25:["$","title",null,{"children":"Team 4 - 🍌🦑 Co2-Equivalent Emissions for different Produce 🦞🍍 | mold | Observable"}] 49:Tffa,(function scatterplot() { const centerX = width / 2; const centerY = height / 2; const scaleFactor = 300; let attr = "size"; const svg = d3.create("svg") .attr('width', width) .attr('height', height); const simulation = d3.forceSimulation(valueByFoodType) .force('x', d3.forceX(width / 2).strength(0.1)) .force('y', d3.forceY(height / 2).strength(0.1)) .force('collide', d3.forceCollide(d => ((d[window.scaleAttribute] * scaleFactor / 2) + 5))) .force('charge', d3.forceManyBody().strength(-30)) //.force('center', d3.forceCenter(width / 2, height / 2)) window.simulation = simulation if (window.separateByType) { window.simulation?.force("x").x(d => (d.foodCounterIndex * width/5)).strength(0.4); window.simulation?.force("y").strength(0.1); } else { window.simulation?.force('x', d3.forceX(width / 2).strength(0.1)) window.simulation?.force('y', d3.forceY(height / 2).strength(0.1)) } setTimeout(() => simulation.alphaTarget(0), 500); const markers = svg.selectAll('.marker') .data(valueByFoodType) .join('g') .attr('class', 'marker'); const tooltip = d3.select("body") .append("div") .attr("class", "tooltip") .style("position", "absolute") .style("z-index", "10") .style("visibility", "hidden") .style("background", "rgba(0, 0, 0, 0.6)") .style("color", "#fff") .style("padding", "10px") .style("border-radius", "4px"); function mouseover() { tooltip.style("visibility", "visible"); } function mousemove(event, d) { tooltip.html(`${d.name}
${d.value.toFixed(2)} kg CO2-eq/kg
Data samples: ${d.sampleCount}`) .style("left", (event.pageX + 10) + "px") .style("top", (event.pageY - 10) + "px"); } function mouseout() { tooltip.style("visibility", "hidden"); } markers.on("mouseover", mouseover) .on("mousemove", mousemove) .on("mouseout", mouseout); markers.append("circle") .attr('r', d => d[window.scaleAttribute] * scaleFactor / 2) .attr('cx', 0) .attr('cy', 0) .attr('fill', d => d.outlineColor) .attr("stroke", d => d.outlineColor) .attr("stroke-width", 4.5) .style("filter", "drop-shadow(1px 1px 4px rgba(0,0,0,0.3))"); markers.append('image') .attr('xlink:href', d => d.img) .attr('width', d => d[window.scaleAttribute] * scaleFactor) .attr('height', d => d[window.scaleAttribute] * scaleFactor) .attr('x', d => -d[window.scaleAttribute] * scaleFactor / 2) .attr('y', d => -d[window.scaleAttribute] * scaleFactor / 2) .attr('clip-path', 'circle(50%)') function drag(simulation) { function dragstarted(event, d) { if (!event.active) simulation.alphaTarget(0.1).restart(); d.fx = d.x; d.fy = d.y; } function dragged(event, d) { d.fx = event.x; d.fy = event.y; } function dragended(event, d) { if (!event.active) simulation.alphaTarget(0); d.fx = null; d.fy = null; } return d3.drag() .on("start", dragstarted) .on("drag", dragged) .on("end", dragended); } markers.call(drag(simulation)); function ticked() { markers.attr('transform', d => { if (!window.selectedSet.has(d.foodCounter)) { const targetX = centerX + (Math.cos(d.idx) * 1200); const targetY = centerY + (Math.sin(d.idx) * 1200); d.x = targetX; d.y = targetY; d.vx = (centerX - targetX) / 3; d.vy = (centerY - targetY) / 3; } return `translate(${d.x}, ${d.y})` }); markers.select("circle").attr('r', d => `${d[window.scaleAttribute] * scaleFactor / 2}`); markers.select("image") .attr('width', d => d[window.scaleAttribute] * scaleFactor) .attr('height', d => d[window.scaleAttribute] * scaleFactor) .attr('x', d => -d[window.scaleAttribute] * scaleFactor / 2) .attr('y', d => -d[window.scaleAttribute] * scaleFactor / 2) .attr('display', window.showImages); } simulation.on('tick', ticked); return svg.node() })()4a:T680,worldMap = { const plot = Plot.marks([ Plot.graticule(), Plot.geo(countries, { fill: (d) => samplesByCountry.get(d.properties.name) }), Plot.sphere(), ]).plot( {projection: {type: projectionType, rotate: globeRotation}, color: {scheme: "BuYlRd", unknown: "#ccc"}} ) // Initialize variables to store the initial mouse position and rotation values let mouseDown = false; let initialMouseX = 0; let initialMouseY = 0; let initialLambda = 0; let initialPhi = 0; let l=0, p=0; // Add event listeners to the plot container plot.addEventListener('mousedown', handleMouseDown); plot.addEventListener('mousemove', handleMouseMove); plot.addEventListener('mouseup', handleMouseUp); // Function to handle mouse down event function handleMouseDown(event) { mouseDown = true; initialMouseX = event.clientX; initialMouseY = event.clientY; initialLambda = globeRotation[0]; initialPhi = globeRotation[1]; } // Function to handle mouse move event function handleMouseMove(event) { if (mouseDown) { const deltaX = event.clientX - initialMouseX; const deltaY = event.clientY - initialMouseY; const lambdaChange = deltaX * 0.2; const phiChange = -deltaY * 0.2; l = parseFloat(initialLambda) + lambdaChange; p = parseFloat(initialPhi) + phiChange; } } // Function to handle mouse up event function handleMouseUp() { mouseDown = false; if (projectionType === "orthographic") { mutable globeRotation = [l, p, mutable globeRotation[2]] } else { mutable globeRotation = [l, 0, mutable globeRotation[2]] } } return plot; }4b:T4f3,{function dataString() { switch (dataToDisplay) { case "Emissions": return "The area of the circles is directly proportional to the emissions value."; case "Emissions (inverted)": return "This view inverts the mapping of the circle area, so lower emission values result in larger elements. It highlights foods with favorable emission values that would otherwise get lost in the visualization. The size mapping here is logarithmic to ensure a visible size difference within the low emission values."; case "Data Sample Count": return "The area of the circles is directly proportional to the amount of available data samples for the item."; default: return "Invalid data."; } } function limitString() { // console.log(sampleSettings); if (sampleSettings.includes("Limit number of samples")) { return "

Limiting the same amount of samples for each category ensures a much more accurate and representative size comparison of the average emission value of each group."; } else { return ""; } } let dataExplanation = ""; dataExplanation += dataString(); dataExplanation += limitString(); document.getElementById("dataExplanation").innerHTML = dataExplanation; return "Set data explanation string.";}4c:T8ec,valueByFoodType = { const result = data .reduce((acc, row) => { const { kgCo2, foodType, foodCounter } = row; const getImage = (name) => `https://whatsthetemperatureinvaliszimmer.com/${name.replaceAll("/", " ").replaceAll(" ", " ").replaceAll(" ", " ")}.jpg`; const index = acc.findIndex(i => i.name === foodType); if (index >= 0) { acc[index].count += 1; acc[index].value += kgCo2; } else { acc.push({ count: 1, value: kgCo2, name: foodType, foodCounter, img: getImage(foodType) }) } return acc; }, []) .map((entry, idx) => { const toIndex = (counter) => { switch (counter) { case "Fruit and Vegetables": return 1; case "Flours, Grains, Pulses and Nuts": return 2; case "Dairy": return 3; case "Meat": return 4; default: return 0; } } return { idx: Math.PI / 180 * (idx * (360 / 194)), name: entry.name, value: entry.value / entry.count, sampleCount: entry.count, sampleCountSize: Math.sqrt(entry.count)/20, size: Math.sqrt((entry.value / entry.count / 73.86) * Math.PI)/Math.PI, //invSize: (1 - Math.sqrt(Math.sqrt(Math.sqrt(Math.sqrt(Math.sqrt(Math.sqrt((entry.value / entry.count / 73.86) * Math.PI)/Math.PI))))))*2, // :( invSize: Math.abs(Math.log10((Math.sqrt((entry.value / entry.count) * Math.PI)/Math.PI/73.86)*10))*0.1, // :( outlineColor: foodCounterColorScale(entry.foodCounter), foodCounter: entry.foodCounter, foodCounterIndex : toIndex(entry.foodCounter), img: entry.img }; }); result.sort(() => (Math.random() > .5) ? 1 : -1) if (sampleSettings.includes("Limit number of samples")) { const found = { "Fruit and Vegetables": 0, "Flours, Grains, Pulses and Nuts": 0, "Dairy": 0, "Meat": 0 }; const limited = []; for (const entry of result) { if (found[entry.foodCounter] < 17) { limited.push(entry); found[entry.foodCounter]++; } } return limited; } else { return result; } } 4d:[] 29:{"id":"01d99556bd8a7116","slug":null,"title":"Team 4 - 🍌🦑 Co2-Equivalent Emissions for different Produce 🦞🍍","thumbnail":"b7076083fba8061df74c9595b31bb562246d2e1ef851369b7d4aa876623a44d7","access_level":"public","create_time":"$D2026-06-04T17:50:09.462Z","update_time":"$D2026-06-04T17:50:09.462Z","view_time":null,"first_public_time":"$D2026-06-04T17:50:09.462Z","has_unpublished_edits":null,"fork":{"id":"4ef4c00b06d9ea89","slug":null,"title":"Team 4 - 🍌🦑 Co2-Equivalent Emissions for different Produce 🦞🍍","owner":{"id":"4fbb25fe821f18db","login":"fhooe-infovis","name":"FHOÖ Information Visualization"}},"stars":0,"starred":false,"owner":{"type":"team","id":"b45221f6de3a5e60","login":"mold","name":"mold","avatar_url":"https://avatars-next.observableusercontent.com/5f80d4b185cdf10883c893de835afe4b7c90ba340143c5c2cba0e008514c7f17","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":1937,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"f19ad6a75c013286","login":"woife","name":"Wolfgang Schwendtbauer","avatar_url":"https://avatars-next.observableusercontent.com/4c33c7d53cc110ffae924fda2b08a163b59ab0d1aa6e4cedafc6a347a69ab970","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"b45221f6de3a5e60","login":"mold","name":"mold","avatar_url":"https://avatars-next.observableusercontent.com/5f80d4b185cdf10883c893de835afe4b7c90ba340143c5c2cba0e008514c7f17","tier":"starter_2024","role":"editor"},{"type":"individual","id":"f19ad6a75c013286","login":"woife","name":"Wolfgang Schwendtbauer","avatar_url":"https://avatars-next.observableusercontent.com/4c33c7d53cc110ffae924fda2b08a163b59ab0d1aa6e4cedafc6a347a69ab970","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Team 4 - 🍌🦑 Co2-Equivalent Emissions for different Produce 🦞🍍","cells":[{"id":0,"value":"# Team 4 - 🍌🦑 Co2-Equivalent Emissions for different Produce 🦞🍍\n\n## Wolfgang Schwendtbauer + Valentin Postl","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1378,"value":"#### The presented dataset is derived from an extensive meta-study on greenhouse gas emissions 🏭 resulting from the production of various types of produce 🌾. The values are expressed as a standardized measurement of CO2 equivalent emissions 🌬️ per kilogram of produce, encompassing the total lifecycle emissions for each specific type of produce 🔄.\nhttps://www.sciencedirect.com/science/article/pii/S0959652616303584#appsec1\n\n#### The cleaned up dataset 🧹 contains 1671 entries 📝 with 194 different foods 🍲 from 72 different countries 🌎, taken from 354 sources 🔍.\n\n#### The items are categorized into 4️⃣ overaching categories:\n

🥩 Meat
🌾 Flours, Grains, Pulses and Nuts
🍎 Fruit and Vegetables
🥛 Dairy

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1545,"value":"#### Let's take a peek at the data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1499,"value":"Plot.plot({\n caption: md`kg CO2-eq/kg produce, BFM or L after conversion per food type`,\n marks: [\n Plot.barY(co2ByType, {x: \"foodType\", y: \"kgCo2\", fill: d => foodCounterColorScale(d.foodCounter), sort: {x: \"y\", reverse: true}}),\n Plot.ruleY([0])\n ],\n x: {\n axis: null\n },\n color: {\n scale: \"foodCounter\",\n domain: [\"Meat\", \"Flours, Grains, Pulses and Nuts\", \"Fruit and Vegetables\", \"Dairy\"],\n range: [\"#b91509\", \"#ff8533\", \"#339966\", \"#5370c6\"],\n legend: true\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1550,"value":"#### The graph displays all data samples sorted by their CO2 emissions. The significant difference between the emission values is evident, with the values for Fruits and Vegetables being barely visible on the chart.\n\n#### Meats clearly have the highest emission values by far. The overall distribution of values is apparent, but it is challenging to fully comprehend the nature of the data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1564,"value":"## Let's take a more fun approach 🥰","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1573,"value":"#### 🔬 This physics-based visualization represents each food item as a circle 🟡 with the area proportional to the average CO2-equivalent emission value. Each item 🍎🍇🥕 is associated with a picture 📷 to represent it visually.\n\n#### 💡 The graph offers interactive functionality ✨, allowing users to drag the items around and explore the dataset intuitively. Various toggles ⚙️ enable different views for a more comprehensive exploration 👀👀👀\n\n#### 🔀 The inverted emissions value aims to emphasize foods with the lowest emission values ⬇️🌱 that might go unnoticed in the standard view. This mapping is displayed logarithmically 📐 to ensure that lower values, which are closer to each other, are distinguishable despite the wider range of larger values in the dataset.\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":586,"value":"viewof selectedCategories = Inputs.checkbox(uniqueCounters, {label: \"Categories\", value: uniqueCounters })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1129,"value":"viewof simulationSettings = {\n return Inputs.checkbox([\"Separate by Type\", \"Show Images\"], {label: \"Settings\", value: [\"Show Images\"]});\n} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1423,"value":"viewof sampleSettings = Inputs.checkbox([\"Limit number of samples\"], {label: \"Sample Settings\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1329,"value":"viewof dataToDisplay = Inputs.select([\"Emissions\", \"Emissions (inverted)\", \"Data Sample Count\"], {value: \"Size\", label: \"Data\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1754,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":234,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1614,"value":"#### 🔎 By limiting the number of samples, the number of items per category is reduced to 17 items, which corresponds to the category with the fewest unique food items (Dairy 🧈). The samples are chosen randomly. Now, when separating by category, the area of the island 🏝️ is much more representative and easier to understand in terms of the average emission value of each category. 🌿🍼🦬🥜\n\n#### It is of note that the dataset also considers dairy substitutes (such as almond Milk) to be within the dairy category. 🤦\n\n\n#### 📊 The Data Sample Count option displays the areas mapped to the number of data samples for each unique food item. However, not only is the quantity of data per sample essential, but also the origin of this data is of interest. 🔍🌍📊","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1933,"value":"### Available data samples per country\n\n#### This map shows the distribution of the data samples across the globe. It is of note, that not all samples in the dataset have a region entry and some of the entries only have the continent as location data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1182,"value":"viewof projectionType = Inputs.select([\"orthographic\" ,\"mercator\", \"conic-equal-area\", \"equal-earth\"], {value: \"orthographic\", label: \"Projection\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1913,"value":"Legend(d3.scaleSequential([0, maxSamplesByCountry], d3.interpolateYlOrRd), {\n title: \"Amount of data samples per country\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1624,"value":"#### In this map the color of each country is proportional to the amount of data entries in the dataset. It is evident that there is a significant bias towards Europe, with a higher concentration of data entries. However, it is notable that many African countries lack any data entries at all.😥","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":102,"value":"## Prepare data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":104,"value":"getValueByCountry = (country) => {\n return samplesByCountry[country.properties.name] ?? 0;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":203,"value":"mutable globeRotation = [0, 0, 0];","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1160,"value":"width = 1200","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1163,"value":"height = 800","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1719,"value":"#### Top Secret Hacks","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1256,"value":"{\n const projection = projectionType;\n if (projection !== \"orthographic\") {\n mutable globeRotation = [mutable globeRotation[0], 0, mutable globeRotation[2]];\n }\n\n return \"Globe rotation reset\";\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":970,"value":"{\n window.selectedSet = new Set(selectedCategories);\n window.simulation?.alphaTarget(0.2).restart();\n\n return \"Category toggle\";\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1125,"value":"{\n const settings = new Set(simulationSettings);\n const attribute = dataToDisplay;\n if (attribute === \"Emissions\") window.scaleAttribute = \"size\";\n if (attribute === \"Emissions (inverted)\") window.scaleAttribute = \"invSize\";\n if (attribute === \"Data Sample Count\") window.scaleAttribute = \"sampleCountSize\";\n\n window.showImages = simulationSettings.includes(\"Show Images\")? \"\" : \"none\";\n window.separateByType = simulationSettings.includes(\"Separate by Type\");\n\n if (settings.has(\"Separate by Type\")) {\n window.simulation?.force(\"x\").x(d => (d.foodCounterIndex * width/5)).strength(0.4);\n window.simulation?.force(\"y\").strength(0.1);\n } else {\n window.simulation?.force('x', d3.forceX(width / 2).strength(0.1))\n window.simulation?.force('y', d3.forceY(height / 2).strength(0.1))\n }\n window.simulation?.force(\"collide\").initialize(valueByFoodType);\n window.simulation?.alphaTarget(0.2).restart();\n\n return \"Simulation Settings\";\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1901,"value":"{\n selectedCategories;\n simulationSettings;\n dataToDisplay;\n\n window.resetTimer && clearTimeout(window.resetTimer);\n\n window.resetTimer = setTimeout(() => {\n // console.log(\"Resetting\");\n window.simulation?.alphaTarget(0)\n }, 1000);\n \n return \"Settle simulation after settings have changed\";\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1723,"value":"#### Top Secret Hacks End","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1743,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":589,"value":"uniqueCounters = new Set(data.reduce((acc, row) => {\n if (!acc.includes(row.foodCounter)) {\n acc.push(row.foodCounter);\n }\n return acc;\n}, []))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":683,"value":"maxCo2Value = valueByFoodType.reduce((max,row) => {\n if (max < row.value) {\n return row.value;\n }\n return max;\n}, 0) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":815,"value":"minCo2Value = valueByFoodType.reduce((min,row) => {\n if (min > row.value) {\n return row.value;\n }\n return min;\n}, Number.MAX_SAFE_INTEGER)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"mapCountryName = (name) => {\n if (name == \"United States\") return \"United States of America\"\n if (name == \"Czech Republic\") return \"Czechia\"\n return name\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":552,"value":"function foodCounterColorScale(subject) {\n switch (subject) {\n case \"Fruit and Vegetables\":\n return \"#339966\"; // Green\n case \"Flours, Grains, Pulses and Nuts\":\n return \"#ff8533\"; // Brown\n case \"Dairy\":\n return \"#5370c6\"; // Blue\n case \"Meat\":\n return \"#b91509\"; // Red\n default:\n return \"#000000\"; // Default color (black)\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":240,"value":"$4c","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1927,"value":"maxSamplesByCountry = {\n let max = 0;\n samplesByCountry.forEach(value => {\n if (value > max) {\n max = value;\n }\n })\n \n return max;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"samplesByCountry = new Map(\n Object.entries(\n data.reduce((acc, row) => {\n const { region, regionType, kgCo2 } = row;\n if (regionType !== \"Country\") return acc;\n \n const mappedRegion = mapCountryName(region);\n acc[mappedRegion] ??= 0;\n acc[mappedRegion] += 1;\n return acc;\n }, {})\n )\n);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"co2ByType = Object.entries(data.reduce((acc, current) => {\n const { foodCounter, foodType, kgCo2 } = current;\n\n if (!selectedCategories.includes(foodCounter)) return acc;\n \n acc[foodType] ??= { sum: 0, count: 0, foodCounter };\n acc[foodType].sum += kgCo2;\n acc[foodType].count++;\n return acc;\n}, {})).map(([foodType, { sum, count, foodCounter }]) => ({\n foodType,\n foodCounter,\n kgCo2: sum / count,\n}));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"data = raw_data.map((row) => {\n let kgCo2 = row[\"kg CO2-eq/kg produce, BFM or L after conversion\"]\n kgCo2 = parseFloat(kgCo2.replace(',','.')) || 0;\n\n // Map numbers to actual numbers instad of number strings\n // rename fields to more practical names which can be used in JS\n // remove empty column and \"Report type\" col\n return {\n foodCounter: row[\"Food counter\"],\n foodType: row[\"Food type\"],\n region: row[\"Region\"],\n regionType: row[\"Region Type\"],\n continent: row[\"Continent\"],\n population: parseInt(row[\"2022 Population\"]),\n subCategory: row[\"Sub-category\"],\n region: row[\"Region\"],\n yearOfStudy: parseInt(row[\"Year of study\"]),\n kgCo2\n }\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"raw_data = d3.csv(\"https://docs.google.com/spreadsheets/d/e/2PACX-1vR-SczoOmn5lJnLSRvUf-IVfF8Q1xOhUfDr9lFhps8EF35yX0e3NzRLGr61JlgljHDD8zqeMwHjt8q0/pub?gid=996108700&single=true&output=csv\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"import { countries50m as countries } from \"@visionscarto/geo\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1917,"value":"import { Legend } from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4d","stdlib":"1"}} 24:null