29:null 2a:["$","title",null,{"children":"Assignment #2, ecountryman | Countryman, Eian | Observable"}] 2f:T5a4,function showScaleGrouping(data, scales) { const margins = { left: 130 }; const x = d3 .scaleLinear() .domain(d3.extent(data)) .range([margins.left, width - 5]); const r = 3; const rectHeight = 10; const chart = svg` `; return chart; }30:[{"id":"1f9440a09e13283444551cda8cbc56ab3bf1a32c81a8fc2838d30b73555af335ad9593a66a550dc8a227a499fabdaf5ea00279ff424d0137e358f77c23967631","href":"https://static.observableusercontent.com/files/1f9440a09e13283444551cda8cbc56ab3bf1a32c81a8fc2838d30b73555af335ad9593a66a550dc8a227a499fabdaf5ea00279ff424d0137e358f77c23967631","name":"DECENNIALPL2020.P1-Data.csv","type":"text/csv","lastModified":1676961161597.905,"size":43549},{"id":"582f648a20dd6a31b6316496e3bd6f26da2b8d3ee1060077594176993ea163bb4c794049d32515120854c4cc05e993b407755e546dd6540f29ea38811e51fef8","href":"https://static.observableusercontent.com/files/582f648a20dd6a31b6316496e3bd6f26da2b8d3ee1060077594176993ea163bb4c794049d32515120854c4cc05e993b407755e546dd6540f29ea38811e51fef8","name":"tl_2016_53_cousub.json","type":"application/json","lastModified":1676961172253.751,"size":2536957}] 2d:{"id":"9ee944e08800a006","slug":null,"title":"Assignment #2, ecountryman","thumbnail":"79bd98701a2f295c27a6895ea6316e862db1cb473b8b530aef08e9ea7dae92a9","access_level":"public","create_time":"$D2023-02-21T04:07:30.216Z","update_time":"$D2023-02-28T23:16:10.048Z","view_time":null,"first_public_time":"$D2023-02-21T04:07:30.216Z","fork":{"id":"c68fb87dffeeb9bc","slug":"second-exercise-on-observable","title":"Second Exercise on Observable: Classification and Colors","owner":{"id":"22130e106df70095","login":"geoviz","name":"Geographic Visualization (GEOG 3540) - University of Iowa"}},"stars":0,"starred":false,"owner":{"type":"team","id":"a6450b8113fc64e9","login":"ecountryman","name":"Countryman, Eian ","avatar_url":"https://avatars-next.observableusercontent.com/71e33126deac679283b00a95302babf4acf525938d8d096599a46e0451848fbb","tier":"starter_2024"},"version":572,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"34f43699b8220c9e","login":"ecountryman","name":"Countryman, Eian ","avatar_url":"https://avatars-next.observableusercontent.com/71e33126deac679283b00a95302babf4acf525938d8d096599a46e0451848fbb","tier":null}],"members":[{"type":"individual","id":"34f43699b8220c9e","login":"ecountryman","name":"Countryman, Eian ","avatar_url":"https://avatars-next.observableusercontent.com/71e33126deac679283b00a95302babf4acf525938d8d096599a46e0451848fbb","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Assignment #2, ecountryman","cells":[{"id":0,"value":"md`# Assignment #2, ecountryman`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6,"value":"topojson = require(\"topojson-client@3\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":10,"value":"Counties = FileAttachment(\"tl_2016_53_cousub.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":12,"value":"counties_features = topojson.feature(Counties, Counties.objects.tl_2016_53_cousub)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":18,"value":"csv_data = d3.csvParse(await FileAttachment(\"DECENNIALPL2020.P1-Data.csv\").text(),({GeographicAreaName, TotalWhite, TotalPop}) => [GeographicAreaName, +TotalWhite/+TotalPop])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":400,"value":"csv_data_objects = Object.assign((d3.csvParse(await FileAttachment(\"DECENNIALPL2020.P1-Data.csv\").text(), d3.autoType)).map(({GeographicAreaName, TotalWhite, TotalPop}) => ({GeographicAreaName: GeographicAreaName, pctWhite: +TotalWhite/+TotalPop})))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":415,"value":"viewof bins = Inputs.range([0, 45], {step: 1, label: \"Bins\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":394,"value":"Plot.plot({\n marks: [\n Plot.rectY(csv_data_objects, Plot.binX({y: \"count\"}, {x: \"pctWhite\", thresholds: bins})),\n Plot.ruleY([0])\n ]\n})\n// This histogram shows the raw distrobution of the data ranging from ~40% to ~95%. There was no data below 40% and above ~95% so it is not displayed. The x-axis shows the percentage of population to be white while the y-axis shows the frequency of that percentage, or how many times that percentage is present as a count. The range 0-45 was chosen because the data contained 41 values so if a larger range was chosen then the data would be much harder to interpret with the breaks appearing much larger than they really are.","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":25,"value":"whitepct = Array.from(csv_data.values(), d => d[1])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":28,"value":"data = Object.assign(new Map(csv_data), {title: [\"Percent Population of Washington that is White\"]})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":174,"value":"md`# Linear Scale (Unclassed)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":242,"value":"linear = d3.scaleLinear()\n .domain(d3.extent(whitepct)) \n .range([\"white\", \"orange\"])\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":199,"value":"chart(numericSort(whitepct), linear)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":267,"value":"md`# Quantile Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":272,"value":"quantile = d3.scaleQuantile()\n .domain(whitepct)\n .range([\"white\", \"#9ecae1\", \"#3182bd\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":274,"value":"chart(numericSort(whitepct), quantile)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":323,"value":"md`# Jenks Natural Breaks Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":373,"value":"naturalbreaks = simple.ckmeans(whitepct, 3).map(v => v.pop())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":319,"value":"jenks = d3\n .scaleThreshold()\n .domain(naturalbreaks)\n .range([\"white\", \"#99d594\", \"green\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":330,"value":"chart(numericSort(whitepct), jenks)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":280,"value":"md`# Equal Interval Classification (Quantize)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":276,"value":"quantize = d3.scaleQuantize()\n .domain([d3.min(whitepct),d3.max(whitepct)])\n .range([\"#efedf5\", \"#bcbddc\", \"#756bb1\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":282,"value":"chart(numericSort(whitepct), quantize)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":295,"value":"md`# Threshold`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":289,"value":"threshold = d3.scaleThreshold()\n .domain([0.65, 0.8, 1]) // These values were used to display low, medium, and high values from the Histogram\n .range([\"#e5f5e0\", \"#99d594\", \"green\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":297,"value":"chart(numericSort(whitepct), threshold)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":355,"value":"showScaleGrouping(whitepct, {\n scaleQuantile: quantile,\n scaleThreshold: threshold,\n scaleJenks: jenks,\n scaleQuantize: quantize,\n scaleQuantizeNice: quantize.copy().nice()\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":129,"value":"md`# Annex`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":261,"value":"function numericSort(x) {\n return (\n x\n // ensure the array is not changed in-place\n .slice()\n // comparator function that treats input as numeric\n .sort((a, b) => a - b)\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":201,"value":"function chart(data, scale) {\n const w = 25,\n cols = Math.floor(Math.min(600, width) / w),\n lines = Math.ceil(100 / cols);\n const chart = d3\n .create(\"svg\")\n .attr(\"width\", cols * w)\n .attr(\"height\", lines * w);\n\n chart\n .append(\"g\")\n .attr(\"transform\", \"translate(2,2)\")\n .attr(\"style\", \"stroke:black; fill:white;\")\n .selectAll(\"rect\")\n .data(data)\n .join(\"rect\")\n .attr(\"width\", w - 3)\n .attr(\"height\", w - 3)\n .attr(\"x\", (_, i) => w * (i % cols))\n .attr(\"y\", (_, i) => w * ((i / cols) | 0))\n .style(\"fill\", d => (scale ? scale(d) : \"#ddd\"));\n return chart.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":313,"value":"simple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":357,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W30","stdlib":"1","titling":"auto"}} 2e:"$2d:body"