Observable Framework 1.6.0 GitHub️ 1.8k

Getting started

For an introduction, see What is Framework?

Welcome! This tutorial will guide your first steps with Observable Framework by way of a hands-on exercise creating a dashboard of local weather. 🌦️

Framework is three things in one:

We’ll touch on each of these parts in this tutorial. It’ll go something like this:

First you’ll setup your local development environment by creating a project. Next you’ll develop: an iterative process where you save changes to source files in your editor while previewing the result in your browser. When you’re ready to share, it’s time to publish: you can either build a static site for self-hosting or deploy directly to Observable. Lastly, you can invite people to view your project!

These are just first steps. You can continue to develop projects after publishing, and republish as needed. You can also setup continuous deployment to publish your site automatically on commit or on schedule. We’ll cover these next steps briefly below.

1. Create

Framework includes a helper script (observable create) for creating new projects. After a few quick prompts — where to create the project, your preferred package manager, etc. — it will stamp out a fresh project from a template.

Framework is a Node.js application published to npm. You must have Node.js 18 or later installed before you can install Framework. Framework is a command-line interface (CLI) that runs in the terminal.

If you run into difficulty following this tutorial, we’re happy to help! Please visit the Observable forum or our GitHub discussions.

To create a new project with npm, run:

npm init "@observablehq"

If you prefer Yarn, run:

yarn create "@observablehq"

You can run the above command anywhere, but you may want to cd to your ~/Development directory first (or wherever you do local development).

This command will ask you a series of questions in order to initialize your new project. For this tutorial, you can simply hit Enter to accept the default values. When you’re done, you should see something like this:

   observable create 

  Welcome to Observable Framework! 👋 This command will help you create a new
  project. When prompted, you can press Enter to accept the default value.

  Want help? https://observablehq.com/framework/getting-started

  Where should we create your project?
  ./hello-framework

  What should we title your project?
  Hello Framework

  Include sample files to help you get started?
  Yes, include sample files

  Install dependencies?
  Yes, via yarn

  Initialize a git repository?
  Yes

  Installed! 🎉

  Next steps… ──────────╮
                        
  cd hello-framework    
  yarn dev              
                        
├────────────────────────╯

  Problems? https://github.com/observablehq/framework/discussions

And that’s it! Your new project is ready to go. 🎉

2. Develop

Next, cd into your new project folder.

cd hello-framework

Framework’s local development server lets you preview your site in the browser as you make rapid changes. The preview server generates pages on the fly: as you edit files in your editor, changes are instantly streamed to your browser.

To start the preview server using npm:

npm run dev

Or with Yarn:

yarn dev

You should see something like this:

Observable Framework v1.6.0
↳ http://127.0.0.1:3000/

If port 3000 is in use, the preview server will choose the next available port, so your actual port may vary. To specify port 4321 (and similarly for any other port), use --port 4321.

For security, the preview server is by default only accessible on your local machine using the loopback address 127.0.0.1. To allow remote connections, use --host 0.0.0.0.

Now visit http://127.0.0.1:3000 in your browser, which should look like:

The default home page (docs/index.md) after creating a new project.

Test live preview

Live preview means that as you save changes, your in-browser preview updates instantly. Live preview applies to Markdown pages, imported JavaScript modules (so-called hot module replacement), data loaders, and file attachments. This feature is implemented by the preview server watching files and pushing changes to the browser over a socket.

To experience live preview, open docs/index.md in your preferred text editor — below we show Visual Studio Code — and position your browser window so that you can see your editor and browser side-by-side. If you then replace the text “Hello, Observable Framework” with “Hi, Mom!” and save, you should see:

No seriously — hi, Mom! Thanks for supporting me all these years.

Create a new page

Now let’s add a page for our weather dashboard. Create a new file docs/weather.md and paste in the following snippet:

# Weather report

```js
display(1 + 2);
```

To see the new page in the sidebar, reload the page.

If you click on the Weather report link in the sidebar, it’ll take you to http://127.0.0.1:3000/weather, where you should see:

The humble beginnings of a local weather dashboard.
The sidebar is hidden by default in narrow windows. If you don’t see the sidebar, you can show it by making the window wider, or using Command-B (⌘B) or Option-B (⌥B) on Firefox and non-macOS, or clicking the right-pointing arrow ↦ on the left edge of the window.

As evidenced by the code 1 + 2 rendered as 3, JavaScript fenced code blocks (```js) are live: the code runs in the browser. Try replacing 2 with Math.random(), and the code will re-run automatically on save. In a bit, we’ll write code to render a chart. We can also use code to debug as we develop, say to inspect data.

Data loader

Next, let’s load some data. The National Weather Service (NWS) provides an excellent and free API for local weather data within the United States. We’ll use the /points/{latitude},{longitude} endpoint to get metadata for the closest grid point to the given location, and then fetch the corresponding hourly forecast.

Create a new file docs/data/forecast.json.js and paste in the following snippet:

const longitude = ;
const latitude = ;

async function json(url) {
  const response = await fetch(url);
  if (!response.ok) throw new Error(`fetch failed: ${response.status}`);
  return await response.json();
}

const station = await json(`https://api.weather.gov/points/${latitude},${longitude}`);
const forecast = await json(station.properties.forecastHourly);

process.stdout.write(JSON.stringify(forecast));

To personalize this code snippet to your current location, edit the longitude and latitude values above, or click the Locate me button above.

NWS does not provide forecasts for points outside the United States. If you specify such a location the API will error and the data loader will fail.
If you would rather write your data loader in Python, R, or some other language, take a peek at the next steps below before continuing.

Your data loader should look like this:

A JavaScript data loader for fetching a local forecast from weather.gov.

If you like, you can run your data loader manually in the terminal:

node docs/data/forecast.json.js

If this barfs a bunch of JSON in the terminal, it’s working as intended. 😅 Normally you don’t run data loaders by hand — Framework runs them automatically, as needed — but data loaders are “just” programs so you can run them manually if you want. Conversely, any executable or shell script that runs on your machine and outputs something to stdout can be a data loader!

File attachments

Framework uses file-based routing for data loaders: the data loader forecast.json.js serves the file forecast.json. To load this file from docs/weather.md we use the relative path ./data/forecast.json. In effect, data loaders are simply a naming convention for generating “static” files — a big advantage of which is that you can edit a data loader and the changes immediately propagate to the live preview without needing a reload.

To load a file in JavaScript, use the built-in FileAttachment. In weather.md, replace the contents of the JavaScript code block (the parts inside the triple backticks ```) with the following code:

const forecast = FileAttachment("./data/forecast.json").json();
FileAttachment is a special function that can only be passed a static string literal as an argument. This restriction enables static analysis, allowing Framework to determine which data loaders to run on build and improving security by only including referenced files in the published site.

You can now reference the variable forecast from other code. For example, you can add another code block that displays the forecast data.

```js
display(forecast);
```

This looks like:

Using FileAttachment to load data.

The built-in display function displays the specified value, a bit like console.log in the browser’s console. As you can see below, display is called implicitly when a code block contains an expression:

1 + 2

For convenience, here’s a copy of the data so you can explore it here:

This is a GeoJSON Feature object of a Polygon geometry representing the grid square. The properties object within contains the hourly forecast data. You can display it on a map with Leaflet, if you like.

This grid point covers the south end of the Golden Gate Bridge.

Plots

Now let’s add a chart using Observable Plot. Framework includes a variety of recommended libraries by default, including Plot, and you can always import more from npm. Replace the display(forecast) code block with the following code:

display(
  Plot.plot({
    title: "Hourly temperature forecast",
    x: {type: "utc", ticks: "day", label: null},
    y: {grid: true, inset: 10, label: "Degrees (F)"},
    marks: [
      Plot.lineY(forecast.properties.periods, {
        x: "startTime",
        y: "temperature",
        z: null, // varying color, not series
        stroke: "temperature",
        curve: "step-after"
      })
    ]
  })
);
Because this is JSON data, startTime is a string rather than a Date. Setting the type of the x scale to utc tells Plot to interpret these values as temporal rather than ordinal.

You should now see:

Using Plot to make a chart.
Try editing forecast.json.js to change the longitude and latitude to a different location! After you save, Framework will run the data loader again and push the new data to the client to update the chart. For example, to see the current forecast at the White House:
const longitude = -77.04;
const latitude = 38.90;

As before, the code block contains an expression (a call to Plot.plot) and hence display is called implicitly. And since this expression evaluates to a DOM element (a <figure> containing an <svg>), display inserts the element directly into the page. We didn’t have to touch the DOM API!

Components

As pages grow, complex inline JavaScript may become unwieldy and repetitive. Tidy code by moving it into functions. In Framework, a function that returns a DOM element is called a component.

To turn the chart above into a component, wrap it in a function and promote the data to a required argument. Accept any named options (such as width) as an optional second argument with destructuring.

function temperaturePlot(data, {width} = {}) {
  return Plot.plot({
    title: "Hourly temperature forecast",
    width,
    x: {type: "utc", ticks: "day", label: null},
    y: {grid: true, inset: 10, label: "Degrees (F)"},
    marks: [
      Plot.lineY(data.properties.periods, {
        x: "startTime",
        y: "temperature",
        z: null, // varying color, not series
        stroke: "temperature",
        curve: "step-after"
      })
    ]
  });
}

Now you can call temperaturePlot to display the forecast anywhere on the page:

display(temperaturePlot(forecast));
JavaScript can be extracted into standalone modules (.js files) that you can import into Markdown. This lets you share code across pages, write unit tests for components, and more.

Layout

Let’s put some finishing touches on and wrap up this tutorial.

While this nascent dashboard only has a single chart on it, most dashboards will have many charts, tables, values, and other elements. To assist layout, Framework includes simple grid and card CSS classes with 1, 2, 3, or 4 columns. (You can write more elaborate custom styles if needed, or load your preferred CSS framework.)

For example, here’s a two-column grid with three cards:

<div class="grid grid-cols-2">
  <div class="card grid-colspan-2">one–two</div>
  <div class="card">three</div>
  <div class="card">four</div>
</div>
Framework’s grid is responsive: on narrow windows, the two-column grid will automatically collapse to a one-column grid. Cells in a grid have the same height by default (using grid-auto-rows), so consider separate <div class="grid"> containers if you want to vary row height.

When placing charts in a grid, you typically want to render responsively based on the width (and sometimes height) of the containing cell. Framework’s resize helper takes a render function returning a DOM element and re-renders whenever the container resizes. It looks like this:

<div class="grid grid-cols-1">
  <div class="card">${resize((width) => temperaturePlot(forecast, {width}))}</div>
</div>

Lastly, let’s apply the dashboard theme and disable the table of contents (toc) using YAML front matter. The dashboard theme allows the main column to span the full width of the window; without it, the main column width is limited to 1152px as appropriate for documentation or a report.

---
theme: dashboard
toc: false
---
Adopting a grid layout and the dashboard theme.

Ta-da! 🎉 Perhaps not the most exciting dashboard yet, but it has potential! Try exploring other data in the NWS forecast and adding more charts. For example, you could visualize precipitation probability.

3. Publish

When you’re ready to share your project — either privately or publicly — you can quickly deploy it to Observable using the deploy command:

npm run deploy

Or with Yarn:

yarn deploy
If you don’t have an Observable account yet, you will be prompted to sign up. Observable is free for individuals and small teams, and we offer paid tiers for larger teams.

This command will ask you a few questions to configure your deploy, including which Observable workspace to use and whether the project should be public or private. You can also enter an optional message to associate with the deploy, but for now feel free to leave this blank by hitting Enter.

When deploy completes, Framework will show your project’s URL on observablehq.cloud, like below. From there you can invite people to your private workspace to see your project, or make your project public so anyone can see it.

   observable deploy 

  To configure deploy, we need to ask you a few questions.

  Which Observable workspace do you want to use?
│  Example Inc. (@example)

  Which project do you want to use?
│  Create a new project

  What slug do you want to use?
│  hello-framework

  Who is allowed to access your project?
│  Private

  What changed in this deploy?
  Enter a deploy message (optional)

  18 uploaded

  Deploy complete

  Deployed project now visible at https://example.observablehq.cloud/hello-framework/
Your deploy configuration is saved to docs/.observablehq/deploy.json. When collaborating on a project, you should commit this file to git so your collaborators don’t have to separately configure deploy.

Self hosting

Of course, you don’t have to deploy to Observable — Framework projects are simply static sites, so you can host them anywhere!

To build your static site with npm, run:

npm run build

Or with Yarn:

yarn build

The build command generates the dist directory; you can then copy this directory to your static site server or preferred hosting service. To preview your built site locally, you can use a local static HTTP server such as http-server:

npx http-server dist

Next steps

Here are a few more tips.

Write a data loader in Python, R, or other language

We coded exclusively in JavaScript for this tutorial, but you can write data loaders in any language — not just JavaScript. Here’s a forecast.json.py you could use in place of the JavaScript data loader above:

import json
import requests
import sys

longitude = -122.47
latitude = 37.80

station = requests.get(f"https://api.weather.gov/points/{latitude},{longitude}").json()
forecast = requests.get(station["properties"]["forecastHourly"]).json()

json.dump(forecast, sys.stdout)

To write the data loader in R, name it forecast.json.R. Or as shell script, forecast.json.sh. You get the idea. See Data loaders: Routing for more. The beauty of this approach is that you can leverage the strengths (and libraries) of multiple languages, and still get instant updates in the browser as you develop.

Deploying automatically

You can schedule builds and deploy your project automatically on commit, or on a schedule. See deploying for more details.

Ask for help, or share your feedback

Please reach out if you have questions or thoughts! You can post on the Observable forum, start a GitHub discussion, or file a GitHub issue. And if you like Framework, please give us a star ⭐️ on GitHub — we appreciate your support. 🙏