interactive map of store traffic. I liked the built-in themes. Tooltips felt rich. Panning was smooth. I even turned on their aria feature to help screen readers. I also borrowed a few tricks from a tiny trading dashboard I built—details here.

What I liked:

  • Polished look with very little setup.
  • Map, heatmap, candlestick, you name it.
  • Good for dashboards on TVs.

What bugged me:

  • Bundle felt bigger than I wanted.
  • Some config felt nested and long.

Real snippet that made a basic line chart with aria:

const chart = echarts.init(document.getElementById('visits'));
chart.setOption({
  aria: { enabled: true },
  xAxis: { type: 'category', data: ['Mon','Tue','Wed','Thu','Fri'] },
  yAxis: { type: 'value' },
  series: [{ type: 'line', data: [120, 200, 150, 80, 70], smooth: true }]
});

When would I use it again? Big screens, fancy charts, exec demos.


Highcharts — Board-room ready, but watch the license

I put Highcharts on a revenue trend page for a client who wanted “that smooth hover.” It looked premium. Export buttons just worked. Annotations were handy.

What I liked:

  • Great tooltips and zoom.
  • Export to PNG/PDF that didn’t fight me.
  • Strong docs.

What bugged me:

  • Needs a paid license for work use.
  • Custom styling can feel a bit strict.

When would I use it again? Corporate dashboards, reports that need printing, teams with budget.


Recharts (React) — Comfortable for React folks

For a React app, I used Recharts to show a funnel and some tiny spark lines (I also prototyped a spider/radar chart with plain JavaScript, but more on that in the separate write-up). The JSX felt natural. I could pass state right into the chart. Legends, grids, and tooltips were components, which felt nice.

What I liked:

  • Composable. Easy to read.
  • Works fine with React hooks and Suspense.

What bugged me:

  • Heavy data or many points can lag.
  • Some charts need extra tuning to look sharp.

Real snippet from my funnel:

<ResponsiveContainer width="100%" height={240}>
  <FunnelChart>
    <Tooltip />
    <Funnel dataKey="value" data={[
      { name: 'Visited', value: 1200 },
      { name: 'Signed Up', value: 350 },
      { name: 'Paid', value: 140 }
    ]} />
  </FunnelChart>
</ResponsiveContainer>

When would I use it again? React apps with “normal” datasets.


ApexCharts — Fast to pretty

I dropped ApexCharts into a product analytics page for area charts, radial bars, and even radar charts. Setup was fast. The hover felt crisp. Syncing multiple charts took minutes, not hours.

What I liked:

  • Good defaults. Bright but not loud.
  • Sync tooltips across charts with a small config.

What bugged me:

  • Styling beyond the basics took time.
  • Docs are fine, but I jumped to GitHub issues a few times.

When would I use it again? SaaS dashboards, KPIs, time series that need zoom.


Plotly.js — Heavy but powerful for data folk

I used Plotly for a small ML demo. Scatter plots, 3D stuff, even a box plot—and a bubble chart. It did a lot with no custom code. But it felt heavy.

What I liked:

  • Many chart types for stats work.
  • Built-in export and hover compare.

What bugged me:

  • Big bundle. My page slowed down.
  • Styling felt a bit rigid.

When would I use it again? Data science demos, notebooks, quick stats charts.


A quick story on speed and pain

I once tried to plot 120,000 points of sensor data. Chart.js crawled. Recharts stuttered. Plotly was not happy. D3 with WebGL scatter (through a small helper lib) finally worked

Published
Categorized as Hard Coding

I Tried a Bunch of Open-Source JavaScript Chart Tools — Here’s What Actually Worked

I make charts for a living thing, and also for fun. I build dashboards at my day job. I also help my friend’s bakery show daily orders on their site. So yeah, I spend time with charts. A lot.

If you’re hunting for more field notes, I recently tried a bunch of open-source JavaScript chart tools and wrote up everything that surprised me along the way.

Over the past year, I used Chart.js, D3.js, Apache ECharts, uPlot, Vega-Lite, Plotly.js, plus a bit of Recharts and Nivo in React projects. I’ll share what felt good, what made me grumpy, and real bits of code I wrote.

Quick note: I ran most tests on a MacBook Air M2 with Vite and TypeScript. But I also tried plain HTML when the use case was tiny. Coffee nearby, always.

What I Reached For, And Why

  • Chart.js: Straightforward bar, line, doughnut. Good docs. Good defaults.
  • D3.js: Full control for custom visuals. More work. Worth it when you need shapes or odd charts.
  • Apache ECharts: Fancy and glossy. Lots of chart types. Good for exec dashboards.
  • uPlot: Very fast line charts. Great for live data and lots of points.
  • Vega-Lite: You write a spec. It builds the chart. Great for quick reports.
  • Plotly.js: Built-in zoom, pan, and save to PNG. Nice for analysis.
  • Recharts / Nivo: If you’re in React, these save time. Open source too.

For a broader sweep of the landscape, here’s how it went when I tried 7 JavaScript chart libraries back-to-back in one weekend.

Honestly, I bounce between them. Like picking shoes. Depends on the day and the walk.

Real Projects I Shipped

1) Sales Dashboard at Work (Chart.js)

We needed a weekly sales view with targets. Nothing fancy, but it had to look clean in a slide deck. I used Chart.js with the annotation plugin for a target line. It took me one lunch break.

Small example I wrote for a line chart:

<canvas id="salesChart"></canvas>
<script type="module">
  import { Chart } from 'chart.js/auto';
  import annotationPlugin from 'chartjs-plugin-annotation';

  Chart.register(annotationPlugin);

  const ctx = document.getElementById('salesChart');
  new Chart(ctx, {
    type: 'line',
    data: {
      labels: ['Mon','Tue','Wed','Thu','Fri','Sat','Sun'],
      datasets: [{
        label: 'Sales',
        data: [120, 150, 180, 130, 170, 190, 210],
        borderColor: '#4f46e5',
        tension: 0.3,
        fill: false
      }]
    },
    options: {
      plugins: {
        annotation: {
          annotations: {
            target: {
              type: 'line',
              yMin: 160, yMax: 160,
              borderColor: '#ef4444',
              borderWidth: 2,
              label: { display: true, content: 'Target' }
            }
          }
        }
      }
    }
  });
</script>

Good: Fast to set up, looks tidy, colors behave.
Bad: Legends and labels can get cramped on small screens. Also, pie labels still bug me. I had to use a plugin for nice outside labels. If doughnut charts are on your mind, you might like seeing how I built 5 JavaScript donut charts and compared the quirks side by side.

2) Greenhouse Sensors, Live (uPlot)

My neighbor runs a small greenhouse. We stream temperature and humidity over WebSocket. With uPlot, the chart stayed smooth even with 50,000+ points. I tested updates at 1 second. It barely blinked.

Tiny setup I used:

<div id="u"></div>
<script type="module">
  import uPlot from "uplot";
  import "uplot/dist/uPlot.min.css";

  const data = [
    [], // x
    [], // temp
  ];

  const u = new uPlot({
    width: 600,
    height: 300,
    series: [
      {},
      { label: "Temp (°C)", stroke: "tomato" }
    ],
    axes: [
      { grid: { show: true } },
      { grid: { show: true } }
    ],
  }, data, document.getElementById("u"));

  // pretend stream
  let x = 0;
  setInterval(() => {
    data[0].push(x++);
    data[1].push(20 + Math.sin(x/10) * 3);
    u.setData(data);
  }, 1000);
</script>

Good: Tiny size and fast.
Bad: Docs are short. You’ll poke around a bit. But once it clicks, it flies.

If you’re looking at live feeds for trading dashboards, I wrote up what worked (and what crashed) when I built a tiny trading dashboard with real-time price data.

3) Beautiful Executive View (Apache ECharts)

I built a quarterly “wins and trends” screen for leadership. ECharts gave me tooltips, gradients, and smooth transitions right away. I used a theme and a bar-with-line overlay. The wow factor helped the story.

Good: Looks rich without much effort. Loads of chart types.
Bad: Bigger bundle. If your app is tight on size, you’ll notice.

4) Weird Shape, Full Control (D3.js)

A product manager wanted a custom “bump chart” with labels that dodge each other. D3 saved me. I wrote scales, axes, and little label rules. It took a weekend, plus a rainy Sunday. But the result? Chef’s kiss.

Here’s a tiny D3 bit I used to make a simple bar:

<svg id="chart" width="400" height="200"></svg>
<script type="module">
  import * as d3 from "d3";
  const data = [5, 8, 3, 10, 6];

  const svg = d3.select("#chart");
  const x = d3.scaleBand().domain(d3.range(data.length)).range([0, 400]).padding(0.2);
  const y = d3.scaleLinear().domain([0, d3.max(data)]).range([200, 0]);

  svg.selectAll("rect")
    .data(data)
    .join("rect")
    .attr("x", (_, i) => x(i))
    .attr("y", d => y(d))
    .attr("width", x.bandwidth())
    .attr("height", d => 200 - y(d))
    .attr("fill", "#10b981");
</script>

Good: You can make anything.
Bad: Time. Also, you need to manage labels, legends, colors, and a11y yourself. For a deeper dive into life with SVG and data joins, here’s what happened when I built charts with D3.js for a production feature.

5) Quick Classroom Report (Vega-Lite)

For my cousin’s science fair, we charted plant growth by day. I wrote a small Vega-Lite spec and used vega-embed. We tweaked colors, added a title, and printed it. We even set alt text and a caption.

Good: Fast from idea to chart. Clear JSON spec.
Bad: Styling outside the spec can feel funky.

6) Ad-Hoc Data Story (Plotly.js)

I had to show distribution and let folks zoom in. Plotly gave me histograms with zoom and a “save as PNG” button. No plugin hunt. It made stakeholder calls easier because they could poke at the data.

Good: Interactions out of the box.
Bad: Size is on the heavy side. And theming can feel different from your app style.

If your next report leans toward timelines and dependencies, you might enjoy my notes on how I tried free Gantt chart JavaScript libraries so you don’t have to wrestle each one yourself.

Side note: last month I helped a researcher visualize spikes in ephemeral photo-sharing among teens. Before we touched the data, we grounded ourselves in the topic by reading an eye-opening primer on snap-based sexting — Snap Sexting — which explains the term, the privacy risks, and the shifting cultural norms, giving you crucial context before you start charting that kind of behavior.

In a similar vein of location-specific, user-generated datasets

Published
Categorized as Hard Coding

My Hands-On Review: JavaScript Scatter Charts That Actually Worked For Me

I’m Kayla. I live in hoodies, coffee, and messy CSV files. Last month, I needed a scatter chart that told a simple story fast. I tried three tools in JavaScript: Chart.js, D3, and ECharts. I didn’t just read docs. I built real charts for work, with real data, on a real deadline. You know what? Each one had a mood. If you're curious about the full narrative behind that experiment, I put together my hands-on review of JavaScript scatter charts that actually worked for me with even more screenshots and code.


Chart.js — “I need something clean in 10 minutes”

I started with Chart.js because I needed a quick scatter for a sprint review. Steps vs calories from my team’s wellness challenge. Simple and neat. I later revisited Chart.js alongside a handful of other open-source projects in this deep dive into JavaScript chart tools that actually worked.

What I liked:

  • It was fast to set up.
  • Tooltips looked good right away.
  • I could add a trend line with a tiny helper.

What bugged me:

  • With thousands of points, it felt slow unless I tweaked it.
  • Styling small bits took more clicks than I wanted.

Real example I used (Steps vs Calories):

<canvas id="scatter" width="600" height="350"></canvas>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script>
// Tiny data sample (I had ~2,400 rows in real life)
const points = [
  { x: 3200, y: 220 }, { x: 5400, y: 310 }, { x: 8000, y: 420 },
  { x: 12000, y: 560 }, { x: 4000, y: 260 }, { x: 10000, y: 500 }
];

// Simple linear regression to draw a trend line
function linReg(data) {
  const n = data.length;
  const sx = data.reduce((s, d) => s + d.x, 0);
  const sy = data.reduce((s, d) => s + d.y, 0);
  const sxy = data.reduce((s, d) => s + d.x * d.y, 0);
  const sxx = data.reduce((s, d) => s + d.x * d.x, 0);
  const m = (n * sxy - sx * sy) / (n * sxx - sx * sx);
  const b = (sy - m * sx) / n;
  const xs = points.map(p => p.x);
  const minX = Math.min(...xs);
  const maxX = Math.max(...xs);
  return [
    { x: minX, y: m * minX + b },
    { x: maxX, y: m * maxX + b }
  ];
}

const ctx = document.getElementById('scatter').getContext('2d');
new Chart(ctx, {
  type: 'scatter',
  data: {
    datasets: [
      {
        label: 'Steps vs Calories',
        data: points,
        pointRadius: 3,
        backgroundColor: 'rgba(54,162,235,0.7)'
      },
      {
        type: 'line',
        label: 'Trend',
        data: linReg(points),
        borderColor: 'rgba(255,99,132,0.9)',
        borderWidth: 2,
        pointRadius: 0
      }
    ]
  },
  options: {
    parsing: false,
    plugins: {
      tooltip: {
        callbacks: {
          label: ctx => `Steps: ${ctx.parsed.x}, Cal: ${ctx.parsed.y}`
        }
      }
    },
    scales: {
      x: { title: { display: true, text: 'Steps' } },
      y: { title: { display: true, text: 'Calories' } }
    }
  }
});
</script>

A tiny tip: with bigger data (I had 10k points one day), I set pointRadius: 1, used parsing: false, and kept the canvas size steady. That kept it smooth enough to show my manager without panic.

Also, my cat walked on the keyboard while I was testing tooltips. No harm done. Good sign.


D3 — “I want full control, even if it takes time”

D3 felt like clay. It let me shape the scatter just the way I wanted. I used it for a marketing report: sessions vs conversion rate, colored by channel. I added jitter, a trend line, and custom axes. It took longer. But it looked like me, not a template. For the curious, I documented the parts that clicked—and the parts that didn't—in a dedicated write-up on building charts with D3.js.

What I liked:

  • Full control over scales, ticks, and colors.
  • Easy to add a custom trend line.
  • Great for teaching why a chart says what it says.

What bugged me:

  • More code, more care.
  • You own the little things (legends, tooltips).

Real example I shipped (Sessions vs Conversion Rate):

<div id="d3-scatter"></div>
<script src="https://cdn.jsdelivr.net/npm/d3@7"></script>
<script>
const data = [
  { x: 500,  y: 0.7, channel: 'Email' },
  { x: 1200, y: 1.2, channel: 'Search' },
  { x: 3000, y: 1.1, channel: 'Social' },
  { x: 800,  y: 0.6, channel: 'Email' },
  { x: 2200, y: 0.9, channel: 'Search' }
];

const w = 640, h = 420, m = { t: 20, r: 20, b: 40, l: 50 };
const svg = d3.select('#d3-scatter').append('svg')
  .attr('width', w).attr('height', h);

const x = d3.scaleLinear()
  .domain(d3.extent(data, d => d.x)).nice()
  .range([m.l, w - m.r]);

const y = d3.scaleLinear()
  .domain(d3.extent(data, d => d.y)).nice()
  .range([h - m.b, m.t]);

const color = d3.scaleOrdinal()
  .domain(['Email','Search','Social'])
  .range(['#1f77b4','#2ca02c','#ff7f0e']);

svg.append('g')
  .attr('transform', `translate(0,${h - m.b})`)
  .call(d3.axisBottom(x));

svg.append('g')
  .attr('transform', `translate(${m.l},0)`)
  .call(d3.axisLeft(y));

svg.selectAll('circle')
  .data(data)
  .enter().append('circle')
  .attr('cx', d => x(d.x))
  .attr('cy', d => y(d.y))
  .attr('r', 4)
  .attr('fill', d => color(d.channel))
  .attr('opacity', 0.7);

// Simple linear regression for trend line
function linReg(points) {
  const n = points.length;
  const sx = d3.sum(points, p => p.x);
  const sy = d3.sum(points, p => p.y);
  const sxy = d3.sum(points, p => p.x * p.y);
  const sxx = d3.sum(points, p => p.x * p.x);
  const m = (n * sxy - sx * sy) / (n * sxx - sx * sx);
  const b = (sy - m * sx) / n;
  const xs = d3.extent(points, p => p.x);
  return [{ x: xs[0], y: m * xs[0] + b }, { x: xs[1], y: m * xs[1] + b }];
}

const trend = linReg(data);
svg.append('line')
  .attr('x1', x(trend[0].x))
  .attr('y1', y(trend[0].y))
  .attr('x2', x(trend[1].x))
  .attr('y2', y(trend[1].y))
  .attr('stroke', '#d62728')
  .attr('stroke-width', 2);
</script>

I used tiny hover cards with title tags on circles for quick hints. Not fancy, but it worked during a live call. Honestly, the trend line stole the show.


ECharts — “Bring on the big data”

I reached for ECharts when I had 50k+ points from a log export. Time vs payload size. I needed pan and zoom that felt smooth. Also, I wanted brush select so my teammate could grab clusters like a lasso.

What I liked:

  • Big data felt okay with canvas and clever tricks.
  • Zoom, brush,
Published
Categorized as Hard Coding

I Tested 7 JavaScript Candlestick Charts. Here’s What Actually Worked.

Hi, I’m Kayla Sox. I code by day and track stocks at night. I built three small trading tools this year. And yes, I used real data. My coffee got cold more than once. I wrote a deeper blow-by-blow of that seven-chart shoot-out in this full testing diary.

I tested these charts on:

  • A 2019 MacBook Air, Chrome
  • A cheap Windows laptop, Edge
  • An iPhone 12 and a beat-up Moto G7

Data came from Binance (BTCUSDT, 1m and 5m), Polygon (AAPL, MSFT, daily), and a tiny Node server I wrote for candles. I tried 5k to 20k candles, fast zoom, pan, crosshair, and live ticks. I checked bundle size with Vite and devtools. Not perfect science, but close enough to feel real.

You know what? Some charts made me smile. Some made me sigh. Here’s the straight talk.

How I Tested (Quick and plain)

  • Built small pages with React and vanilla JS.
  • Used WebSocket updates (one tick per second, sometimes faster).
  • Added simple indicators: SMA(20), EMA(50), RSI(14).
  • Looked for smooth zoom, clear tooltips, clean candles, and no weird lag.
  • Tried both dark and light themes. Because glare hurts.

Now, onto the charts I actually used.

1) TradingView Lightweight Charts — My Top Pick

This one felt right the second I panned the chart. If you want to dig into the API yourself, the official docs for TradingView Lightweight Charts are refreshingly clear.

  • What I loved: buttery zoom; crisp crosshair; nice spacing; easy series overlays.
  • What bugged me: indicators aren’t built in, so I wrote my own lines.
  • Real test: live BTC ticks at 1s. 10k candles stayed smooth. CPU stayed calm on my Air.
  • Mobile: pinch zoom worked great; no random jumps.
  • Bundle: small and friendly for the build.
  • License: open and fine for most use cases.

I used it in a crypto bot dashboard. Added buy/sell markers with tiny triangles. No drama. It just worked.

2) Highcharts Stock — The “I Need Everything” Choice

When a client wants depth and polish, I reach for this. It looks rich out of the box. The demo gallery on Highcharts Stock shows just how many knobs you can turn.

  • What I loved: built-in range selector; great annotations; solid gaps for market hours.
  • What bugged me: license cost for commercial work; bundle felt heavier.
  • Real test: AAPL daily with gaps and compare mode. Smooth and clear.
  • Indicators: plenty. I stacked MACD and RSI under the chart like a champ.
  • Mobile: fine, but a little heavier on older phones.

I shipped a report page with it. Boss smiled. I slept well.

3) ApexCharts — Fast Start, Friendly API

It’s good when you want to ship fast with React and not fight the API.

  • What I loved: simple props; tooltips are easy; candlestick colors are clear.
  • What bugged me: very large data sets felt a bit heavy past 15k candles.
  • Real test: MSFT daily, 10-year span. A touch slow on zoom, but still okay.
  • Add-ons: heatmaps and bars play nice if you want extra flair.

I built a one-page tracker for a friend. We had it running before lunch. That mini trading dashboard journey is written up in this short case study.

4) ECharts — Free, Flexible, Powerful

ECharts can look stunning with the right config. It takes a little care, but it pays off.

  • What I loved: tons of control; great tooltip format; clean OHLC.
  • What bugged me: config can get wordy; you need to tune it for speed.
  • Real test: 20k candles with EMA lines. It held up with smart sampling.
  • Mobile: good, with smooth pan if you keep the data trimmed.

I used it for a weekend hack. Swapped between bars and candles with one state tweak. Felt slick. Earlier I also tried a bunch of open-source JavaScript chart tools and noted what actually worked in this roundup.

5) Chart.js + Financial Plugin — Good If You Already Use Chart.js

I like Chart.js for simple stuff. Candles work with a plugin. It’s fine for light loads.

  • What I loved: one ecosystem; clear docs; easy tooltips.
  • What bugged me: not the fastest with big sets; advanced stock tools are basic.
  • Real test: daily candles for a year. Fine. Five years? It started to drag.
  • Great for: school projects, quick demos, and dashboards that don’t need speed.

I put it in a school finance club site. They were happy. It was simple.

6) Plotly.js — Data Nerd Heaven, But Heavy

If you need deep data work, Plotly is strong. But it’s not tiny.

  • What I loved: subplots; linked axes; export as PNG; lots of chart types.
  • What bugged me: bundle weight; panning felt heavier with many candles.
  • Real test: multi-panel layout with RSI and volume. Looked sharp, lagged on phone.
  • Best for: research, static reports, and slides.

I used it for a quarterly report. Pretty charts, zero fuss on sharing.

7) Klinecharts — Small, Focused, Finance-First

This one is built for candles. It’s lean and quick.

  • What I loved: fast draw; nice finance features; clean API.
  • What bugged me: fewer extras; docs feel thinner than the big guys.
  • Real test: 1m BTC stream at 1s. Ran smooth on my old Android.
  • Best for: simple trading UIs that need speed and not much else.

I dropped it into a small WebSocket app. It kept up just fine.

My Real Picks (Short and sweet)

  • Best overall feel: TradingView Lightweight Charts
  • Best for enterprise features: Highcharts Stock
  • Best free power and control: ECharts
  • Easiest React start: ApexCharts
  • Best tiny, finance-first: Klinecharts
  • Best if you already have Chart.js: Chart.js + Financial plugin
  • Best for analytics and reports: Plotly.js

Honorable mention: EJSChart also provides a super-lightweight candlestick component that feels snappy even with live data streams.

Small Things That Matter (I learned the hard way)

  • Time zones: Test New York vs. UTC. Gaps can look strange if you skip this.
  • Tooltips: Snap to candle, not the mouse. It feels cleaner.
  • Volume overlay: Keep it subtle. Too much color kills the eyes.
  • Live updates: Batch ticks by second. Don’t redraw every millisecond.
  • Indicators: SMA and EMA are easy. RSI can slow things down if you’re sloppy.
  • Theme: Pick fonts that don’t wobble on mobile. I like simple sans fonts.

Here’s the thing: speed is nice, but feel matters more. If the crosshair jitters, traders get annoyed. If zoom jumps, folks lose trust. I saw both. I fixed both by trimming data and easing animations.

What I Use Today

  • For my crypto bot: TradingView Lightweight Charts with custom EMA and small markers.
  • For client dashboards: Highcharts Stock or ECharts, based on budget.
  • For quick tests and classes: ApexCharts or Chart.js with the plugin.

I still try new tools. Markets change. Browsers change. My taste changes too. But this list holds up. Last month I even lined up seven general-purpose JavaScript chart libraries to see which ones hold up, and you can read the results here.

If you’re unsure, start with Lightweight Charts. Add one indicator. Pan around. If it feels good, you’re set. If not, ECharts is a solid plan B.

Got a weird use case? Tell me. I’ve probably spilled coffee on that problem already.

Just like some JavaScript libraries mature gracefully with age and feature depth, there are whole communities that appreciate maturity in other areas of life as well. If your late-night coding breaks could use a different kind of seasoned perspective, check out Mature Women on FuckLocal. You’ll find verified profiles of confident, experienced women ready for authentic conversation and no-pressure meet-ups—perfect for unwinding after a marathon debugging session.

And if you happen to be coding (or just decompressing) in the Twin Cities and want an equally streamlined way to connect with locals, swing by the classifieds at MegaPersonals Minneapolis for quick, location-based listings that update in real time—think of it as applying the same fast, filter-friendly logic of a good chart API to finding a low

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Categorized as Hard Coding

I Built a JavaScript Organizational Chart Three Ways — Here’s What Actually Worked

I had to ship an org chart for our team portal last spring. HR wanted photos, titles, and those tricky dotted-line reports. Oh, and it had to print clean for a board deck. Classic, right?

If you want the blow-by-blow on this exact project, I wrote a companion piece that walks through the three approaches step by step — read the full story here.

I tried three routes: Google Charts OrgChart, OrgChart JS by Balkan, and a custom D3 build. If you’re open to a fourth path, the new EJS Chart library ships an org-chart component that slots nicely between quick demos and full-blown custom builds. I used all three on real data. About 620 people. Mixed managers. New hires every week. It got messy. You know what? That’s where you see what holds up.
For quick reference, the official Google chart documentation is available here, and the OrgChart JS product page lives here.

What I needed (and what bit me)

  • Fit 600+ nodes, with smooth zoom and pan
  • Collapse branches, but keep search fast
  • Show dotted-line (matrix) reports
  • Photos, titles, and a small badge for location
  • Export to PNG and PDF
  • Work on Chrome, Safari, iPad, and a stubborn Windows laptop in a kiosk

Now the fun part: how each tool behaved when I pushed it.

Google Charts OrgChart — fast start, small ceiling

Setup was easy. I had a chart on the page in 15 minutes. It looked clean. Collapsing worked. Search was okay with a simple list.

But I hit limits fast. Styling was tight. Dotted-line links? Not really. Printing was okay for a small team, but for 600 nodes, the layout got cramped and fuzzy.

What I liked

  • It’s free and simple
  • Great for small teams or a quick demo
  • Collapse and expand works out of the box

What bugged me

  • Hard to style cards (HTML allowed, but still fussy)
  • No clean dotted-line support
  • Slow with very large data

A tiny snippet I used:

<div id="org"></div>
<script>
  google.charts.load('current', {packages:['orgchart']});
  google.charts.setOnLoadCallback(drawChart);

  function drawChart() {
    const data = new google.visualization.DataTable();
    data.addColumn('string', 'Name');
    data.addColumn('string', 'Manager');
    data.addColumn('string', 'ToolTip');

    data.addRows([
      [{'v':'1', 'f':'Ava Lee<div style="color:#777">CEO</div>'}, '', ''],
      [{'v':'2', 'f':'Sam Patel<div>VP Sales</div>'}, '1', ''],
      [{'v':'3', 'f':'Rin Park<div>VP Eng</div>'}, '1', ''],
    ]);

    const chart = new google.visualization.OrgChart(document.getElementById('org'));
    chart.draw(data, {allowHtml: true, size: 'large'});
  }
</script>

It worked fine for a pilot. Not for my whole company.

For a broader look at how other open-source chart solutions fared in my tests, check out my separate roundup of contenders — I tried a bunch of open-source JavaScript chart tools, here’s what actually worked.

OrgChart JS (Balkan) — polished, paid, and practical

This one surprised me. It felt like it was built for busy folks like me. Templates, export buttons, drag and drop, and real-world stuff like assistant roles and partner lines.

We shipped this path first. It took me one day to set the base chart and two more days to tune badges, photos, and collapse rules. Export to PDF was crisp. It even handled our Hebrew labels right-to-left, which saved me a week.

What I liked

  • Looks great out of the box; templates like “olivia” and “ana”
  • Easy export to PNG/PDF
  • Built-in search is fast; collapse by level works
  • Dotted-line and assistant roles felt natural

What bugged me

  • It’s paid (not shocking, just a factor)
  • My Safari on macOS stuttered with 1,000+ nodes and huge photos
  • Custom link styles needed more tinkering than I hoped

Real snippet from my page:

<div id="tree" style="width:100%; height:700px;"></div>
<script src="orgchart.js"></script>
<script>
  const nodes = [
    { id: 1, name: "Ava Lee", title: "CEO", photo: "ava.jpg" },
    { id: 2, pid: 1, name: "Sam Patel", title: "VP Sales", photo: "sam.jpg" },
    { id: 3, pid: 1, name: "Rin Park", title: "VP Eng", photo: "rin.jpg" },
    // dotted-line relationship (reports to both Sales and Eng)
    { id: 22, pid: 2, name: "Mia Cho", title: "Ops", photo: "mia.jpg" },
    { id: 22_2, pid: 3, mid: 22 } // maps a second link to Mia
  ];

  const chart = new OrgChart(document.getElementById("tree"), {
    template: "olivia",
    mouseScrool: OrgChart.action.zoom,
    nodeBinding: {
      field_0: "name",
      field_1: "title",
      img_0: "photo"
    },
    collapse: { level: 2 },
    enableSearch: true,
    tags: {
      remote: { template: "olivia" }
    }
  });

  chart.load(nodes);

  // simple export button I wired up
  document.getElementById("export").onclick = () => chart.exportPNG();
</script>

That dotted-line trick (the extra entry with mid) was my “aha” moment. It looked tidy on screen and still printed well.

D3.js — full control, more work

I love D3 because I can bend it any way I want. And I did. I used d3.hierarchy and d3.tree, then added curved links. I even switched to canvas for a heavy view and kept SVG for labels. Super fast. Super custom.

But it took time. I wrote keyboard nav. I wrote a print layout. I wrote my own “search and reveal” logic. It felt good, but my team had a deadline, so we kept this build as a backup.

If you’re still shopping for visualization libraries, I also put seven of the most popular JavaScript chart libraries head-to-head — here’s what actually worked.

What I liked

  • Total control over style, links, and layout
  • Canvas mode flew with 2,000 nodes
  • Easy to shape custom badges and tooltips

What bugged me

  • More code, more testing
  • Printing took real work
  • Accessibility needed extra care

Here’s the tiny seed of my D3 build:

<svg id="chart" width="1200" height="800"></svg>
<script src="d3.v7.min.js"></script>
<script>
  const data = {
    name: "Ava Lee",
    children: [
      { name: "Sam Patel", children: [{ name: "Mia Cho" }] },
      { name: "Rin Park" }
    ]
  };

  const root = d3.hierarchy(data);
  const tree = d3.tree().nodeSize([100, 200])(root);

  const svg = d3.select("#chart");
  const g = svg.append("g").attr("transform", "translate(60,60)");

  g.selectAll(".link")
    .data(tree.links())
    .enter().append("path")
    .attr("class", "link")
    .attr("fill", "none")
    .attr("stroke", "#999")
    .attr("d", d3.linkHorizontal()
      .x(d => d.y)
      .y(d => d.x));

  const node = g.selectAll(".node")
    .data(tree.descendants())
    .enter().append("g")
    .attr("transform", d => `translate(${d.y},${d.x})`);

  node.append("rect")
    .attr("width", 150)
    .attr("height", 50)
    .attr("x", -75)
    .attr("y", -25)
    .attr("rx", 6)
    .attr("fill", "#fff")
    .attr("stroke", "#ccc");

  node.append("text")
    .attr("text-anchor", "middle")
    .attr("dy", "0.35em")
    .text(d => d.data.name);
</script>

Simple, clear, and yours to shape. But yes, it’s work.

Real week at work: the reorg crunch

We had a reorg drop on a Thursday. New VPs, a new PMO, and a dotted-line mess. I

Published
Categorized as Hard Coding

I Tried a Bunch of JavaScript Chart Libraries. Here’s What Actually Worked For Me.

I make dashboards for work. And sometimes for fun, like my kid’s school fundraiser page. I’ve used a bunch of chart libraries in JavaScript. Some saved my day. Some made me grumpy. Here’s the honest version, with real stuff I built and what went right (and wrong). If you’re after just the open-source angle, this write-up on OSS chart tools is a solid primer.

The quick take (so you don’t scroll forever)

  • Chart.js: Fast to set up. Great for small dashboards.
  • D3.js: Power tool. You can build anything, but you’ll sweat a little.
  • ECharts: Handles big data well. Nice zoom. Good on phones.
  • Highcharts: Polished and friendly for business folks. License for commercial use.
  • Recharts: Sweet with React. Ships quick. Good defaults.
  • ApexCharts: Clean stock charts. Easy mixed charts.
  • Plotly.js: Fancy science plots. Heavy bundle.
  • uPlot: Speed demon for huge time series. But very bare bones.

Looking for a shorter list? I once compared seven JavaScript chart libraries head-to-head.

One newcomer that impressed me is EJSchart, which slots somewhere between Chart.js and ECharts in terms of power-to-weight ratio.

Now, let me explain how I used each one.

Chart.js and the school fundraiser pie

I started with Chart.js (official site) for a simple school fundraiser site. We had three groups selling cookies. I made a pie (donut) chart and a line chart showing sales per day. Setup took me under 30 minutes.

What I liked:

  • The defaults look good right away.
  • Tooltips and legends just work.
  • It’s small and easy to add to a page.

What bugged me:

  • Custom labels took a bit of code.
  • Too many points made it choppy.

A tiny slice of how I set it up:

  • Type: "line"
  • Data: dates on the x-axis
  • Options: tension: 0.3 for smooth lines, responsive: true, maintainAspectRatio: false

Simple, right? It did the job. Parents could see the trend. And yes, chocolate chip won.

D3.js for a city budget map (I got picky)

I built a city budget map with D3.js (full teardown here) (official site) for a local council page. It was a choropleth (a color map) with a linked bar chart. When you hovered over a district on the map, the bars changed. Felt slick.

What I liked:

  • Total control. I tuned scales, axes, and transitions.
  • Smooth motion on hover. No jank.

What made me sigh:

  • It took time. Like, “late night coffee” time.
  • More code to maintain.
  • Accessibility needs extra care.

But hey, when you need custom stuff—like odd color breaks or weird labels—D3 lets you do it.

ECharts handled 60,000 points without crying

I had a power grid dashboard with 60,000 sensor points per series. Chart.js struggled. ECharts stayed smooth. Pinch-to-zoom on mobile felt great. I used data zoom, sampling, and a built-in dark theme.

What I liked:

  • Big data felt fine.
  • Built-in zoom, pan, and tooltip formatter.
  • Themes without fuss.

What I didn’t love:

  • The config can get long.
  • Bundle size is bigger than Chart.js.

If you’ve got lots of time series and want it to run well on phones, ECharts is a good bet. (I also toyed with JavaScript spider charts here—fun, but not for 60 k points!)

Highcharts for my finance team (they love exports)

Our finance folks needed clean line charts with data grouping, and they wanted one-click export to PDF and PNG. Highcharts nailed it. I wired up date pickers, added series toggles, and the charts looked “board ready.” For a deeper dive into finance dashboards, see how I built a tiny trading dashboard.

What I liked:

  • Built-in export that just works.
  • Good keyboard support and ARIA labels.
  • Data grouping helps with large date ranges.

Heads up:

  • License cost for commercial use.
  • Styling can feel a bit “Highcharts-y” unless you theme it.

But when execs say “Can we print this?” it’s nice to say yes.

Recharts for a React sprint (ship it fast)

I had a React app with a tight deadline. Recharts helped me ship in two days. I stacked bars, added custom tooltips, and used ResponsiveContainer so it looked good on any screen. No wrestling with refs. (I later swapped in radar charts for a KPI view—zero drama.)

What I liked:

  • Composable components (BarChart, Line, Tooltip, Legend).
  • Plays nice with React state.
  • Easy to test.

Tricky bits:

  • Heavy custom labels got messy.
  • Not as fast with very large data.

Still, for everyday product work in React, it’s a win.

ApexCharts for candlesticks and mixed charts

I built a small trading view: candlestick with volume bars below, plus a mini brush chart to zoom. ApexCharts felt made for this.

What I liked:

  • Candles look clean out of the box.
  • Mixed charts are simple.
  • Nice crosshairs and annotations.

Watch-outs:

  • Some docs pages felt light on edge cases.
  • Styling tiny details took hunting.

If you do finance charts, this one feels comfy.

Plotly.js for lab-style charts (3D and stats)

A research team needed a 3D scatter with hover labels and a density contour. Plotly.js did both, plus box plots, without me writing custom math. It felt like a Swiss Army knife. I also knocked out a quick bubble chart walkthrough while testing sizes and color scales—Plotly handled it fine.

What I liked:

  • Lots of chart types, especially for science.
  • Complex tooltips and hover modes.
  • Good for notebooks and quick prototypes.

Downside:

  • Big bundle. Slower loads.
  • The UI felt a bit heavy for small sites.

For research dashboards, it’s a strong pick.

uPlot when I had 1 million points (yes, a lot)

I tested a log viewer with 1 million points. Most libraries tapped out. uPlot was tiny and fast. But it’s bare. I had to wire my own legend, color scales, and downsampling.

What I liked:

  • Speed. Like, real speed.
  • Minimal footprint.

What I missed:

  • Fancy tooltips, themes, and helpers.
  • Built-in zoom polish.

Use it when speed is king and you can build the rest.

Performance notes from the trenches

  • SVG vs Canvas: For many points, Canvas wins. D3 SVG can lag with big sets.
  • Downsample on large time series. I use “min-max” buckets per pixel.
  • Lazy load charts. I load heavy libs only on pages that need them.
  • Avoid reflow storms. In React, memoize data and keep chart props stable.
  • Timelines your thing? Free Gantt chart libraries save a ton of custom work.

You know what? Sometimes the “boring” stuff—like cleaning data—matters more than the library.

If you’re pulling market or classifieds data to visualize—say, tracking how many listings appear in each city—having all the source URLs in one place saves hours of scraping prep; this exhaustive list of Craigslist sites gives you every city sub-domain in one shot, so you can automate the fetch and get straight to charting the trends.

Similarly, if your analysis zeroes in on adult classifieds traffic along the South Carolina coast, scraping the Myrtle Beach feed at Mega Personals Myrtle Beach lets you pull a focused, up-to-date dataset of posts you can pivot by date or category before feeding it into your charting layer.

Accessibility and touch, because people

Published
Categorized as Hard Coding

I Tried Making an ECG Chart with JavaScript. Here’s What Worked (and What Didn’t)

I build little health dashboards for clinics. I also teach kids to code on weekends. Funny mix, right? So I had to draw a live ECG line on a web page. Not a fake one. A real, moving line that keeps up with the heart. Smooth, fast, and clear.

If you’d like the full play-by-play of that initial experiment, you can read this deep-dive on building an ECG chart with JavaScript.


My Setup (real, messy, and honest)

  • Laptop: MacBook Air M2, 16 GB RAM
  • Browser: Chrome 127
  • Also tested on a cheap Windows mini PC and a Raspberry Pi 4
  • Data: 1-lead at 360 Hz and 500 Hz; then 12-lead at 500 Hz
  • Window size: 10 seconds on screen (so 5,000 points per lead at 500 Hz)

I also spilled coffee once and had to restart Chrome. That counts as “real life,” right?


What I Tested

  • Smoothie Charts (good for live streams)
  • uPlot (very fast, tiny, plain)
  • Chart.js (nice and friendly, but can get slow)
  • ECharts (feature rich; looks great)
  • Plotly (shiny; great for analysis and zoom)
  • D3 (roll-your-own; very flexible)
  • Raw Canvas (DIY; fastest if you keep it lean)
  • LightningChart JS (very fast; paid tier for big stuff)
  • JSCharting (robust commercial library; I didn’t benchmark it for ECG yet, but many teams swear by its real-time performance)

By the way, if you need a charting library purpose-built for ECG and other biosignals, take a look at EJsChart — it’s specialized for medical waveforms and trims away a lot of the plumbing.

I’ve used each of these in real projects. Some twice. Some only once. Some I still use every week.

I also published a broader rundown of the pros and cons when I tried a bunch of JavaScript chart libraries and noted what actually worked.


What I Needed the ECG Chart to Do

  • Keep 60 FPS when streaming 500 samples per second
  • Show 10 seconds of data with no jitters
  • Let me zoom and scroll when I pause
  • Handle 12 leads without my laptop begging for mercy
  • Look like a proper ECG: clean line, no jagged edges, steady baseline

Seems fair, right?


The Quick Winners

1) Smoothie Charts — Easiest Live Feed

I used Smoothie Charts to get a “it works!” demo in 10 minutes. It streams well and feels made for this.

What I liked:

  • Dead simple live line
  • Good on low power devices
  • Very little code

What bugged me:

  • Hard to show 12 leads without extra work
  • Not the best for zoom, pan, or fancy labels

Real code I used for a 1-lead feed at 500 Hz:

<canvas id="ecg" width="900" height="200"></canvas>
<script src="https://cdnjs.cloudflare.com/ajax/libs/smoothie/1.36.0/smoothie.min.js"></script>
<script>
  const canvas = document.getElementById('ecg');
  const chart = new SmoothieChart({
    millisPerPixel: 2,       // ~10s on 1000px
    grid: { strokeStyle: '#ddd', verticalSections: 10 },
    interpolation: 'linear',
    maxValue: 2.0, minValue: -2.0
  });
  chart.streamTo(canvas, /* delay */ 0);

  const line = new TimeSeries();
  chart.addTimeSeries(line, { strokeStyle: 'rgba(200,0,0,1)', lineWidth: 2 });

  // fake sample source: 500 Hz
  setInterval(() => {
    const now = Date.now();
    const sample = Math.sin(now / 15) * 0.4 + (Math.random() - 0.5) * 0.02;
    line.append(now, sample);
  }, 2);
</script>

On my MacBook, this held 60 FPS for 1 lead. On Raspberry Pi 4, it was still smooth.


2) uPlot — Best Raw Speed for Many Points

uPlot is plain Canvas and very fast. It uses low memory. It doesn’t hand-hold you. That’s fine by me.

What I liked:

  • Super fast for 10-second windows
  • Great for 12 leads if you draw them in small strips
  • Small library; loads quick

What bugged me:

  • You wire up streaming yourself
  • Zoom is there, but you’ll tweak it

Real code from my clinic viewer (1 lead stream):

<link rel="stylesheet" href="https://unpkg.com/uplot/dist/uPlot.min.css">
<div id="ecg"></div>
<script src="https://unpkg.com/uplot/dist/uPlot.iife.min.js"></script>
<script>
  const N = 5000; // 10s * 500Hz
  const t = new Array(N);
  const y = new Array(N).fill(0);
  const start = Date.now();

  for (let i = 0; i < N; i++) t[i] = i;

  const u = new uPlot({
    width: 900,
    height: 220,
    series: [
      {},
      { label: "Lead I", stroke: "red", width: 2 }
    ],
    axes: [
      { scale: "x", values: (u, ticks) => ticks.map(v => (v/500).toFixed(1) + "s") },
      { scale: "y", space: 40, values: (u, ticks) => ticks.map(v => v.toFixed(1) + " mV") }
    ],
    scales: { x: { time: false }, y: { auto: true } }
  }, [t, y], document.getElementById('ecg'));

  let idx = 0;
  function pushSample(sample) {
    y[idx % N] = sample;
    idx++;
    // rotate window logically without copying
    const off = idx % N;
    const tView = t.map((_, i) => i);
    const yView = y.slice(off).concat(y.slice(0, off));
    u.setData([tView, yView]);
  }

  function tick() {
    // fake 500 Hz
    for (let i = 0; i < 8; i++) { // batch a bit
      const now = Date.now() - start;
      const s = Math.sin(now / 15) * 0.4 + (Math.random() - 0.5) * 0.02;
      pushSample(s);
    }
    requestAnimationFrame(tick);
  }
  tick();
</script>

Note: For 12 leads, I made 12 small charts stacked, all sharing time. Still smooth.

That sprint was part of a head-to-head where I pitted seven different JavaScript chart libraries against each other to see which could really keep up.


The “Nice but Careful” Picks

Chart.js

Chart.js looks friendly and feels safe. For slow data it’s great. But at 500 Hz with long windows, it lagged for me.

  • 1 lead at 500 Hz: OK for short windows
  • 12 leads: it got choppy; redraws were heavy

Plotly

I love Plotly for zoom and explore time. For live ECG? It was heavy after a few minutes with many points. Nice for playback, though.

ECharts

Pretty, rich, feature packed. For streaming ECG, it felt heavier than uPlot and Smoothie. Still fine for 1–3 leads.

D3 (custom)

I built one with D3 Paths. It worked but took more time. I had to tune it a lot. I wouldn’t start here unless I need full control.

If you’re curious about that rabbit hole, here’s the write-up on what actually worked (and didn’t) when I built charts with D3.js.

LightningChart JS

This one flew. WebGL helps. It handled 12 leads like a champ on my laptop. But if you need full features, you’ll hit the paid tier. Worth it for bigger teams or long-term use.


Raw Canvas: Fast, But You Become The Library

When I needed rock-solid speed for 12 leads, I wrote a tiny Canvas renderer. It’s not pretty, but it runs fast and steady.

What I liked:

  • Fast even on weak machines
  • I control every pixel
  • Easy to keep garbage collection low

What I dealt with:

  • I had to manage buffers and time axes
  • More code to maintain

If you’re

Published
Categorized as Hard Coding

I Tried Free JavaScript Charts So You Don’t Have To

I make lots of tiny dashboards. Some for work notes. Some for home stuff, like tracking snack costs for my kid’s soccer team. Charts help me see what’s going on fast. If you’re curious about even more nitty-gritty details, I logged the whole saga in this separate deep-dive on free JavaScript chart libraries.

But which free chart tool actually feels good to use?

Here’s what I learned, with real examples you can copy.


My quick setup (so you know the vibe)

  • Data size: small arrays up to a few thousand points
  • Devices: old ThinkPad, an iPhone SE, and a cheap Android
  • Needs: fast start, tooltips, labels, and easy colors
  • Bonus: I like nice defaults. I don’t want to fuss all day

While we’re on the topic of quick, low-overhead tools, students collaborating on class dashboards often need an equally friction-free space to discuss their findings in real time. For that, consider InstantChat for College—it gives campus groups free, private chat rooms, file sharing, and instant notifications so your project team can iterate on charts without endless email threads.

Simple, right? Now the charts.


Chart.js — the easy friend

Chart.js (official site) feels friendly. It looks good out of the box. The docs are clear. I can make a clean bar chart in minutes. Chart.js also placed near the very top when I tried seven different JavaScript chart libraries side-by-side.

What I like:

  • Good defaults and legends
  • Smooth hover and tooltips
  • Plugins for stuff like data labels

What bugged me:

  • Custom shapes take extra work
  • The config can feel deep once you get fancy

Tiny example (a bar chart for monthly snacks):

<canvas id="snackBar"></canvas>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script>
const ctx = document.getElementById('snackBar');
new Chart(ctx, {
  type: 'bar',
  data: {
    labels: ['Jan','Feb','Mar','Apr'],
    datasets: [{
      label: 'Snack Spend ($)',
      data: [42, 31, 58, 36],
      backgroundColor: '#4e79a7'
    }]
  },
  options: { responsive: true, plugins: { legend: { display: true } } }
});
</script>

It just works. And sometimes that’s all you need.


Apache ECharts — flashy and loaded with stuff

ECharts (official docs) has range. Maps, heatmaps, gauges—lots. It runs smooth, even with many points. Great for “wow” charts. I first appreciated how flexible ECharts can be when I tested a bunch of open-source chart tools.

What I like:

  • Big feature set
  • Themes look sharp
  • Handles big data better than most

What bugged me:

  • The config object gets long
  • I still peek at docs a lot

One pleasant surprise: ECharts can even power project-timeline visuals like Gantt bars, which I unpack in this look at free Gantt chart libraries.

Line chart with a subtle gradient:

<div id="lineChart" style="height:300px"></div>
<script src="https://cdn.jsdelivr.net/npm/echarts/dist/echarts.min.js"></script>
<script>
const chart = echarts.init(document.getElementById('lineChart'));
chart.setOption({
  title: { text: 'Daily Steps' },
  xAxis: { type: 'category', data: ['Mon','Tue','Wed','Thu','Fri','Sat','Sun'] },
  yAxis: { type: 'value' },
  series: [{
    type: 'line',
    data: [5200, 6100, 6800, 7200, 4900, 8000, 7600],
    areaStyle: {}
  }],
  tooltip: { trigger: 'axis' }
});
</script>

It feels fast and glossy. Like a power suit, but for charts.


D3.js — total control, but you’ll work for it

D3 is a toolbox, not a chart kit. You get control over every mark. Every scale. Every tick. But it takes time. I use it when I need a custom look. My full field report on where D3 shines (and where it trips you up) lives over here: building charts with D3.js—what actually worked.

What I like:

  • Full control
  • Smart helpers (scales, axes)
  • Great for special charts

What bugged me:

  • More code for simple stuff
  • You handle layout and labels yourself

Simple bar chart:

<svg id="d3bar" width="400" height="220"></svg>
<script src="https://cdn.jsdelivr.net/npm/d3@7"></script>
<script>
const data = [42, 31, 58, 36];
const labels = ['Jan','Feb','Mar','Apr'];
const w=400, h=220, p=30;
const x = d3.scaleBand().domain(labels).range([p, w-p]).padding(0.2);
const y = d3.scaleLinear().domain([0, d3.max(data)]).nice().range([h-p, p]);

const svg = d3.select('#d3bar');
svg.append('g').attr('transform', `translate(0,${h-p})`).call(d3.axisBottom(x));
svg.append('g').attr('transform', `translate(${p},0)`).call(d3.axisLeft(y));

svg.selectAll('rect')
  .data(data).enter().append('rect')
  .attr('x', (_,i)=>x(labels[i]))
  .attr('y', d=>y(d))
  .attr('width', x.bandwidth())
  .attr('height', d=>h-p - y(d))
  .attr('fill', '#f28e2b');
</script>

If you want total freedom, this is it. Just bring patience. And if your data calls for shiny bubble visuals, here’s how that went for me in a real project—building bubble charts in JavaScript.


Recharts (for React folks) — props, props, props

If you live in React, Recharts feels natural. You pass props and get charts. It’s simple to read and easy to tweak.

What I like:

  • Composable parts
  • Clear props for common needs
  • Good for dashboards in React

What bugged me:

  • You’ll need React
  • Custom shapes still take time

Quick area chart:

import { AreaChart, Area, XAxis, YAxis, Tooltip, ResponsiveContainer } from 'recharts';

const data = [
  { month: 'Jan', sales: 12 },
  { month: 'Feb', sales: 18 },
  { month: 'Mar', sales: 25 },
  { month: 'Apr', sales: 20 }
];

export default function SalesArea() {
  return (
    <ResponsiveContainer width="100%" height={250}>
      <AreaChart data={data}>
        <XAxis dataKey="month" />
        <YAxis />
        <Tooltip />
        <Area dataKey="sales" stroke="#59a14f" fill="#59a14f" fillOpacity={0.3} />
      </AreaChart>
    </ResponsiveContainer>
  );
}

Feels like Lego blocks for charts.


ApexCharts — business-y and fast to ship

ApexCharts gives you nice touches right away. Crosshairs, annotations, sparkline style—handy for reports. It’s also one of my go-tos when I need finance-flavored visuals like candlesticks; you can see the blow-by-blow in this test of seven JavaScript candlestick chart options.

What I like:

  • Pretty out of the box
  • Annotations and markers are simple
  • Good interactions

What bugged me:

  • Defaults look a bit “business”—you may tweak fonts
  • Events are good, but deep custom shapes are limited

Basic area chart:

“`html

const options = {
chart: { type: ‘area’, height: 260 },
series: [{ name: ‘Orders’, data: [15, 22, 19, 31, 28, 35] }],
xaxis: { categories: [‘Jan’,’Feb’,’Mar’,’Apr’,’May’,’Jun’] },
dataLabels: { enabled: false },
stroke: { curve: ‘

Published
Categorized as Hard Coding

I Used Google Charts JavaScript API. Here’s My Take.

I built a small dashboard last week, then another for a client shop. Both ran on Google Charts. I’ll keep it real. It was fast, fun, and a little fussy in a few spots.
More details landed in a dedicated write-up that you can skim here: I Used Google Charts JavaScript API—Here’s My Take.

You know what? I liked it more than I thought I would.

The very short version

  • Good stuff: quick setup, lots of chart types, smooth tooltips, easy image export.
  • Not so great: needs Google’s loader online, big data can feel slow, “Material” charts miss options, dates can be tricky.

Why I tried it

I had a sales story to tell. A simple one: months, totals, and a few spikes that needed context. I didn’t want a giant build. I wanted a chart on screen in minutes. This did that.

Setup in minutes (for real)

Here’s how I got a line chart on screen. This is the exact code I used in my test page. If you want the official step-by-step, Google’s own Quick Start guide walks through the same process in just a few lines.

<!-- Include the loader once -->
<script src="https://www.gstatic.com/charts/loader.js"></script>

<div id="line-chart" style="height:320px;"></div>

<script>
  google.charts.load('current', { packages: ['corechart'] });
  google.charts.setOnLoadCallback(draw);

  function draw() {
    const data = google.visualization.arrayToDataTable([
      ['Month', 'Sales'],
      ['Jan', 1200],
      ['Feb', 1350],
      ['Mar', 1420],
      ['Apr', 980]
    ]);

    const options = {
      title: 'Shop Sales',
      curveType: 'function',
      legend: { position: 'bottom' },
      height: 300,
      colors: ['#1a73e8'],
      chartArea: { width: '80%', height: '70%' }
    };

    const el = document.getElementById('line-chart');
    const chart = new google.visualization.LineChart(el);
    chart.draw(data, options);
  }
</script>

It loaded fast. The default styles looked clean. No fuss. Nice.

Real project: sales dashboard for a coffee shop

I tracked monthly bean sales, daily drink counts, and a top-flavors pie. I mixed chart types, all on one page. For a quick visual overview of every chart Google offers, the Chart Gallery is a handy reference.
If your dashboard ever needs an org structure instead of sales numbers, see how I built a JavaScript organizational chart three different ways and what finally stuck.

  • A line chart for monthly sales
  • A column chart for daily drinks
  • A pie chart for flavors (vanilla won, by a lot)

Column chart with click events

I wanted to click a bar and show the number. So I hooked into “select.” Worked fine.

<div id="drinks" style="height:320px;"></div>
<script>
  google.charts.setOnLoadCallback(drawDrinks);

  function drawDrinks() {
    const data = google.visualization.arrayToDataTable([
      ['Day', 'Drinks'],
      ['Mon', 210],
      ['Tue', 235],
      ['Wed', 250],
      ['Thu', 195],
      ['Fri', 310]
    ]);

    const options = {
      title: 'Drinks per Day',
      height: 300,
      legend: 'none',
      colors: ['#ea4335']
    };

    const el = document.getElementById('drinks');
    const chart = new google.visualization.ColumnChart(el);
    chart.draw(data, options);

    google.visualization.events.addListener(chart, 'select', () => {
      const sel = chart.getSelection()[0];
      if (!sel) return;
      const day = data.getValue(sel.row, 0);
      const count = data.getValue(sel.row, 1);
      alert(`${day}: ${count}`);
    });
  }
</script>

Simple. It felt “clicky” in a good way.

Live data: I updated every 15 seconds

I pulled fresh numbers from my own API. I had to rebuild the DataTable, then redraw. It was smooth for small data.
For bigger timelines—think project plans instead of coffee orders—I recently tried free Gantt chart JavaScript libraries so you don’t have to.

<div id="live" style="height:320px;"></div>
<script>
  google.charts.setOnLoadCallback(startLive);

  function startLive() {
    const el = document.getElementById('live');
    const chart = new google.visualization.LineChart(el);
    const options = { title: 'Live Orders', legend: 'none', height: 300 };

    function fetchAndDraw() {
      fetch('/api/orders.json')
        .then(r => r.json())
        .then(rows => {
          // rows example: [['12:00', 5], ['12:05', 7], ...]
          const data = new google.visualization.DataTable();
          data.addColumn('string', 'Time');
          data.addColumn('number', 'Orders');
          data.addRows(rows);
          chart.draw(data, options);
        })
        .catch(console.error);
    }

    fetchAndDraw();
    setInterval(fetchAndDraw, 15000);
  }
</script>

Tip: don’t redraw every second. Your fans will spin. Your users will frown.

Resize quirks and my tiny fix

When I resized the window, the chart looked cramped. So I throttled redraws. This kept it snappy.

<script>
  function debounce(fn, wait) {
    let t;
    return function() {
      clearTimeout(t);
      t = setTimeout(fn, wait);
    };
  }

  // Example: redraw an existing chart with stored data/options
  // window.addEventListener('resize', debounce(() => chart.draw(data, options), 150));
</script>

I know, it’s a small thing. But it helps.

Better labels and money format

Price looks nicer with commas and a dollar sign. This took one line.
Speaking of finance, I also tested seven JavaScript candlestick charts to find which ones nail the tiny highs and lows.

<script>
  // After you build your DataTable:
  const formatter = new google.visualization.NumberFormat({
    prefix: '$',
    groupingSymbol: ',',
    fractionDigits: 0
  });
  // Format column index 1 (Sales)
  formatter.format(data, 1);
</script>

I also added annotations for quick read. People love tiny labels right on the bar.

const data = google.visualization.arrayToDataTable([
  ['Month', 'Sales', { role: 'annotation' }],
  ['Jan', 1200, '$1.2k'],
  ['Feb', 1350, '$1.35k'],
  ['Mar', 1420, '$1.42k']
]);

Export to PNG for reports

My client wanted a PDF report. I clicked a button and grabbed the image URI.

<button id="save">Save Chart Image</button>
<script>
  // After you create your chart:
  document.getElementById('save').addEventListener('click', () => {
    const uri = chart.getImageURI();
    // You can open it or send it to your backend
    window.open(uri, '_blank');
  });
</script>

Worked great for email and print.

Dates: a tiny gotcha that bit me

For time charts, you use Date objects. JavaScript months start at 0. January is 0. Yep.

const data = new google.visualization.DataTable();
data.addColumn('date', 'Day');
data.addColumn('number', 'Sales');

data.addRow([new Date(2024, 0, 1), 120]); // Jan 1, 2024
data.addRow([new Date(2024, 0, 2), 150]); // Jan 2, 2024

I messed this up once and laughed at myself later. It happens.

Material vs Classic: I switched back

Material charts look fresh. But they miss some options. I needed more control, like annotations and series tweaks. So I used the “Classic” ones:

  • Material: google.charts.Bar
  • Classic: google.visualization.ColumnChart

Classic had the knobs I needed. It felt safer for a custom dashboard.
If you’re shopping around, my roundup where I tried seven JavaScript chart libraries might save you an afternoon.

Performance notes from my screen

  • Under 2,000 rows? Fine. Smooth tooltips.
Published
Categorized as Hard Coding