merge: First tutorial + Example (#804)

## Changes:

- /docs is now a Getting Started page with the first tutorial
- basic-example added
This commit is contained in:
João Demenech 2023-04-27 14:55:54 -03:00 committed by GitHub
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commit ad209c8f21
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{
"extends": "next/core-web-vitals"
}

36
examples/basic-example/.gitignore vendored Normal file
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# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.js
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.pnpm-debug.log*
# local env files
.env*.local
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts

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This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).
## Getting Started
First, run the development server:
```bash
npm run dev
# or
yarn dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
You can start editing the page by modifying `pages/index.tsx`. The page auto-updates as you edit the file.
[API routes](https://nextjs.org/docs/api-routes/introduction) can be accessed on [http://localhost:3000/api/hello](http://localhost:3000/api/hello). This endpoint can be edited in `pages/api/hello.ts`.
The `pages/api` directory is mapped to `/api/*`. Files in this directory are treated as [API routes](https://nextjs.org/docs/api-routes/introduction) instead of React pages.
## Learn More
To learn more about Next.js, take a look at the following resources:
- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
## Deploy on Vercel
The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.

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import { MDXRemote } from 'next-mdx-remote';
import dynamic from 'next/dynamic';
import { Mermaid } from '@flowershow/core';
// Custom components/renderers to pass to MDX.
// Since the MDX files aren't loaded by webpack, they have no knowledge of how
// to handle import statements. Instead, you must include components in scope
// here.
const components = {
Table: dynamic(() => import('./Table')),
mermaid: Mermaid,
// Excel: dynamic(() => import('../components/Excel')),
// TODO: try and make these dynamic ...
Vega: dynamic(() => import('./Vega')),
VegaLite: dynamic(() => import('./VegaLite')),
LineChart: dynamic(() => import('./LineChart')),
} as any;
export default function DRD({ source }: { source: any }) {
return <MDXRemote {...source} components={components} />;
}

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import { useEffect, useState } from "react";
const DebouncedInput = ({
value: initialValue,
onChange,
debounce = 500,
...props
}) => {
const [value, setValue] = useState(initialValue);
useEffect(() => {
setValue(initialValue);
}, [initialValue]);
useEffect(() => {
const timeout = setTimeout(() => {
onChange(value);
}, debounce);
return () => clearTimeout(timeout);
}, [value]);
return (
<input
{...props}
value={value}
onChange={(e) => setValue(e.target.value)}
/>
);
};
export default DebouncedInput;

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import VegaLite from "./VegaLite";
export default function LineChart({
data = [],
fullWidth = false,
title = "",
}) {
var tmp = data;
if (Array.isArray(data)) {
tmp = data.map((r, i) => {
return { x: r[0], y: r[1] };
});
}
const vegaData = { table: tmp };
const spec = {
$schema: "https://vega.github.io/schema/vega-lite/v5.json",
title,
width: 500,
height: 300,
mark: {
type: "line",
color: "black",
strokeWidth: 1,
tooltip: true,
},
data: {
name: "table",
},
selection: {
grid: {
type: "interval",
bind: "scales",
},
},
encoding: {
x: {
field: "x",
timeUnit: "year",
type: "temporal",
},
y: {
field: "y",
type: "quantitative",
},
},
};
return <VegaLite fullWidth={fullWidth} data={vegaData} spec={spec} />;
}

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import {
createColumnHelper,
FilterFn,
flexRender,
getCoreRowModel,
getFilteredRowModel,
getPaginationRowModel,
getSortedRowModel,
useReactTable,
} from "@tanstack/react-table";
import {
ArrowDownIcon,
ArrowUpIcon,
ChevronDoubleLeftIcon,
ChevronDoubleRightIcon,
ChevronLeftIcon,
ChevronRightIcon,
} from "@heroicons/react/24/solid";
import React, { useEffect, useMemo, useState } from "react";
import parseCsv from "../lib/parseCsv";
import DebouncedInput from "./DebouncedInput";
import loadData from "../lib/loadData";
const Table = ({
data: ogData = [],
cols: ogCols = [],
csv = "",
url = "",
fullWidth = false,
}) => {
if (csv) {
const out = parseCsv(csv);
ogData = out.rows;
ogCols = out.fields;
}
const [data, setData] = React.useState(ogData);
const [cols, setCols] = React.useState(ogCols);
const [error, setError] = React.useState(""); // TODO: add error handling
const tableCols = useMemo(() => {
const columnHelper = createColumnHelper();
return cols.map((c) =>
columnHelper.accessor(c.key, {
header: () => c.name,
cell: (info) => info.getValue(),
})
);
}, [data, cols]);
const [globalFilter, setGlobalFilter] = useState("");
const table = useReactTable({
data,
columns: tableCols,
getCoreRowModel: getCoreRowModel(),
state: {
globalFilter,
},
globalFilterFn: globalFilterFn,
onGlobalFilterChange: setGlobalFilter,
getFilteredRowModel: getFilteredRowModel(),
getPaginationRowModel: getPaginationRowModel(),
getSortedRowModel: getSortedRowModel(),
});
useEffect(() => {
if (url) {
loadData(url).then((data) => {
const { rows, fields } = parseCsv(data);
setData(rows);
setCols(fields);
});
}
}, [url]);
return (
<div className={`${fullWidth ? "w-[90vw] ml-[calc(50%-45vw)]" : "w-full"}`}>
<DebouncedInput
value={globalFilter ?? ""}
onChange={(value) => setGlobalFilter(String(value))}
className="p-2 text-sm shadow border border-block"
placeholder="Search all columns..."
/>
<table>
<thead>
{table.getHeaderGroups().map((hg) => (
<tr key={hg.id}>
{hg.headers.map((h) => (
<th key={h.id}>
<div
{...{
className: h.column.getCanSort()
? "cursor-pointer select-none"
: "",
onClick: h.column.getToggleSortingHandler(),
}}
>
{flexRender(h.column.columnDef.header, h.getContext())}
{{
asc: (
<ArrowUpIcon className="inline-block ml-2 h-4 w-4" />
),
desc: (
<ArrowDownIcon className="inline-block ml-2 h-4 w-4" />
),
}[h.column.getIsSorted() as string] ?? (
<div className="inline-block ml-2 h-4 w-4" />
)}
</div>
</th>
))}
</tr>
))}
</thead>
<tbody>
{table.getRowModel().rows.map((r) => (
<tr key={r.id}>
{r.getVisibleCells().map((c) => (
<td key={c.id}>
{flexRender(c.column.columnDef.cell, c.getContext())}
</td>
))}
</tr>
))}
</tbody>
</table>
<div className="flex gap-2 items-center justify-center">
<button
className={`w-6 h-6 ${
!table.getCanPreviousPage() ? "opacity-25" : "opacity-100"
}`}
onClick={() => table.setPageIndex(0)}
disabled={!table.getCanPreviousPage()}
>
<ChevronDoubleLeftIcon />
</button>
<button
className={`w-6 h-6 ${
!table.getCanPreviousPage() ? "opacity-25" : "opacity-100"
}`}
onClick={() => table.previousPage()}
disabled={!table.getCanPreviousPage()}
>
<ChevronLeftIcon />
</button>
<span className="flex items-center gap-1">
<div>Page</div>
<strong>
{table.getState().pagination.pageIndex + 1} of{" "}
{table.getPageCount()}
</strong>
</span>
<button
className={`w-6 h-6 ${
!table.getCanNextPage() ? "opacity-25" : "opacity-100"
}`}
onClick={() => table.nextPage()}
disabled={!table.getCanNextPage()}
>
<ChevronRightIcon />
</button>
<button
className={`w-6 h-6 ${
!table.getCanNextPage() ? "opacity-25" : "opacity-100"
}`}
onClick={() => table.setPageIndex(table.getPageCount() - 1)}
disabled={!table.getCanNextPage()}
>
<ChevronDoubleRightIcon />
</button>
</div>
</div>
);
};
const globalFilterFn: FilterFn<any> = (row, columnId, filterValue: string) => {
const search = filterValue.toLowerCase();
let value = row.getValue(columnId) as string;
if (typeof value === "number") value = String(value);
return value?.toLowerCase().includes(search);
};
export default Table;

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// Wrapper for the Vega component
import { Vega as VegaOg } from "react-vega";
export default function Vega(props) {
return <VegaOg {...props} />;
}

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// Wrapper for the Vega Lite component
import { VegaLite as VegaLiteOg } from "react-vega";
export default function VegaLite(props) {
return <VegaLiteOg {...props} />;
}

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# Data
This is the README.md this project.
## Table
<Table url="data_1.csv" />
## Vega Lite Line Chart from URL
<VegaLite spec={ { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "data": {"url": "data_2.csv"}, "width": 550, "height": 250, "mark": "line", "encoding": { "x": {"field": "Time", "type": "temporal"}, "y": {"field": "Anomaly (deg C)", "type": "quantitative"}, "tooltip": {"field": "Anomaly (deg C)", "type": "quantitative"} } } } />

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export default async function loadData(url: string) {
const response = await fetch(url)
const data = await response.text()
return data
}

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import matter from "gray-matter";
import mdxmermaid from "mdx-mermaid";
import { h } from "hastscript";
import remarkCallouts from "@flowershow/remark-callouts";
import remarkEmbed from "@flowershow/remark-embed";
import remarkGfm from "remark-gfm";
import remarkMath from "remark-math";
import remarkSmartypants from "remark-smartypants";
import remarkToc from "remark-toc";
import remarkWikiLink from "@flowershow/remark-wiki-link";
import rehypeAutolinkHeadings from "rehype-autolink-headings";
import rehypeKatex from "rehype-katex";
import rehypeSlug from "rehype-slug";
import rehypePrismPlus from "rehype-prism-plus";
import { serialize } from "next-mdx-remote/serialize";
/**
* Parse a markdown or MDX file to an MDX source form + front matter data
*
* @source: the contents of a markdown or mdx file
* @format: used to indicate to next-mdx-remote which format to use (md or mdx)
* @returns: { mdxSource: mdxSource, frontMatter: ...}
*/
const parse = async function (source, format) {
const { content, data, excerpt } = matter(source, {
excerpt: (file, options) => {
// Generate an excerpt for the file
file.excerpt = file.content.split("\n\n")[0];
},
});
const mdxSource = await serialize(
{ value: content, path: format },
{
// Optionally pass remark/rehype plugins
mdxOptions: {
remarkPlugins: [
remarkEmbed,
remarkGfm,
[remarkSmartypants, { quotes: false, dashes: "oldschool" }],
remarkMath,
remarkCallouts,
remarkWikiLink,
[
remarkToc,
{
heading: "Table of contents",
tight: true,
},
],
[mdxmermaid, {}],
],
rehypePlugins: [
rehypeSlug,
[
rehypeAutolinkHeadings,
{
properties: { className: 'heading-link' },
test(element) {
return (
["h2", "h3", "h4", "h5", "h6"].includes(element.tagName) &&
element.properties?.id !== "table-of-contents" &&
element.properties?.className !== "blockquote-heading"
);
},
content() {
return [
h(
"svg",
{
xmlns: "http:www.w3.org/2000/svg",
fill: "#ab2b65",
viewBox: "0 0 20 20",
className: "w-5 h-5",
},
[
h("path", {
fillRule: "evenodd",
clipRule: "evenodd",
d: "M9.493 2.853a.75.75 0 00-1.486-.205L7.545 6H4.198a.75.75 0 000 1.5h3.14l-.69 5H3.302a.75.75 0 000 1.5h3.14l-.435 3.148a.75.75 0 001.486.205L7.955 14h2.986l-.434 3.148a.75.75 0 001.486.205L12.456 14h3.346a.75.75 0 000-1.5h-3.14l.69-5h3.346a.75.75 0 000-1.5h-3.14l.435-3.147a.75.75 0 00-1.486-.205L12.045 6H9.059l.434-3.147zM8.852 7.5l-.69 5h2.986l.69-5H8.852z",
}),
]
),
];
},
},
],
[rehypeKatex, { output: "mathml" }],
[rehypePrismPlus, { ignoreMissing: true }],
],
format,
},
scope: data,
}
);
return {
mdxSource: mdxSource,
frontMatter: data,
excerpt,
};
};
export default parse;

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import papa from "papaparse";
const parseCsv = (csv) => {
csv = csv.trim();
const rawdata = papa.parse(csv, { header: true });
const cols = rawdata.meta.fields.map((r, i) => {
return { key: r, name: r };
});
return {
rows: rawdata.data,
fields: cols,
};
};
export default parseCsv;

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/** @type {import('next').NextConfig} */
const nextConfig = {
reactStrictMode: true,
swcMinify: true,
}
module.exports = nextConfig

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{
"name": "basic-example",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint"
},
"dependencies": {
"@flowershow/core": "^0.4.10",
"@flowershow/remark-callouts": "^1.0.0",
"@flowershow/remark-embed": "^1.0.0",
"@flowershow/remark-wiki-link": "^1.1.2",
"@heroicons/react": "^2.0.17",
"@opentelemetry/api": "^1.4.0",
"@tanstack/react-table": "^8.8.5",
"@types/node": "18.16.0",
"@types/react": "18.2.0",
"@types/react-dom": "18.2.0",
"eslint": "8.39.0",
"eslint-config-next": "13.3.1",
"gray-matter": "^4.0.3",
"hastscript": "^7.2.0",
"mdx-mermaid": "2.0.0-rc7",
"next": "13.2.1",
"next-mdx-remote": "^4.4.1",
"papaparse": "^5.4.1",
"react": "18.2.0",
"react-dom": "18.2.0",
"react-vega": "^7.6.0",
"rehype-autolink-headings": "^6.1.1",
"rehype-katex": "^6.0.3",
"rehype-prism-plus": "^1.5.1",
"rehype-slug": "^5.1.0",
"remark-gfm": "^3.0.1",
"remark-math": "^5.1.1",
"remark-smartypants": "^2.0.0",
"remark-toc": "^8.0.1",
"typescript": "5.0.4"
},
"devDependencies": {
"@tailwindcss/typography": "^0.5.9",
"autoprefixer": "^10.4.14",
"postcss": "^8.4.23",
"tailwindcss": "^3.3.1"
}
}

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import '../styles/globals.css'
import type { AppProps } from 'next/app'
export default function App({ Component, pageProps }: AppProps) {
return <Component {...pageProps} />
}

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// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
import type { NextApiRequest, NextApiResponse } from 'next'
import { promises as fs } from 'fs';
import path from 'path';
export default async function handler(
req: NextApiRequest,
res: NextApiResponse<string>
) {
const contentDir = path.join(process.cwd(), '/content');
const datasets = await fs.readdir(contentDir);
const query = req.query;
const { fileName } = query;
const dataFile = path.join(
process.cwd(),
'/content/' + datasets[0] + '/' + fileName
);
const data = await fs.readFile(dataFile, 'utf8');
res.status(200).send(data)
}

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import { GetStaticProps } from 'next';
import { promises as fs } from 'fs';
import path from 'path';
import parse from '../lib/markdown';
import DRD from '../components/DRD';
export const getStaticProps: GetStaticProps = async (context) => {
const contentDir = path.join(process.cwd(), '/content');
const datasets = await fs.readdir(contentDir);
const datasetReadme = path.join(
process.cwd(),
'/content/' + datasets[0] + '/README.md'
);
const readme = await fs.readFile(datasetReadme, 'utf8');
let { mdxSource, frontMatter } = await parse(readme, '.mdx');
return {
props: {
mdxSource,
frontMatter,
},
};
};
export default function DatasetPage({ mdxSource, frontMatter }) {
return (
<div className="prose mx-auto">
<header>
<div className="mb-6">
<>
<h1>{frontMatter.title}</h1>
{frontMatter.author && (
<div className="-mt-6">
<p className="opacity-60 pl-1">{frontMatter.author}</p>
</div>
)}
{frontMatter.description && (
<p className="description">{frontMatter.description}</p>
)}
</>
</div>
</header>
<main>
<DRD source={mdxSource} />
</main>
</div>
);
}

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module.exports = {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
}

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Year,Temp Anomaly
1850,-0.418
2020,0.923
1 Year Temp Anomaly
2 1850 -0.418
3 2020 0.923

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@ -0,0 +1,173 @@
Time,Anomaly (deg C),Lower confidence limit (2.5%),Upper confidence limit (97.5%)
1850,-0.41765878,-0.589203,-0.24611452
1851,-0.2333498,-0.41186792,-0.054831687
1852,-0.22939907,-0.40938243,-0.04941572
1853,-0.27035445,-0.43000934,-0.110699534
1854,-0.29163003,-0.43282393,-0.15043613
1855,-0.2969512,-0.43935776,-0.15454465
1856,-0.32035372,-0.46809322,-0.1726142
1857,-0.46723005,-0.61632216,-0.31813794
1858,-0.3887657,-0.53688604,-0.24064532
1859,-0.28119546,-0.42384982,-0.13854107
1860,-0.39016518,-0.5389766,-0.24135375
1861,-0.42927712,-0.5972301,-0.26132414
1862,-0.53639776,-0.7037096,-0.36908585
1863,-0.3443432,-0.5341645,-0.1545219
1864,-0.4654367,-0.6480974,-0.282776
1865,-0.33258784,-0.5246526,-0.14052312
1866,-0.34126064,-0.52183825,-0.16068307
1867,-0.35696334,-0.55306214,-0.16086453
1868,-0.35196072,-0.52965826,-0.17426313
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1878,-0.011315193,-0.13121258,0.10858219
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1914,-0.2625924,-0.38276345,-0.1424214
1915,-0.19184391,-0.32196194,-0.06172589
1916,-0.42020997,-0.5588941,-0.28152588
1917,-0.54301953,-0.6921192,-0.3939199
1918,-0.42458433,-0.58198184,-0.26718682
1919,-0.32551822,-0.48145813,-0.1695783
1920,-0.2985808,-0.44860035,-0.14856121
1921,-0.24067703,-0.38175339,-0.09960067
1922,-0.33922812,-0.46610323,-0.21235302
1923,-0.31793055,-0.444173,-0.1916881
1924,-0.3120622,-0.4388317,-0.18529275
1925,-0.28242525,-0.4147755,-0.15007503
1926,-0.12283547,-0.25264767,0.006976739
1927,-0.22940508,-0.35135695,-0.10745319
1928,-0.20676155,-0.33881804,-0.074705064
1929,-0.39275664,-0.52656746,-0.25894582
1930,-0.1768054,-0.29041144,-0.06319936
1931,-0.10339768,-0.2126916,0.0058962475
1932,-0.14546166,-0.25195515,-0.0389682
1933,-0.32234442,-0.4271004,-0.21758842
1934,-0.17433685,-0.27400395,-0.07466974
1935,-0.20605922,-0.30349734,-0.10862111
1936,-0.16952093,-0.26351926,-0.07552261
1937,-0.01919893,-0.11975875,0.08136089
1938,-0.012200732,-0.11030374,0.08590227
1939,-0.040797167,-0.14670466,0.065110326
1940,0.07593584,-0.04194966,0.19382134
1941,0.038129337,-0.16225387,0.23851255
1942,0.0014060909,-0.1952124,0.19802457
1943,0.0064140745,-0.19959097,0.21241911
1944,0.14410514,-0.054494828,0.3427051
1945,0.043088365,-0.15728289,0.24345961
1946,-0.1188128,-0.2659574,0.028331792
1947,-0.091205545,-0.23179041,0.04937931
1948,-0.12466127,-0.25913337,0.009810844
1949,-0.14380224,-0.2540775,-0.033526987
1950,-0.22662179,-0.33265698,-0.12058662
1951,-0.06115397,-0.15035024,0.028042298
1952,0.015354565,-0.08293597,0.11364509
1953,0.07763074,-0.020529618,0.1757911
1954,-0.11675021,-0.20850271,-0.024997713
1955,-0.19730993,-0.28442997,-0.1101899
1956,-0.2631656,-0.33912563,-0.18720557
1957,-0.035334926,-0.10056862,0.029898768
1958,-0.017632553,-0.083074555,0.04780945
1959,-0.048004825,-0.11036375,0.0143540995
1960,-0.115487024,-0.17416587,-0.056808177
1961,-0.019997388,-0.07078052,0.030785747
1962,-0.06405444,-0.11731443,-0.010794453
1963,-0.03680589,-0.09057008,0.016958294
1964,-0.30586675,-0.34949213,-0.26224136
1965,-0.2043879,-0.25357357,-0.15520222
1966,-0.14888458,-0.19839221,-0.09937696
1967,-0.11751631,-0.16062479,-0.07440783
1968,-0.1686323,-0.21325313,-0.124011464
1969,-0.031366713,-0.07186544,0.009132013
1970,-0.08510657,-0.12608096,-0.04413217
1971,-0.20593274,-0.24450706,-0.16735843
1972,-0.0938271,-0.13171694,-0.05593726
1973,0.04993336,0.013468528,0.086398184
1974,-0.17253734,-0.21022376,-0.1348509
1975,-0.11075424,-0.15130512,-0.07020335
1976,-0.21586166,-0.25588378,-0.17583954
1977,0.10308852,0.060056705,0.14612034
1978,0.0052557723,-0.034576867,0.04508841
1979,0.09085813,0.062358618,0.119357646
1980,0.19607207,0.162804,0.22934014
1981,0.25001204,0.21939126,0.28063282
1982,0.034263328,-0.005104665,0.07363132
1983,0.22383861,0.18807402,0.2596032
1984,0.04800471,0.011560736,0.08444869
1985,0.04972978,0.015663471,0.08379609
1986,0.09568697,0.064408,0.12696595
1987,0.2430264,0.21218552,0.27386728
1988,0.28215173,0.2470353,0.31726816
1989,0.17925027,0.14449838,0.21400215
1990,0.36056247,0.32455227,0.39657268
1991,0.33889654,0.30403617,0.3737569
1992,0.124896795,0.09088206,0.15891153
1993,0.16565846,0.12817313,0.2031438
1994,0.23354977,0.19841294,0.2686866
1995,0.37686616,0.34365577,0.41007656
1996,0.2766894,0.24318004,0.31019878
1997,0.4223085,0.39009082,0.4545262
1998,0.57731646,0.54304415,0.6115888
1999,0.32448497,0.29283476,0.35613516
2000,0.3310848,0.29822788,0.36394167
2001,0.48928034,0.4580683,0.5204924
2002,0.5434665,0.51278186,0.57415116
2003,0.5441702,0.5112426,0.5770977
2004,0.46737072,0.43433833,0.5004031
2005,0.60686255,0.5757053,0.6380198
2006,0.5725527,0.541973,0.60313237
2007,0.5917013,0.56135315,0.6220495
2008,0.46564984,0.43265733,0.49864236
2009,0.5967817,0.56525564,0.6283077
2010,0.68037146,0.649076,0.7116669
2011,0.53769773,0.5060012,0.5693943
2012,0.5776071,0.5448553,0.6103589
2013,0.6235754,0.5884838,0.6586669
2014,0.67287165,0.63890487,0.7068384
2015,0.82511437,0.79128706,0.8589417
2016,0.93292713,0.90176356,0.96409065
2017,0.84517425,0.81477475,0.87557375
2018,0.762654,0.731052,0.79425603
2019,0.8910726,0.85678726,0.92535794
2020,0.9227938,0.8882121,0.9573755
2021,0.6640137,0.5372486,0.79077876
1 Time Anomaly (deg C) Lower confidence limit (2.5%) Upper confidence limit (97.5%)
2 1850 -0.41765878 -0.589203 -0.24611452
3 1851 -0.2333498 -0.41186792 -0.054831687
4 1852 -0.22939907 -0.40938243 -0.04941572
5 1853 -0.27035445 -0.43000934 -0.110699534
6 1854 -0.29163003 -0.43282393 -0.15043613
7 1855 -0.2969512 -0.43935776 -0.15454465
8 1856 -0.32035372 -0.46809322 -0.1726142
9 1857 -0.46723005 -0.61632216 -0.31813794
10 1858 -0.3887657 -0.53688604 -0.24064532
11 1859 -0.28119546 -0.42384982 -0.13854107
12 1860 -0.39016518 -0.5389766 -0.24135375
13 1861 -0.42927712 -0.5972301 -0.26132414
14 1862 -0.53639776 -0.7037096 -0.36908585
15 1863 -0.3443432 -0.5341645 -0.1545219
16 1864 -0.4654367 -0.6480974 -0.282776
17 1865 -0.33258784 -0.5246526 -0.14052312
18 1866 -0.34126064 -0.52183825 -0.16068307
19 1867 -0.35696334 -0.55306214 -0.16086453
20 1868 -0.35196072 -0.52965826 -0.17426313
21 1869 -0.31657043 -0.47642276 -0.15671812
22 1870 -0.32789087 -0.46867347 -0.18710826
23 1871 -0.3685807 -0.5141493 -0.22301209
24 1872 -0.32804197 -0.4630833 -0.19300064
25 1873 -0.34133235 -0.4725396 -0.21012507
26 1874 -0.3732512 -0.5071426 -0.2393598
27 1875 -0.37562594 -0.514041 -0.23721085
28 1876 -0.42410994 -0.56287116 -0.28534868
29 1877 -0.101108834 -0.22982001 0.027602348
30 1878 -0.011315193 -0.13121258 0.10858219
31 1879 -0.30363432 -0.43406433 -0.1732043
32 1880 -0.31583208 -0.44015095 -0.19151321
33 1881 -0.23224552 -0.35793498 -0.10655605
34 1882 -0.29553008 -0.4201501 -0.17091006
35 1883 -0.3464744 -0.4608177 -0.23213111
36 1884 -0.49232006 -0.6026686 -0.38197154
37 1885 -0.47112358 -0.5830682 -0.35917896
38 1886 -0.42090362 -0.5225382 -0.31926903
39 1887 -0.49878576 -0.61655986 -0.3810117
40 1888 -0.37937889 -0.49332377 -0.265434
41 1889 -0.24989556 -0.37222093 -0.12757017
42 1890 -0.50685817 -0.6324095 -0.3813068
43 1891 -0.40131494 -0.5373699 -0.26525995
44 1892 -0.5075585 -0.64432853 -0.3707885
45 1893 -0.49461925 -0.6315314 -0.35770702
46 1894 -0.48376393 -0.6255681 -0.34195974
47 1895 -0.4487516 -0.58202064 -0.3154826
48 1896 -0.28400728 -0.4174015 -0.15061308
49 1897 -0.25980017 -0.39852425 -0.12107607
50 1898 -0.48579213 -0.6176492 -0.35393503
51 1899 -0.35543364 -0.48639694 -0.22447036
52 1900 -0.23447904 -0.3669676 -0.10199049
53 1901 -0.29342857 -0.42967388 -0.15718324
54 1902 -0.43898427 -0.5754281 -0.30254042
55 1903 -0.5333264 -0.66081935 -0.40583345
56 1904 -0.5975614 -0.7288325 -0.46629035
57 1905 -0.40775132 -0.5350291 -0.28047356
58 1906 -0.3191393 -0.45052385 -0.18775477
59 1907 -0.5041577 -0.6262818 -0.38203365
60 1908 -0.5138707 -0.63748026 -0.3902612
61 1909 -0.5357649 -0.6526296 -0.41890016
62 1910 -0.5310242 -0.6556868 -0.40636164
63 1911 -0.5392051 -0.66223973 -0.4161705
64 1912 -0.47567302 -0.5893311 -0.36201498
65 1913 -0.46715254 -0.5893755 -0.34492958
66 1914 -0.2625924 -0.38276345 -0.1424214
67 1915 -0.19184391 -0.32196194 -0.06172589
68 1916 -0.42020997 -0.5588941 -0.28152588
69 1917 -0.54301953 -0.6921192 -0.3939199
70 1918 -0.42458433 -0.58198184 -0.26718682
71 1919 -0.32551822 -0.48145813 -0.1695783
72 1920 -0.2985808 -0.44860035 -0.14856121
73 1921 -0.24067703 -0.38175339 -0.09960067
74 1922 -0.33922812 -0.46610323 -0.21235302
75 1923 -0.31793055 -0.444173 -0.1916881
76 1924 -0.3120622 -0.4388317 -0.18529275
77 1925 -0.28242525 -0.4147755 -0.15007503
78 1926 -0.12283547 -0.25264767 0.006976739
79 1927 -0.22940508 -0.35135695 -0.10745319
80 1928 -0.20676155 -0.33881804 -0.074705064
81 1929 -0.39275664 -0.52656746 -0.25894582
82 1930 -0.1768054 -0.29041144 -0.06319936
83 1931 -0.10339768 -0.2126916 0.0058962475
84 1932 -0.14546166 -0.25195515 -0.0389682
85 1933 -0.32234442 -0.4271004 -0.21758842
86 1934 -0.17433685 -0.27400395 -0.07466974
87 1935 -0.20605922 -0.30349734 -0.10862111
88 1936 -0.16952093 -0.26351926 -0.07552261
89 1937 -0.01919893 -0.11975875 0.08136089
90 1938 -0.012200732 -0.11030374 0.08590227
91 1939 -0.040797167 -0.14670466 0.065110326
92 1940 0.07593584 -0.04194966 0.19382134
93 1941 0.038129337 -0.16225387 0.23851255
94 1942 0.0014060909 -0.1952124 0.19802457
95 1943 0.0064140745 -0.19959097 0.21241911
96 1944 0.14410514 -0.054494828 0.3427051
97 1945 0.043088365 -0.15728289 0.24345961
98 1946 -0.1188128 -0.2659574 0.028331792
99 1947 -0.091205545 -0.23179041 0.04937931
100 1948 -0.12466127 -0.25913337 0.009810844
101 1949 -0.14380224 -0.2540775 -0.033526987
102 1950 -0.22662179 -0.33265698 -0.12058662
103 1951 -0.06115397 -0.15035024 0.028042298
104 1952 0.015354565 -0.08293597 0.11364509
105 1953 0.07763074 -0.020529618 0.1757911
106 1954 -0.11675021 -0.20850271 -0.024997713
107 1955 -0.19730993 -0.28442997 -0.1101899
108 1956 -0.2631656 -0.33912563 -0.18720557
109 1957 -0.035334926 -0.10056862 0.029898768
110 1958 -0.017632553 -0.083074555 0.04780945
111 1959 -0.048004825 -0.11036375 0.0143540995
112 1960 -0.115487024 -0.17416587 -0.056808177
113 1961 -0.019997388 -0.07078052 0.030785747
114 1962 -0.06405444 -0.11731443 -0.010794453
115 1963 -0.03680589 -0.09057008 0.016958294
116 1964 -0.30586675 -0.34949213 -0.26224136
117 1965 -0.2043879 -0.25357357 -0.15520222
118 1966 -0.14888458 -0.19839221 -0.09937696
119 1967 -0.11751631 -0.16062479 -0.07440783
120 1968 -0.1686323 -0.21325313 -0.124011464
121 1969 -0.031366713 -0.07186544 0.009132013
122 1970 -0.08510657 -0.12608096 -0.04413217
123 1971 -0.20593274 -0.24450706 -0.16735843
124 1972 -0.0938271 -0.13171694 -0.05593726
125 1973 0.04993336 0.013468528 0.086398184
126 1974 -0.17253734 -0.21022376 -0.1348509
127 1975 -0.11075424 -0.15130512 -0.07020335
128 1976 -0.21586166 -0.25588378 -0.17583954
129 1977 0.10308852 0.060056705 0.14612034
130 1978 0.0052557723 -0.034576867 0.04508841
131 1979 0.09085813 0.062358618 0.119357646
132 1980 0.19607207 0.162804 0.22934014
133 1981 0.25001204 0.21939126 0.28063282
134 1982 0.034263328 -0.005104665 0.07363132
135 1983 0.22383861 0.18807402 0.2596032
136 1984 0.04800471 0.011560736 0.08444869
137 1985 0.04972978 0.015663471 0.08379609
138 1986 0.09568697 0.064408 0.12696595
139 1987 0.2430264 0.21218552 0.27386728
140 1988 0.28215173 0.2470353 0.31726816
141 1989 0.17925027 0.14449838 0.21400215
142 1990 0.36056247 0.32455227 0.39657268
143 1991 0.33889654 0.30403617 0.3737569
144 1992 0.124896795 0.09088206 0.15891153
145 1993 0.16565846 0.12817313 0.2031438
146 1994 0.23354977 0.19841294 0.2686866
147 1995 0.37686616 0.34365577 0.41007656
148 1996 0.2766894 0.24318004 0.31019878
149 1997 0.4223085 0.39009082 0.4545262
150 1998 0.57731646 0.54304415 0.6115888
151 1999 0.32448497 0.29283476 0.35613516
152 2000 0.3310848 0.29822788 0.36394167
153 2001 0.48928034 0.4580683 0.5204924
154 2002 0.5434665 0.51278186 0.57415116
155 2003 0.5441702 0.5112426 0.5770977
156 2004 0.46737072 0.43433833 0.5004031
157 2005 0.60686255 0.5757053 0.6380198
158 2006 0.5725527 0.541973 0.60313237
159 2007 0.5917013 0.56135315 0.6220495
160 2008 0.46564984 0.43265733 0.49864236
161 2009 0.5967817 0.56525564 0.6283077
162 2010 0.68037146 0.649076 0.7116669
163 2011 0.53769773 0.5060012 0.5693943
164 2012 0.5776071 0.5448553 0.6103589
165 2013 0.6235754 0.5884838 0.6586669
166 2014 0.67287165 0.63890487 0.7068384
167 2015 0.82511437 0.79128706 0.8589417
168 2016 0.93292713 0.90176356 0.96409065
169 2017 0.84517425 0.81477475 0.87557375
170 2018 0.762654 0.731052 0.79425603
171 2019 0.8910726 0.85678726 0.92535794
172 2020 0.9227938 0.8882121 0.9573755
173 2021 0.6640137 0.5372486 0.79077876

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<path d="M141.04 16c-11.04 0-19 7.2-19 18s8.96 18 20 18c6.67 0 12.55-2.64 16.19-7.09l-7.65-4.42c-2.02 2.21-5.09 3.5-8.54 3.5-4.79 0-8.86-2.5-10.37-6.5h28.02c.22-1.12.35-2.28.35-3.5 0-10.79-7.96-17.99-19-17.99zm-9.46 14.5c1.25-3.99 4.67-6.5 9.45-6.5 4.79 0 8.21 2.51 9.45 6.5h-18.9zM248.72 16c-11.04 0-19 7.2-19 18s8.96 18 20 18c6.67 0 12.55-2.64 16.19-7.09l-7.65-4.42c-2.02 2.21-5.09 3.5-8.54 3.5-4.79 0-8.86-2.5-10.37-6.5h28.02c.22-1.12.35-2.28.35-3.5 0-10.79-7.96-17.99-19-17.99zm-9.45 14.5c1.25-3.99 4.67-6.5 9.45-6.5 4.79 0 8.21 2.51 9.45 6.5h-18.9zM200.24 34c0 6 3.92 10 10 10 4.12 0 7.21-1.87 8.8-4.92l7.68 4.43c-3.18 5.3-9.14 8.49-16.48 8.49-11.05 0-19-7.2-19-18s7.96-18 19-18c7.34 0 13.29 3.19 16.48 8.49l-7.68 4.43c-1.59-3.05-4.68-4.92-8.8-4.92-6.07 0-10 4-10 10zm82.48-29v46h-9V5h9zM36.95 0L73.9 64H0L36.95 0zm92.38 5l-27.71 48L73.91 5H84.3l17.32 30 17.32-30h10.39zm58.91 12v9.69c-1-.29-2.06-.49-3.2-.49-5.81 0-10 4-10 10V51h-9V17h9v9.2c0-5.08 5.91-9.2 13.2-9.2z" fill="#000"/>
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.container {
padding: 0 2rem;
}
.main {
min-height: 100vh;
padding: 4rem 0;
flex: 1;
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
}
.footer {
display: flex;
flex: 1;
padding: 2rem 0;
border-top: 1px solid #eaeaea;
justify-content: center;
align-items: center;
}
.footer a {
display: flex;
justify-content: center;
align-items: center;
flex-grow: 1;
}
.title a {
color: #0070f3;
text-decoration: none;
}
.title a:hover,
.title a:focus,
.title a:active {
text-decoration: underline;
}
.title {
margin: 0;
line-height: 1.15;
font-size: 4rem;
}
.title,
.description {
text-align: center;
}
.description {
margin: 4rem 0;
line-height: 1.5;
font-size: 1.5rem;
}
.code {
background: #fafafa;
border-radius: 5px;
padding: 0.75rem;
font-size: 1.1rem;
font-family: Menlo, Monaco, Lucida Console, Liberation Mono, DejaVu Sans Mono,
Bitstream Vera Sans Mono, Courier New, monospace;
}
.grid {
display: flex;
align-items: center;
justify-content: center;
flex-wrap: wrap;
max-width: 800px;
}
.card {
margin: 1rem;
padding: 1.5rem;
text-align: left;
color: inherit;
text-decoration: none;
border: 1px solid #eaeaea;
border-radius: 10px;
transition: color 0.15s ease, border-color 0.15s ease;
max-width: 300px;
}
.card:hover,
.card:focus,
.card:active {
color: #0070f3;
border-color: #0070f3;
}
.card h2 {
margin: 0 0 1rem 0;
font-size: 1.5rem;
}
.card p {
margin: 0;
font-size: 1.25rem;
line-height: 1.5;
}
.logo {
height: 1em;
margin-left: 0.5rem;
}
@media (max-width: 600px) {
.grid {
width: 100%;
flex-direction: column;
}
}
@media (prefers-color-scheme: dark) {
.card,
.footer {
border-color: #222;
}
.code {
background: #111;
}
.logo img {
filter: invert(1);
}
}

View File

@ -0,0 +1,105 @@
@tailwind base;
@tailwind components;
@tailwind utilities;
@import "@flowershow/remark-callouts/styles.css";
.w-5 {
width: 1.25rem
}
.h-5 {
height: 1.25rem
}
/* mathjax */
.math-inline > mjx-container > svg {
display: inline;
align-items: center;
}
/* smooth scrolling in modern browsers */
html {
scroll-behavior: smooth !important;
}
/* tooltip fade-out clip */
.tooltip-body::after {
content: "";
position: absolute;
right: 0;
top: 3.6rem; /* multiple of $line-height used on the tooltip body (defined in tooltipBodyStyle) */
height: 1.2rem; /* ($top + $height)/$line-height is the number of lines we want to clip tooltip text at*/
width: 10rem;
background: linear-gradient(
to right,
rgba(255, 255, 255, 0),
rgba(255, 255, 255, 1) 100%
);
}
:is(h2, h3, h4, h5, h6):not(.blogitem-title) {
margin-left: -2rem !important;
padding-left: 2rem !important;
scroll-margin-top: 4.5rem;
position: relative;
}
.heading-link {
padding: 1px;
position: absolute;
left: 0;
top: 50%;
transform: translateY(-50%);
margin: auto 0;
border-radius: 5px;
background: #1e293b;
opacity: 0;
transition: opacity 0.2s;
}
.light .heading-link {
/* border: 1px solid #ab2b65; */
/* background: none; */
background: #e2e8f0;
}
:is(h2, h3, h4, h5, h6):not(.blogitem-title):hover .heading-link {
opacity: 100;
}
.heading-link svg {
transform: scale(0.75);
}
@media screen and (max-width: 640px) {
.heading-link {
visibility: hidden;
}
}
html,
body {
padding: 0;
margin: 0;
font-family: -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Oxygen,
Ubuntu, Cantarell, Fira Sans, Droid Sans, Helvetica Neue, sans-serif;
}
a {
color: inherit;
text-decoration: none;
}
* {
box-sizing: border-box;
}
@media (prefers-color-scheme: dark) {
html {
color-scheme: dark;
}
body {
color: white;
background: black;
}
}

View File

@ -0,0 +1,12 @@
/** @type {import('tailwindcss').Config} */
module.exports = {
content: [
'./app/**/*.{js,ts,jsx,tsx,mdx}',
'./pages/**/*.{js,ts,jsx,tsx,mdx}',
'./components/**/*.{js,ts,jsx,tsx,mdx}',
],
theme: {
extend: {},
},
plugins: [require('@tailwindcss/typography')],
};

View File

@ -0,0 +1,20 @@
{
"compilerOptions": {
"target": "es5",
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": true,
"skipLibCheck": true,
"strict": false,
"forceConsistentCasingInFileNames": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "node",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", "middleware.ts"],
"exclude": ["node_modules"]
}

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@ -1,53 +1,405 @@
# 🌀 PortalJS: The JavaScript framework for data portals
# Getting Started
🌀 PortalJS is a framework for rapidly building rich data portal frontends using a modern frontend approach. PortalJS can be used to present a single dataset or build a full-scale data catalog/portal.
Welcome to the PortalJS documentation!
Built in JavaScript and React on top of the popular [Next.js](https://nextjs.com/) framework. PortalJS assumes a "decoupled" approach where the frontend is a separate service from the backend and interacts with backend(s) via an API. It can be used with any backend and has out of the box support for [CKAN](https://ckan.org/).
If you have questions about anything related to PortalJS, you're always welcome to ask our community on [GitHub Discussions](https://github.com/datopian/portaljs/discussions) or on [our chat channel on Discord](https://discord.gg/EeyfGrGu4U).
PortalJS offers a variety of examples (templates) to bootstrap your own data portal in just a few minutes. [Get started now!](#getting-started)
## System Requirements
## Features
- Node.js 14.18.0 or newer
- MacOS, Windows (including WSL), and Linux are supported
- 🗺️ Unified sites: present data and content in one seamless site, pulling datasets from a DMS (e.g. CKAN) and content from a CMS (e.g. Wordpress) with a common internal API.
- 👩‍💻 Developer friendly: built with familiar and modern frontend tech such as JavaScript, React and Next.js, so you can take advantage of everything these technologies have to offer: Server Side Rendering, Static Site Generation, huge number of examples and more!
- 🔋 Batteries included: full set of portal components out of the box e.g. catalog search, dataset showcase, blog, etc.
- 🎨 Easy to theme and customize: installable themes, use standard CSS and React+CSS tooling. Add new routes quickly.
- 🧱 Extensible: quickly extend and develop/import your own React components
- 📝 Well documented: full set of documentation plus the documentation of Next.js and Apollo.
## Automatic Setup
## What is a data portal?
To automatically setup a new project, simply run the following command (don't forget to change `<project-name>` to the name of your project):
A Data Portal is a gateway to data. That gateway can be big or small, open or restricted. For example, data.gov is open to everyone, whilst an enterprise "intra" data portal is restricted to that enterprise (and perhaps even to certain people within it).
```bash
npx create-next-app <project-name> --example https://github.com/datopian/portaljs/tree/main/examples/basic-example
```
A Data Portal's core purpose is to enable the rapid discovery and use of data. However, as a flexible, central point of truth on an organizations data assets, a Data Portal can become essential data infrastructure and be extended or integrated to provide many additional features:
Your new project is now created. Note that the dependencies are going to be installed already. To run it, get into the directory of the project and run:
- Data storage and APIs
- Data visualization and exploration
- Data validation and schemas
- Orchestration and integration of data
- Data Lake coordination and organization
```bash=
npm run dev
```
The rise of Data Portals reflect the rapid growth in the volume and variety of data that organizations hold and use. With so much data available internally (and externally) it is hard for users to discover and access the data they need. And with so many potential users and use-cases it is hard to anticipate what data will be needed, when.
The project is now going to be running on http://localhost:3000.
**[Read more about data portals](/docs/dms/data-portals)**
At this point you can start editing the project as you want or start the Portal.JS tutorials series.
## Getting started
# Tutorial
To get started creating your own data portal with PortalJS, take a look at the examples below and analyze which one best suits your needs:
## Create a data portal with a single dataset
### Examples
Welcome to the PortalJS tutorials series. In this first tutorial, we are going to take a look at a simple data portal example built with PortalJS, understand its structure and learn how we can customize it, specially with data components.
* [Data catalog with data coming from CKAN](/docs/example-ckan)
* [Simple data catalog](/docs/example-data-catalog)
The resulting data portal is our _Hello World_ equivalent: a single dataset, and it looks like this:
Then, simply follow the instructions on the given example page to use it as the template of your project.
<img src="/assets/docs/tutorial-1-img-1.png" />
## Tutorials and guides
This tutorials series is sequential, so the next tutorials starts from where this one left, don't forget to save your progress, and, finally, let's get started!
We are working on that! Tutorials coming out soon.
### Create a new PortalJS project
## Getting Help
First step is to create a new PortalJS project. To do that, please follow the instructions on the [Getting Started](#getting-started) section of the docs.
If you have questions about anything related to PortalJS, you're always welcome to ask our community on [GitHub Discussions](https://github.com/datopian/portaljs/discussions) or on our [Discord server](https://discord.gg/EeyfGrGu4U).
Now, make sure you have the project running on your local environment (`npm run dev`) and access http://localhost:3000 on your browser. As you can see, the new project is not empty, it already contains some content which we will use as a base in this tutorial. Here's what the page looks like:
<img src="/assets/docs/tutorial-1-img-2.png" />
### Basics
As you can see, the page is very generic, and consists of a header, some text, a table and a line chart (built with Vega). Soon we are going to make it more interesting, but first, how did we end up with this?
#### The content routing system
Let's start by analyzing the main components of the folder strucutre of the project:
```bash
content/
my-dataset/
README.md
public/
data_1.csv
data_2.csv
```
You see that `README.md` file inside the content folder? That's exactly what's being rendered on your browser. PortalJS uses a filesystem approach to content routing, this means that the folder structure inside the content folder determines the routes used to access the pages in the application, a page being a `.md` (Markdown) file, analogously to a HTML document. When the file is named "README.md", it means that it's an index file. Take a look at the following example:
```bash
content/
README.md # => Page rendered at /
folder-1/
README.md # => Page rendered at /folder-1
folder-2/
README.md # => Page rendered at /folder-2
folder-2-1/
README.md # => Page rendered at /folder-2/folder-2-1
```
INTERNAL NOTE: let's change that to index.md instead of README.md. Add examples of non-index pages? The MDX pipeline should be handling other .md files but it's not doing that rn. Maybe remove next paragraph
Note that it's also possible to create non-index pages, but this is not going to be demonstrated on this tutorial for the sake of simplicity.
#### The pages
_Cool, a Markdown file becomes a page, but what is a Markdown file :thinking_face:?_
Without getting into much detail, Markdown is a markup language which allows users to write structured and formatted text using a very simple syntax, with the beauty of not leaving the realm of plain text and keeping the document completely human-readable (opposite of, for instance, HTML, in which the document might get messy and very hard to read when it's not being rendered on a browser).
It's not the intent of this tutorial to guide the user throught Markdown, but it's a requirement to understand it, so if you are not familiar with it we encourage you to take a look at [this guide](https://www.datopian.com/playbook/markdown) written by Datopian (Markdown is going to take over the world, seriously, you won't regret it!).
Now that you are aware of Markdown documents and their application on PortalJS, let's hop to how this page you were seeing on your browser looks like behind the scenes. You probably noticed the cool chart and table on the page. Plain Markdown cannot do that, but the extended Markdown on PortalJS can.
If you open `content/README.md` on your IDE or any text editor, you are going to come across the following content:
```markdown
# Data
This is the README.md this project.
## Table
<Table url="data_1.csv" />
## Vega Lite Line Chart from URL
<VegaLite spec={ { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "data": {"url": "data_2.csv"}, "width": 550, "height": 250, "mark": "line", "encoding": { "x": {"field": "Time", "type": "temporal"}, "y": {"field": "Anomaly (deg C)", "type": "quantitative"}, "tooltip": {"field": "Anomaly (deg C)", "type": "quantitative"} } } } />
```
Note the `<Table />` and the `<VegaLite />` components, that's how data components are used on PortalJS, similar to tags on HTML documents. Each data component will have it's own set of attributes. These two are not the only data components that are supported, but it's interesting to note that data components can be used in a way as simple as the table pointing to a CSV file, or as flexible and complex as a chart built using a VegaLite spec.
One other very interesting point to notice here is that both data components are getting its data from the data files inside the public folder. When a relative URL is provided as the data source for a data component, PortalJS will look for the given file in the public folder.
We now have the basics, let's build something.
### Making the dataset page more interesting
It's time to start playing around with the project. Let's say we want to create a dataset page to present the data about the TV series Breaking Bad (or feel free choose a different theme and be creative!). Here are some sites with data that we can use:
- [Openpsychometrics.com Test](https://openpsychometrics.org/tests/characters/stats/BB/)
- [Rotten Tomatoes Page](https://www.rottentomatoes.com/tv/breaking_bad)
Open the `content/my-dataset/README.md` file and delete the content inside it. Now, let's start with a heading and description:
```markdown
# Breaking Bad Statistics
**Data source:** https://openpsychometrics.org/tests/characters/stats/BB/
Visualizations about the public perception of the Breaking Bad TV series and its characters.
```
Cool, with that, our intention with this page is now clear. Time to add some visualizations.
#### Tables
Let's start with a table. There's an interesting table in the dataset about the notability of 10 of the characters on the [Openpsychometrics.com Test](https://openpsychometrics.org/tests/characters/stats/BB/), let's reproduce that in our page. Here's the data in CSV format:
```bash
Notability,Name
91.3,Walter White
88.9,Jesse Pinkman
82.5,Mike Ehrmantraut
79.6,Gus Fring
74.8,Hank Schrader
73.8,Saul Goodman
61.3,Jane Margolis
55.4,Skyler White
46.8,Flynn White
27.9,Marie Schrader
```
Tables can be created from different data sources on PortalJs, these being:
##### URL
The URL can be either internal (relative) or external. To create a table from a URL, use the following syntax:
```jsx
<Table url="data.csv" /> // Internal, file at /public/data.csv
// Or
<Table url="https://people.sc.fsu.edu/~jburkardt/data/csv/addresses.csv" />
```
##### Inline CSV
To create a table using inline CSV, use the following syntax:
```jsx
<Table csv={`
Year,Cost
2018,50345.50
2019,65272.20
2020,48413.80
2021,76213.50
2022,71653.60
`} />
```
##### Columns and rows
Finally, you can also provide the data in the form of columns and rows using the following syntax:
```jsx
<Table cols={[
{ key: 'id', name: 'ID' },
{ key: 'firstName', name: 'First name' },
{ key: 'lastName', name: 'Last name' },
{ key: 'age', name: 'Age' }
]} data={[
{ id: 1, lastName: 'Snow', firstName: 'Jon', age: 35 },
{ id: 2, lastName: 'Lannister', firstName: 'Cersei', age: 42 },
{ id: 3, lastName: 'Lannister', firstName: 'Jaime', age: 45 },
{ id: 4, lastName: 'Stark', firstName: 'Arya', age: 16 },
{ id: 7, lastName: 'Clifford', firstName: 'Ferrara', age: 44 },
{ id: 8, lastName: 'Frances', firstName: 'Rossini', age: 36 },
{ id: 9, lastName: 'Roxie', firstName: 'Harvey', age: 65 },
]}
/>
```
___
Now that you are more familiar with the table data component, let's go ahead and add the table to the page. Since there are only a few rows in the data, inline CSV might be a good option for this table, but feel free to create a CSV file or to convert the data to columns and rows if you want. Add that to the end of the file:
```markdown
## Character Notability
<Table csv={`
Notability,Name
91.3,Walter White
88.9,Jesse Pinkman
82.5,Mike Ehrmantraut
79.6,Gus Fring
74.8,Hank Schrader
73.8,Saul Goodman
61.3,Jane Margolis
55.4,Skyler White
46.8,Flynn White
27.9,Marie Schrader
`} />
_Isn't it interesting that Saul is so below in the ranking? There's even a spin-off about him._
```
Here's how it's going to look like on the page:
<img src="/assets/docs/tutorial-1-img-3.png" />
#### Simple line charts
Let's use the `<LineChart />` data component and the ratings on Rotten Tomatoes to create a rating by year line chart (note that each season was released in a diffent year).
INTERNAL NOTE: LineChart is not working properly on the example, the width is not right. Can't we make numeric X work as well instead of having just years? We still have that bug in which the X is offsetted by -1.
First, here's the data of the rating by season in CSV format:
```bash
Year,Rating
2008,86
2009,97
2010,100
2011,100
2012,97
```
The `<LineChart />` data component expects two attributes: `title` and `data`, so let's add the following to the end of the file:
```markdown
## Rating x Season
<LineChart title="Rating x Season" data={
[
["2008",86],
["2009",97],
["2010",100],
["2011",100],
["2012",97]
]
}
/>
_Consistently well received by critics_
```
Here are the results:
<img src="/assets/docs/tutorial-1-img-4.png" />
#### Complex charts
Finally, PortalJS also supports the creation of visualizations with [Vega and VegaLite](https://vega.github.io/). This becomes specially interesting when it's desired to create more complex and custom visualizations. To demonstrate this, let's add a bar chart that compares Breaking Bad to Better Call Saul, a spin-off of the series, based on the data on Rotten Tomatoes. Here's the data in CSV format:
```bash
TV Show,Average Tomatometer,Average Audience Score
Breaking Bad,0.96,0.97
Better Call Saul,0.98,0.96
```
Add that to the file:
```jsx=
## Breaking Bad x Better Call Saul
<VegaLite spec={
{
"width": 150,
"data": {
"values": [
{"TV Show": "Breaking Bad", "Rating": "Average Tomatometer", "Value":0.96},
{"TV Show":"Breaking Bad", "Rating": "Average Audience Score", "Value":0.97},
{"TV Show":"Better Call Saul", "Rating": "Average Tomatometer", "Value":0.98},
{"TV Show":"Better Call Saul", "Rating": "Average Audience Score", "Value":0.96}
]
},
"mark": "bar",
"encoding": {
"column": {"field": "TV Show","header": {"orient": "bottom"}},
"y": {"field": "Value", "type": "quantitative"},
"x": {"field": "Rating", "axis": null},
"color": {"field": "Rating"}
},
"config": {
"view": {"stroke": "transparent"}
}
}
} />
_The producers were able to successfully expand the success of the original series to the spin-off_
```
It's going to look like this when you navigate to the page again:
<img src="/assets/docs/tutorial-1-img-5.png" />
### Final results
Here's the whole source code of the dataset page we built:
```markdown
# Breaking Bad Statistics
**Data source:** https://openpsychometrics.org/tests/characters/stats/BB/
Visualizations about the public perception of the Breaking Bad TV series and its characters.
## Character Notability
<Table csv={`
Notability,Name
91.3,Walter White
88.9,Jesse Pinkman
82.5,Mike Ehrmantraut
79.6,Gus Fring
74.8,Hank Schrader
73.8,Saul Goodman
61.3,Jane Margolis
55.4,Skyler White
46.8,Flynn White
27.9,Marie Schrader
`} />
_Isn't it interesting that Saul is so below in the ranking? There's even a spin-off about him._
## Rating x Season
<LineChart title="Rating x Season" data={
[
["2008",86],
["2009",97],
["2010",100],
["2011",100],
["2012",97]
]
}
/>
_Consistently well received by critics_
## Breaking Bad x Better Call Saul
<VegaLite spec={
{
"width": 150,
"data": {
"values": [
{"TV Show": "Breaking Bad", "Rating": "Average Tomatometer", "Value":0.96},
{"TV Show":"Breaking Bad", "Rating": "Average Audience Score", "Value":0.97},
{"TV Show":"Better Call Saul", "Rating": "Average Tomatometer", "Value":0.98},
{"TV Show":"Better Call Saul", "Rating": "Average Audience Score", "Value":0.96}
]
},
"mark": "bar",
"encoding": {
"column": {"field": "TV Show","header": {"orient": "bottom"}},
"y": {"field": "Value", "type": "quantitative"},
"x": {"field": "Rating", "axis": null},
"color": {"field": "Rating"}
},
"config": {
"view": {"stroke": "transparent"}
}
}
} />
_The producers were able to successfully expand the success of the original series to the spin-off_
```
And here's a screenshot of what it looks like:
<img src="/assets/docs/tutorial-1-img-6.png" />
### Next steps
Now that you already know how to create pages and render data components, we encourage you to play around with this project. You can try adding new visualizations, changing values, or creating a new page about something you find interesting.
Finally, proceed to the next tutorial in the series.

View File

@ -4,7 +4,8 @@
"private": true,
"scripts": {
"dev": "next dev",
"build": "npm run mddb && next build",
"build": "next build",
"prebuild": "npm run mddb && node ./scripts/fix-symlinks.mjs",
"start": "next start",
"mddb": "mddb content"
},

View File

@ -0,0 +1,15 @@
// Script executed before builds
import { exec } from "child_process";
import fs from "fs";
// If Vercel environment is detected
if (process.env.VERCEL_ENV) {
console.log(
"[scripts/fix-symlinks.mjs] Vercel environment detected. Fixing symlinks..."
);
// fs.unlinkSync('public/assets')
exec('cp -r ./content/assets ./public/')
}