2b:null 2c:["$","title",null,{"children":"Noise Solution Overview | David Chung | Observable"}] 31:[{"id":"427b7e657a33305560324e5e492800dc404c9c808fe0dfbece7e4daeba35d905cdb3bb7487b19ba0641132cd07c592444e3c7f771f8b9d876e687e2b04d43188","href":"https://static.observableusercontent.com/files/427b7e657a33305560324e5e492800dc404c9c808fe0dfbece7e4daeba35d905cdb3bb7487b19ba0641132cd07c592444e3c7f771f8b9d876e687e2b04d43188","name":"Noise Solution VFSG report FINA.csv","type":"text/csv","lastModified":1723901930064.162,"size":27886}] 2f:{"id":"96c6d6b3213f0fba","slug":null,"title":"Noise Solution Overview","thumbnail":"dc50a91b955bc346604cee5d6451efc3ac970bca9ed58e8d61d399a3655630db","access_level":"public","create_time":"$D2024-08-17T13:37:47.749Z","update_time":"$D2024-08-18T00:36:31.463Z","view_time":null,"first_public_time":"$D2024-08-17T20:46:24.509Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"c6c194ae539fe1b6","login":"davidmchung123","name":"David Chung","avatar_url":"https://avatars-next.observableusercontent.com/7a790a48b01de24d496bc40861e79d0fa2453f577c980a3b466fff235ddb1361","tier":"starter_2024"},"version":867,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"9d2e4251d43434ca","login":"davidmchung123","name":"David Chung","avatar_url":"https://avatars-next.observableusercontent.com/7a790a48b01de24d496bc40861e79d0fa2453f577c980a3b466fff235ddb1361","tier":null}],"members":[{"type":"individual","id":"9d2e4251d43434ca","login":"davidmchung123","name":"David Chung","avatar_url":"https://avatars-next.observableusercontent.com/7a790a48b01de24d496bc40861e79d0fa2453f577c980a3b466fff235ddb1361","tier":null,"role":"editor"},{"type":"team","id":"c6c194ae539fe1b6","login":"davidmchung123","name":"David Chung","avatar_url":"https://avatars-next.observableusercontent.com/7a790a48b01de24d496bc40861e79d0fa2453f577c980a3b466fff235ddb1361","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Noise Solution Overview","cells":[{"id":0,"value":"# Noise Solution Overview","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"First lets look at the distribution of the data.\nComments, Likes, and Posts are all Left Skewed.\nStarting and End Scores are uniformly distributed.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"Looks like the average outcome is uniform with it showing mostly minor improvements.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":816,"value":"viewof gender = Inputs.select([\"All\",\"Male\",\"Female\"], {value: \"All\", label: \"Gender\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"Plot.plot({\n marks: [\n Plot.rectY(n, Plot.binX({y: \"count\"}, {x: \"ScoreDiff\", fill:\"blue\", fillOpacity:0.6, tip:true})),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"Engagement continues to be left skewed even when accounting for comments and likes per post. Very few posts get more than 2 comments and likes. Which suggests the program mainly benefits the individual rather than the organizations. This is done after sessions are concluded and have no impact on outcomes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":32,"value":"Plot.plot({\n title: \"Social Media Post Frequency to Engagement\",\n marks: [\n Plot.rectY(n, Plot.binX({y: \"count\"}, {x: \"Engagement\", fill:\"blue\", fillOpacity:0.6, tip:true})),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"Now let's look at outcomes per quintile. Looking at outcomes of all participates the bottom 20% have had slightly negative results. While on average for the remaining it has been mostly positive. The top 20% show the largest improvements. But on average it is mainly an improvement with some seeing little to no improvement.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":71,"value":"Plot.plot({\n marks: [\n Plot.barY(quintilesDiff, {x: \"quintile\", y: \"difference\", fill:\"blue\", fillOpacity:0.6, tip:true, sort: {x: \"y\"}}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"Now lets look at the quintiles for starting points and how they impact the ending points. To start off we see that the majority of individuals are categorized as belonging to the Low Wellbeing Band. Making up approximately 60% of individuals. With Medium and High Wellbeing only account for 20% each respectively.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":108,"value":"Plot.plot({\n title:\"Averages for each Start Score Quintile\", \n x: {\n padding:0.33\n },\n y: {\n domain:[0,30]\n }, \n marks: [\n Plot.barY(quintiles, {x: \"quintile\", y: \"value\", sort: {x: \"y\"}, fill: (d) => getColor(d.value), fillOpacity: 0.8, tip:true}),\n Plot.ruleY([0]),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":179,"value":"When we calculate the quintiles for end scores we see that on average the program improved wellbeing in almost all quintiles. So the bottom scores area improving and the top ones too. However, how does this match on an individual level? And how big are the jumps? We know that around 20% of participants experienced a negative outcome with another 20% experienced little to no change.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":173,"value":"Plot.plot({\n title:\"Averages for each End Score Quintile\",\n x: {\n padding:0.33\n },\n y: {\n domain:[0,30]\n },\n marks: [\n Plot.barY(quintilesEnd, {x: \"quintile\", y: \"value\", sort: {x: \"y\"}, fill: (d) => getColor(d.value), fillOpacity: 0.8, tip:true}),\n Plot.ruleY([0]),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":659,"value":"For the bottom 40% we see a trend of mostly increases in scored from start to finish of the program. Gradually we see that the number of people who's outlooks decrease increases with higher initial scores i.e. those between the 60-80 and 80-100 mark are more likely to experience no change or negative outlooks. But even from 60 onward people tend to have positive experiences.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":668,"value":"Plot.plot({\n title:\"Start Score Quintiles to End Score Difference\",\n height:400,\n padding: 0,\n x: {\n grid: true,\n domain: [-20, 25] \n },\n y:{ grid: true},\n color: {scheme: \"YlGnBu\", legend:true},\n marks: [Plot.rect(subsets, Plot.binX({fill: \"proportion-facet\"}, {x: \"ScoreDiff\", fy: \"Quintile\", inset: 0.5, tip:true}))]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":768,"value":"Looking at outcomes by Industry we see that for most cases we see an overall increase in wellbeing. However, outcomes are not always equal as some individual in the upper quarter may shift downward while those in the bottom group see an increase. This decrease is not inherently due to the program itself but possibly other factors in their personal lives as these trends tend to be outliers.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":389,"value":"## Distribution of Scores by Industry","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":325,"value":"\n \n \n \n

\n ${startPlot}\n

\n ${endPlot}\n

\n \n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"## Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":82,"value":"quintiles= calculateQuintiles(start).map(function(d,i) {\n let x = {\n value: d,\n quintile: 20 * (i + 1) + \"%\"\n };\n return x;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":170,"value":"quintilesEnd = calculateQuintiles(end).map(function(d,i) {\n let x = {\n value: d,\n quintile: 20 * (i + 1) + \"%\"\n };\n return x;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"quintilesDiff = calculateQuintiles(diff).map(function(d,i) {\n let x = {\n difference: d,\n quintile: 20 * (i + 1) + \"%\"\n };\n return x;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":163,"value":"end = n.filter(function(d) {\n if (d[\"SWEMWBS End Score\"] !== NaN) {\n return d[\"SWEMWBS End Score\"];\n }\n}).map((d) => d[\"SWEMWBS End Score\"]);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":429,"value":"subsets = createSubsets(sorted, 5).flatMap((subset, index) => subset.map(item => ({ ...item, Quintile: 20 * (index + 1) })));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":423,"value":"sorted = n.filter(function(d) {\n if (d[\"SWEMWBS Start Score\"] !== NaN && d[\"SWEMWBS End Score\"] !== NaN) {\n return d;\n }\n}).sort((a, b) => a[\"SWEMWBS Start Score\"] - b[\"SWEMWBS Start Score\"]);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"start = n.filter(function(d) {\n if (d[\"SWEMWBS Start Score\"] !== NaN) {\n return d[\"SWEMWBS Start Score\"];\n }\n}).map((d) => d[\"SWEMWBS Start Score\"]);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"diff = n.filter(function(d) {\n if (d.ScoreDiff !== NaN) {\n return d.ScoreDiff;\n }\n}).map((d) => d.ScoreDiff);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"n.map(function(d) {\n d.Engagement = (d[\"Comments\"] + d[\"Likes\"]) / d[\"Posts\"]\n return d;\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"n.map(function(d) {\n d.ScoreDiff = d[\"SWEMWBS End Score\"] - d[\"SWEMWBS Start Score\"]\n return d;\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":197,"value":"n = removeDuplicateIds(noise).filter(function(d) {\n if (d.Gender === gender || gender === \"All\") {\n return d;\n }\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"function getColor(value) {\n const band = bands.find(b => value >= b.start && value <= b.end);\n return band ? colorScale(band.start) : null; // Handle out-of-range values\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"bands = [\n { name: 'Low', start: 7, end: 21.54 },\n { name: 'Moderate', start: 21.55, end: 23.21 },\n { name: 'High', start: 23.22, end: 35 }\n];","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"\ncolorScale = d3.scaleLinear()\n .domain(bands.map(band => band.start))\n .range(d3.schemeSet1);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"function calculateQuintiles(data) {\n const sortedData = data.slice().sort((a, b) => a - b);\n const n = sortedData.length;\n\n if (n === 0) return []; // Handle empty array\n\n const quintileSize = Math.floor(n / 5);\n const remainder = n % 5;\n\n const quintiles = [];\n let start = 0;\n for (let i = 0; i < 4; i++) {\n const end = start + quintileSize + (i < remainder ? 1 : 0);\n quintiles.push(\n sortedData.slice(start, end).reduce((acc, val) => acc + val, 0) / (end - start)\n );\n start = end;\n }\n quintiles.push(\n sortedData.slice(start).reduce((acc, val) => acc + val, 0) / (n - start)\n );\n\n return quintiles;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":194,"value":"function removeDuplicateIds(arr) {\n return arr.reduce((acc, item) => {\n if (!acc.some(obj => obj.UIN === item.UIN)) {\n acc.push(item);\n }\n return acc;\n }, []);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":427,"value":"function createSubsets(sortedArray, numSubsets) {\n const subsetSize = Math.floor(sortedArray.length / numSubsets);\n const extra = sortedArray.length % numSubsets;\n const subsets = [];\n\n let index = 0;\n for (let i = 0; i < numSubsets; i++) {\n const size = subsetSize + (i < extra ? 1 : 0);\n subsets.push(sortedArray.slice(index, index + size));\n index += size;\n }\n\n return subsets;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":854,"value":"countPostalCodes(noise.map((d) => d.Postcode))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":846,"value":"function countPostalCodes(postalCodes) {\n const counts = {};\n\n postalCodes.forEach(code => {\n const prefix = code.substring(0, 2);\n counts[prefix] = (counts[prefix] || 0) + 1;\n });\n\n return counts;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"noise = await FileAttachment(\"Noise Solution VFSG report FINA.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"noise","database":"$undefined"},{"id":210,"value":"# Test","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":216,"value":"groupedData = d3.group(n, d => d[\"Participant: Industry\"]);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":258,"value":"industry_data = [...groupedData.entries()].map(([industry, industryData]) => industryData)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":270,"value":"industry = [...groupedData.entries()].map(([industry, industryData]) => industry !== \"\" ? industry : \"Unknown\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":291,"value":"startPlot = industry_data.map(function(d,i) { \n return Plot.plot({\n height:225,\n y: {\n domain: [0, 45]\n },\n x: {\n domain: [0, 40]\n },\n title: industry[i], // Add the title here\n marks: [\n Plot.rectY(industry_data[i], Plot.binX({y: \"count\"}, {x: \"SWEMWBS Start Score\", tip: true, fill: (d) => getColor(d[\"SWEMWBS Start Score\"]), fillOpacity: 0.8})),\n Plot.ruleY([0])\n ]\n }) \n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":321,"value":"endPlot = industry_data.map(function(d,i) { \n return Plot.plot({\n height:225,\n y: {\n domain: [0, 45]\n },\n x: {\n domain: [0, 40]\n },\n title: industry[i], // Add the title here \n marks: [\n Plot.rectY(industry_data[i], Plot.binX({y: \"count\"}, {x: \"SWEMWBS End Score\", tip:true, fill: (d) => getColor(d[\"SWEMWBS End Score\"]), fillOpacity: 0.8})),\n Plot.ruleY([0])\n ]\n }) \n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W31","stdlib":"1","titling":"auto"}} 30:"$2f:body"