25:["$","title",null,{"children":"Contribution to «What is Work?» | Jack Verstappen | Observable"}] 4b:T46a,# Contribution to *«What is Work?»* For the ongoing research titled **«What is Work?»** by Philippine Hoegen, Julia Reist, and Miriam Hempel, I provided some *data visualization* inspiration for a public event at Kunsthal Gent. Responding to an open call, people participated in a discussion on what they value in work, and how they value their work. Each individual was asked to give a score for the value of their work on 12 dimensions, collectively building up a giant *spider chart* on the floor of the Kunsthal Gent. This process was captured as a time-lapse video. Eventually, the question presented itself if through (data) analysis any tentative conclusions could be drawn. I offered my help by translating a digital format (an image) of the end result into numeric data and putting some charts together to visualize the data in various ways, allowing them to have different perspectives on the results. For more detail on the full research, please visit: - *[What is Work?](https://kunsthal.gent/en/events/what-is-work)* - *[[Presenting: What is Work?](https://kunsthal.gent/activiteiten/episode-4-what-is-work)* 4c:T82f,viewof drawBasicTernaryPlot = { const node = DOM.svg(width, height); const svg = d3.select(node); const radius = basicTernaryPlot.radius(); const chart = svg .append("g") .attr("transform", `translate(${width / 2} ${height / 2 + yOffset})`) .attr("height", 300) .attr("font-family", selectedFont) //"sans-serif") .attr("id", "chart") .on("mousemove", handleMouseMove); const defs = chart.append("defs"); const clipPath = defs .append("clipPath") .attr("id", "trianglePath") .append("path") .attr("d", basicTernaryPlot.triangle()); const axisLabelsGroup = chart .append("g") .attr("class", "axis-labels") .call(axisLabels, basicTernaryPlot.axisLabels()); const gridLinesPaths = basicTernaryPlot .gridLines(gridLineCount) .map(axisGrid => axisGrid.map(d3.line()).join(" ")); const gridGroup = chart .append("g") .attr("class", "grid") .call(grid, gridLinesPaths); const axisTicksGroups = chart .append("g") .attr("class", "ternary-ticks") .attr("font-size", 10) .selectAll("g") .data(basicTernaryPlot.ticks(tickCount)) .join("g") .attr("class", "axis-ticks"); axisTicksGroups.call(ticks); const ternaryData = dataplus.map(d => { const [x, y] = basicTernaryPlot(d); return { x, y, ...d }; }); // data const dots = chart .append("g") .attr("class", "data") .attr("clip-path", "url(#trianglePath)") .selectAll("circle") .data(ternaryData) .join("circle") .attr("r", 4) .attr("cx", d => d.x) .attr("cy", d => d.y) .attr("fill", "#444") .attr("stroke", "#ddd"); // initial triangle const trianglePath = chart .append("path") .attr("d", basicTernaryPlot.triangle()) .attr("fill", "transparent") .attr("stroke", "black") .attr("stroke-width", 2); function handleMouseMove(d) { const xy = d3.pointer(d); const inverse = basicTernaryPlot.invert(xy); node.dispatchEvent(new CustomEvent("input"), { bubbles: true }); node.value = inverse; } return svg.node(); }4d:[{"id":"b26a756f422d78330c40ceedd66f5125618c1476fa91a21e7f995b201e22281e205015bbc7a1caaf42dad028c8bcf7b3a774a95d5e5df4b3fd37190cb3862263","href":"https://static.observableusercontent.com/files/b26a756f422d78330c40ceedd66f5125618c1476fa91a21e7f995b201e22281e205015bbc7a1caaf42dad028c8bcf7b3a774a95d5e5df4b3fd37190cb3862263","name":"whatiswork.csv","type":"text/csv","lastModified":1644223840073.514,"size":1044},{"id":"5a882b0e4d3b405ad8ca96c08450c3393f69e8a98d12652de2f4a8653c01117710c935e112cd8674d4a96f376a60d2b6dd1f747c357b5508f7f6a213b97a6aaf","href":"https://static.observableusercontent.com/files/5a882b0e4d3b405ad8ca96c08450c3393f69e8a98d12652de2f4a8653c01117710c935e112cd8674d4a96f376a60d2b6dd1f747c357b5508f7f6a213b97a6aaf","name":"whatiswork.png","type":"image/png","lastModified":1647107998586.589,"size":4577510}] 47:{"id":"2463151d90ed8576","slug":"what-is-work","title":"Contribution to «What is Work?»","thumbnail":"8271ba0741eaca8cb63a4885462b5f45abaddcb2b512613816ec54716c1beed1","access_level":"public","create_time":"$D2022-02-07T08:47:14.821Z","update_time":"$D2022-04-11T14:43:49.976Z","view_time":null,"first_public_time":"$D2022-02-08T15:50:42.711Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"a6a1e4b9e523112f","login":"jackverstappen","name":"Jack Verstappen","avatar_url":"https://avatars-next.observableusercontent.com/eef162d47242ef1943dcd344ae5eac7ad3ef7f08e43632320ff154d7c1ceddc5","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":1224,"publish_version":1224,"role":null,"authored":false,"authors":[{"type":"individual","id":"cf6b5a97ab314cb8","login":"jackverstappen","name":"Jack Verstappen","avatar_url":"https://avatars-next.observableusercontent.com/eef162d47242ef1943dcd344ae5eac7ad3ef7f08e43632320ff154d7c1ceddc5","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"cf6b5a97ab314cb8","login":"jackverstappen","name":"Jack Verstappen","avatar_url":"https://avatars-next.observableusercontent.com/eef162d47242ef1943dcd344ae5eac7ad3ef7f08e43632320ff154d7c1ceddc5","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Contribution to «What is Work?»","cells":[{"id":973,"value":"$4b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":878,"value":"\n

$\"what$

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":500,"value":"## What about every dimension individually? \nA well-known representation of a group of scorings is a [box-plot](https://en.wikipedia.org/wiki/Box_plot), shown below. Every value dimension is represented by:\n- a **thick black (vertical) line**, representing the average result\n- a **gray box**, representing the range for 50% of the results around the average\n- a **thin black (horizontal) line**, representing the full range of answers, excluding potential outliers\n- eventual some **bubbles** representing results considered as an outlier\n\nWhen ordering the dimensions according to their average value, to shows that \n- *«self-expression»*\n- *«agency/autonomy»*\n- *«gaining skills»*\n- *«appreciation»*\n\nare high valued. Where although *«general knowledge»* also has a relatively high value, because the wide range of values is way less obvious. \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":439,"value":"Plot.plot({\n marginLeft:200,\n marginBottom:40,\n height:400,\n width:800,\n style: {\n backgroundColor: colorBackground,\n color: color,\n fontFamily: selectedFont,\n fontSize: 16,\n overflow: \"hidden\"\n },\n y: {\n domain : columns.sort((firstEl, secondEl) => {\n return columnMean(secondEl) - columnMean(firstEl);\n })\n },\n x: {\n domain: [0, 100],\n tickRotate: 0,\n ticks: 10,\n tickSize: 5,\n line: true,\n tickPadding: 10,\n labelAnchor: \"right\",\n labelOffset: -10,\n nice: true,\n grid: true,\n inset: 20\n },\n marks: [\n boxX(dataflat, {y: \"dimension\", x: \"value\"})\n ]\n})\n\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":507,"value":"## What about a combination of dimensions?\nTo explore if there are any relationships between dimensions, various graphs require the selection of dimensions. Below up to 3 different dimensions can independently be selected. The selected dimensions will be represented by *x, y* or *z*.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":42,"value":"viewof xColumn = Inputs.select(columns_x, {sort: false, unique: true, label: \"x\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"viewof yColumn = Inputs.select(columns_y, {sort: false, unique: true, label: \"y\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"viewof zColumn = Inputs.select(columns_z, {sort: false, unique: true, label: \"z\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1156,"value":"### Linear correlation \nFirst off is the exploration of a singular linear correlation, i.e. how the *x* and *y* are correlated.\nEach participant is represented against those two selected dimensions as a **black dot**, where a **dotted line** represented the [linear regression](https://en.wikipedia.org/wiki/Linear_regression) between those dimensions. \n\nA (relative) horizontal line indicates that is little to deduce from the two selected dimensions. A line with a (relative) strong slope indicates a stronger relationship between the two selected dimensions. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":15,"value":" \nregPlot.plot({\n grid: true,\n style: {\n backgroundColor: colorBackground,\n color: color,\n fontFamily: selectedFont, \n fontSize: 12,\n overflow: \"hidden\"\n },\n x: {\n tickRotate: 0,\n ticks: 8,\n tickSize: 5,\n line: true,\n tickPadding: 10,\n labelAnchor: \"right\",\n labelOffset: -10,\n nice: true,\n domain: [0, 100]},\n y: {domain: [0, 100]},\n width: 800,\n heigth:800,\n marks: [\n regPlot.dot(data, {\n x: xColumn,\n y: yColumn,\n fill: \"#000000\"\n }),\n regPlot.ruleX(data,{\n x: xColumn,\n y1: yColumn,\n y2: d => model(d[xColumn]),\n strokeOpacity: 0.1\n }),\n regPlot.line(\n data,\n regPlot.regression({\n x: xColumn,\n y: yColumn,\n strokeDasharray: [1.5, 4],\n strokeWidth: 1.5,\n type,\n bandwidth: param,\n order: param\n })\n )\n ]\n})\n\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":516,"value":"### Ternary plot of 3 dimensions \nIn the following section a [ternary plot(https://en.wikipedia.org/wiki/Ternary_plot) displays the value distribution over the following 3 selected dimensions :\n* {x} : *«${xColumn}»*\n* {y} : *«${yColumn}»*\n* {z} : *«${zColumn}»*\n\nThis is accomplished by adding the value of those dimensions and determining the percentage that each of the individual dimensions contributes to that total. Visually this will indicate for each participant towards\n- the **center** of the triangle if all dimensions have a similar value\n- the **top** of the triangle if the *x-dimension* has a higher value\n- the **bottom-left** of the triangle if the *y-dimension* has a higher value\n- the **bottom-right** of the triangle if the *z-dimension* has a higher value","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":283,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":496,"value":"---\n## Appendix\nBelow are various elements supporting the above charts. There are no specific insights to obtain from them. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":640,"value":"### Data Loading/Handling/Extending","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3,"value":"data = FileAttachment(\"whatiswork.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"Inputs.table(data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"columns = data.columns.slice(1, )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":947,"value":"columns_x = columns;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":936,"value":"columns_y = columns.filter(e => { console.log(e); return e != xColumn});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":945,"value":"columns_z = columns_y.filter(e => e != yColumn);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":209,"value":"dataplus = {\n return data.map(d => ({...d, composition: composition([d[xColumn], d[yColumn], d[zColumn]])}));\n\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":429,"value":"dataflat = {\n var flat = [];\n data.forEach((d) => {\n columns.forEach((c) => {\n flat.push({\"person\":d.person,\"dimension\":c,\"value\":d[c]});\n });\n }); \n return flat;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":696,"value":"columnAverage = (col) => {\n return dataplus.reduce( ( p, c ) => p + c[col], 0 ) / dataplus.length;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1068,"value":"errorCoefficient = ss.rSquared(data.map(d => ([d[xColumn], d[yColumn]])), model).toFixed(3)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1090,"value":"mse = {\n let middle = Math.floor(data.length / 2);\n let sse = data.map((d) => {\n return (d[yColumn] - model(d[xColumn]))**2;\n }).sort((a,b)=>a-b)\n return data.length % 2 !== 0 ? sse[middle] : (sse[middle - 1] + sse[middle]) / 2;\n}\n ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":755,"value":"columnMean = (col) => {\n var sorted = dataplus.sort((firstEl, secondEl) => { return secondEl[col] - firstEl[col];})\n var mid = (dataplus.length - dataplus.length %2) / 2 + dataplus.length % 2 - 1;\n if (dataplus.length % 2 == 0 ) {\n return (sorted[mid][col]+sorted[mid+1][col])/2;\n } else {\n return sorted[mid][col];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":852,"value":"columnRange = (col) => {\n var sorted = dataplus.sort((firstEl, secondEl) => { return secondEl[col] - firstEl[col];})\n return sorted[dataplus.length-1][col] - sorted[0][col];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":854,"value":"columnIQR = (col) => {\n var sorted = dataplus.sort((firstEl, secondEl) => { return secondEl[col] - firstEl[col];})\n return sorted[13][col] - sorted[4][col];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":647,"value":"### Graph configurations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":122,"value":"viewof type = Inputs.select([\"Linear\", \"Quad\", \"Poly\", \"Log\", \"Exp\", \"LOESS\"], {\n label: \"type\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"viewof param = type === \"LOESS\"\n ? Inputs.range([0, 1], { label: \"bandwidth (LOESS)\" })\n : type === \"Poly\"\n ? Inputs.range([1, 5], { label: \"poly order\", value: 3, step: 1 })\n : Object.assign(html`no additional settings`, { value: null })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":286,"value":"viewof gridLineCount = Inputs.range([0, 20], {\n value: 10,\n step: 1,\n label: \"Grid line count\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":287,"value":"viewof tickCount = Inputs.range([1, 25], {\n value: 10,\n step: 1,\n label: \"Tick count\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":579,"value":"### Style Settings","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":585,"value":"viewof color = Inputs.color({label: \"Text color\", value: \"#333\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":586,"value":"viewof colorBackground = Inputs.color({label: \"Chart color\", value: \"#ffffff\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":566,"value":"viewof selectedFont = Inputs.select(\n [\n \"Source Serif Pro\",\n \"Source Sans Pro\",\n \"Alegreya\",\n \"Inter\",\n \"Lato\",\n \"Laila\",\n \"Merriweather\",\n \"PT Serif\",\n \"Roboto Slab\",\n \"Rubik\",\n \"Syne Tactile\"\n ],\n {\n label: \"Desired font\",\n // options:,\n value: \"Source Serif Pro\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":572,"value":"parseFont = selectedFont.split(\" \").join(\"+\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":567,"value":"style = html`\n\n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":636,"value":"### Helper functions & settings","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":185,"value":"composition = arr => {\n let total = d3.sum(arr) \n return arr.map(d => d / total)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"basicTernaryPlot = {\n const normalTernary = barycentric()\n .a((d) => d.composition[0])\n .b((d) => d.composition[1])\n .c((d) => d.composition[2]);\n\n return ternaryPlot(normalTernary)\n .tickFormat(\"%\")\n .radius(radius)\n .tickAngles([0,0,-60])\n //.tickAnchors([\"end\",\"end\",\"end\"])\n .labelAngles([0,0,0])\n .labelOffsets([20,40,60])\n .labels(labels);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":259,"value":"labels = ['{x}','{y}','{z}']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":256,"value":"radius = Math.min(width, height) / 2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":263,"value":"height = 400","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":303,"value":"yOffset = radius / 4","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1040,"value":"model = ss.linearRegressionLine(ss.linearRegression(data.map(d => ([d[xColumn], d[yColumn]]))))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":630,"value":"### Import helper libraries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"import {addRegression} from \"@fil/plot-regression\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"reg = require(\"d3-regression@1\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"regPlot = addRegression(Plot_)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"import { Plot as Plot_ } from \"@fil/plot-splom\" // for Plot.transform & Plot.column","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"import {boxX, boxY, outliers, iqr1, iqr2, quartile1, quartile3} from \"@observablehq/plot-box\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"import { ternaryPlot, barycentric, grid, ticks, axisLabels } from \"@julesblm/d3-ternary\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1009,"value":"d3 = require(\"d3-array@3\", \"d3-axis@3\", \"d3-regression@1.3\", \"d3-scale@4\", \"d3-shape@3\", \"d3-selection@3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1032,"value":"ss = require(\"simple-statistics@6.0.0/dist/simple-statistics.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4d","stdlib":"1"}} 24:null