29:null 2a:["$","title",null,{"children":"Assignment 2 Grubbs: Classification and Colors | Caitlyn Grubbs | Observable"}] 2f:T5a4,function showScaleGrouping(data, scales) { const margins = { left: 130 }; const x = d3 .scaleLinear() .domain(d3.extent(data)) .range([margins.left, width - 5]); const r = 3; const rectHeight = 10; const chart = svg` `; return chart; }30:[{"id":"ea68d88f5589ef942b7e3b3f7497da160bc2882125aab30c22f6ed3d91f534c24cbd571250783a403aacca33032426948dcc9c99b3dbc0e8748d306a5b00e59b","href":"https://static.observableusercontent.com/files/ea68d88f5589ef942b7e3b3f7497da160bc2882125aab30c22f6ed3d91f534c24cbd571250783a403aacca33032426948dcc9c99b3dbc0e8748d306a5b00e59b","name":"Ohio_counties_1.json","type":"application/json","lastModified":1676668239012.124,"size":98641},{"id":"109e649be9a2e27a16e9842384d3bd8dd26f33ede3085a77f1143a887869d6844c7226e9304bbc062feb6354feefd82b03144794e40965b6e020ed0256ddb940","href":"https://static.observableusercontent.com/files/109e649be9a2e27a16e9842384d3bd8dd26f33ede3085a77f1143a887869d6844c7226e9304bbc062feb6354feefd82b03144794e40965b6e020ed0256ddb940","name":"OhioCounties-1@1.csv","type":"text/csv","lastModified":1676668239012.124,"size":36082}] 2d:{"id":"019028933bb55e3b","slug":null,"title":"Assignment 2 Grubbs: Classification and Colors","thumbnail":"cd56ff6d3f5ac58cc23d23d5fc54ebdd8015b3e781e0c5b1b83244e009e43791","access_level":"public","create_time":"$D2023-02-17T21:10:39.012Z","update_time":"$D2023-03-01T02:03:16.565Z","view_time":null,"first_public_time":"$D2023-02-17T21:10:39.012Z","fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"0aa3594635541487","login":"csgrubbs","name":"Caitlyn Grubbs","avatar_url":"https://avatars-next.observableusercontent.com/9e5e79ae430db86b3a0e5381e25d05ec880bd4430d3c3de1942ff8260210aea3","tier":"starter_2024"},"version":499,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"02ee44fb0826af91","login":"csgrubbs","name":"Caitlyn Grubbs","avatar_url":"https://avatars-next.observableusercontent.com/9e5e79ae430db86b3a0e5381e25d05ec880bd4430d3c3de1942ff8260210aea3","tier":null}],"members":[{"type":"individual","id":"02ee44fb0826af91","login":"csgrubbs","name":"Caitlyn Grubbs","avatar_url":"https://avatars-next.observableusercontent.com/9e5e79ae430db86b3a0e5381e25d05ec880bd4430d3c3de1942ff8260210aea3","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Assignment 2 Grubbs: Classification and Colors","cells":[{"id":0,"value":"md`# Assignment 2 Grubbs: Classification and Colors`","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":"ohio_counties = FileAttachment(\"Ohio_counties_1.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":12,"value":"county_features = topojson.feature(ohio_counties, ohio_counties.objects.Ohio_counties_1)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":18,"value":"csv_data = d3.csvParse(await FileAttachment(\"OhioCounties-1@1.csv\").text(),({FIPS, Num_Rural, Population}) => [+FIPS,+Num_Rural/+Population])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":400,"value":"csv_data_objects = Object.assign((d3.csvParse(await FileAttachment(\"OhioCounties-1@1.csv\").text(), d3.autoType)).map(({FIPS, Num_Rural, Population}) => ({FIPS: +FIPS, pctRural: +Num_Rural/+Population})))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":415,"value":"viewof bins = Inputs.range([0, 20], {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: \"pctRural\", thresholds: bins})),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":481,"value":"The histogram of the percentage of rural residents per county shows the distribution across the counties in Ohio in my data. It is unimodal and reasonably normally distributed with a few exceptions at the high and low ends of the data. This means I have no natural dividing point in my data.\n\nBecause the data is a percentage of rural residents in each county, the range should be from 0 to 1, however, there were two counties that had a higher than 1 ratio because the data had more rural residents than it did the population. This is a discrepancy in the data. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":25,"value":"Ruralpct = 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 Ohio: Rural\"]})","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(Ruralpct))\n .range([\"#ffffd4\", \"#8c2d04\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":199,"value":"chart(numericSort(Ruralpct), 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(Ruralpct)\n .range([\"#ffffd4\", \"#fed98e\", \"#fe9929\", \"#cc4c02\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":274,"value":"chart(numericSort(Ruralpct), 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(Ruralpct, 4).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([\"#edf8fb\", \"#b2e2e2\", \"#66c2a4\", \"#238b45\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":330,"value":"chart(numericSort(Ruralpct), 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(Ruralpct),d3.max(Ruralpct)])\n .range([\"#f2f0f7\", \"#cbc9e2\", \"#9e9ac8\", \"#6a51a3\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":282,"value":"chart(numericSort(Ruralpct), 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([0.25, 0.5, 0.75])\n .range([\"#eff3ff\", \"#bdd7e7\", \"#6baed6\", \"#2171b5\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":485,"value":"This is how create a manual classification method. I chose the breaks of 0.25, 0.5, and 0.75 since the mean and median were about 0.5 and the data is reasonably normally distributed. This allowed me to classify my data equally in the four quartiles. (I do have data that is above 1 as described above, so it is not a perfect equal interval classification.) Breaking the range from 0-1 into four classes, it easy for the user to understand since it is common to see population density measures in very low, low, high, and very high density so I can apply this to the rural residency measure.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":297,"value":"chart(numericSort(Ruralpct), threshold)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":355,"value":"showScaleGrouping(Ruralpct, {\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 = 20,\n cols = Math.floor(Math.min(600, width) / w),\n lines = Math.ceil(200 / 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"