29:null 2a:["$","title",null,{"children":"Second Exercise on Observable: Classification and Colors | Eric Thomas | 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":"7f63cd60bd780fb581d9a0869fc0fa88e96e3b42d72a56a1b4dd371f15cfb3cd74b48884b9b96cade4aec2653338f9a9a264ff95ed638e2327f10a3350c6ce82","href":"https://static.observableusercontent.com/files/7f63cd60bd780fb581d9a0869fc0fa88e96e3b42d72a56a1b4dd371f15cfb3cd74b48884b9b96cade4aec2653338f9a9a264ff95ed638e2327f10a3350c6ce82","name":"Alaska.json","type":"application/json","lastModified":1676671910489.577,"size":25543},{"id":"cebcca49f80541efc6820907fd1b3096d5c95574ddbbdac4d57fe6bc342d89026ccd851d16cc186eee115565d800dfe8cb3589069d3d4dd16fdcbcc75db6dd3b","href":"https://static.observableusercontent.com/files/cebcca49f80541efc6820907fd1b3096d5c95574ddbbdac4d57fe6bc342d89026ccd851d16cc186eee115565d800dfe8cb3589069d3d4dd16fdcbcc75db6dd3b","name":"Alaska@1.csv","type":"text/csv","lastModified":1676671910489.577,"size":8290},{"id":"bda86c11d2a6f8fa14c0e0242eb5d9a33ef67896f5eb10d3a70a6af84145e2b1afba368747c7c9de07e8aa0686524202dc8cb993c840aa0df23d21b43d262084","href":"https://static.observableusercontent.com/files/bda86c11d2a6f8fa14c0e0242eb5d9a33ef67896f5eb10d3a70a6af84145e2b1afba368747c7c9de07e8aa0686524202dc8cb993c840aa0df23d21b43d262084","name":"MI_FoodIns.csv","type":"text/csv","lastModified":1676671981123.476,"size":1792767},{"id":"0ca53fd2fc4734acd1b662d79ab8f18759617a61c8607f613ed128f3d93c248f67340d100ac1803d055a62e8a3874894af50f528ab3f2ff1ff47a198de1c2194","href":"https://static.observableusercontent.com/files/0ca53fd2fc4734acd1b662d79ab8f18759617a61c8607f613ed128f3d93c248f67340d100ac1803d055a62e8a3874894af50f528ab3f2ff1ff47a198de1c2194","name":"MI_Counties.json","type":"application/json","lastModified":1676671981085.043,"size":1825523}] 2d:{"id":"1a476a00b694a712","slug":null,"title":"Second Exercise on Observable: Classification and Colors","thumbnail":"f42776a687315d5c06a785399c78f1db72a0a9697dffce4fe05af7356047fed8","access_level":"public","create_time":"$D2023-02-17T22:11:50.489Z","update_time":"$D2023-03-01T03:47:54.071Z","view_time":null,"first_public_time":"$D2023-02-17T22:11:50.489Z","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":"8439e4da5850f0a6","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":"starter_2024"},"version":569,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}],"members":[{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null,"role":"editor"}],"comments":[{"id":"07b9245ff73a8fd7","reply_to_id":null,"content":"The Name of this Notebook!","create_time":"$D2023-02-17T23:32:34.242Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"c1f767ce2ae68a27","reply_to_id":null,"content":"Introduces the d3 library to the notebook","create_time":"$D2023-02-17T23:32:55.160Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"82533721372acb44","reply_to_id":null,"content":"Introduces topojson-client to the notebook","create_time":"$D2023-02-17T23:33:32.962Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"e63e8e4d5f82efb7","reply_to_id":"07b9245ff73a8fd7","content":"In order to add a file to this notebook, click the paperclip icon on the right side of the web page. After that, click the '+' icon next to the bolded File attachments line.","create_time":"$D2023-02-21T04:11:22.387Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"6aa0cc47b27c20ce","reply_to_id":null,"content":"This imports the .json file attachment and assigns a variable to it. \n- The file is \"MI_Counties.json\n- The variable assigned is MI_Counties","create_time":"$D2023-02-21T04:12:30.803Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"733c8787e60ef51d","reply_to_id":null,"content":"This creates a feature class from the polygons in the .json file you just added.\nWithin the parentheses, it is (FILE VARIABLE, directory to specific array)\n- The variable we are looking in is MI_Counties\n- The features we are extracting are in MI_Counties > objects > MI_Counties","create_time":"$D2023-02-21T04:15:24.070Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"bda7a3042e01b4c9","reply_to_id":null,"content":"This extracts the specific csv data you are interested in from your attached csv file. \nd3.csvParse extracts the data and after the => is what you want to do with the data.\n\n- d3.csvParse(await FileAttachment(\"MI_FoodIns.csv\").text(),({FIPS, TractSNAP, Pop2010})\nadds the columns FIPS, TractsSNAP, and Pop2010 to the code. \n- [FIPS, +TractSNAP/+Pop2010]) \nuses the columns you imported to create new columns/arrays within your codes dataset.","create_time":"$D2023-02-21T04:19:25.684Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"29a674cc1b98c21e","reply_to_id":null,"content":"This creates objects for the data you created. \nOn top of bringing in the data like the last step, there is an additional section:\n(({FIPS, TractSNAP, Pop2010}) => ({FIPS: +FIPS, pctSNAP: +TractSNAP/+Pop2010}))\nThis assigns a name to each of the values you created.\nFIPS for the FIPS code. pctSNAP for the TractSNAP/Pop2010 variable.","create_time":"$D2023-02-21T04:24:40.707Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"b2b6de53953e6423","reply_to_id":null,"content":"This is the bar graph of the data. It is counting the number of variables on the x-axis that fit within certain bins (a range) within the graph. \n- Plot.binX({y: \"count\"} \nCreates a count on the y axis for...\n- {x: \"pctSNAP\", \nThe pct of population qualifying for SNAP benefits on the X axis within...\n-thresholds: bins})),\nThe set range of each datapoint","create_time":"$D2023-02-21T04:34:11.466Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"c1f997481b87bb4a","reply_to_id":null,"content":"creates an easily understandable array from our CSV data that we imported.\n- d => d[1]\ncreates a single array from the specific nested array we want \"pctSNAP\"","create_time":"$D2023-02-21T04:37:52.502Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"64213f3e7cc98d83","reply_to_id":null,"content":"This assigns a title to the CSV data we imported.","create_time":"$D2023-02-21T04:40:17.236Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"162b43d786e7663a","reply_to_id":null,"content":"This creates a linear unclassed function of the previously created array (pctSNAP) and assigns a color to the range as the domain varies","create_time":"$D2023-02-21T04:41:50.866Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"e32e826b30d0d1a4","reply_to_id":null,"content":"This is graphing the function created in the previous line of code in a linear fashion.","create_time":"$D2023-02-21T04:42:29.712Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"063036e688b91001","reply_to_id":null,"content":"This creates a function from our created array and uses a quantile classification. Each color code [\"#e0f3db\", \"#a8ddb5\", \"#43a2ca\"] corresponds with a different range of the data.","create_time":"$D2023-02-21T04:44:06.158Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"9d0c8a5c34b1e9b9","reply_to_id":null,"content":"This numerically sorts the data and adds the color scheme created with the previous line of code.","create_time":"$D2023-02-21T04:44:56.222Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"9550fdb9fd968859","reply_to_id":null,"content":"This uses our created array to compute a classification with natural breaks. The 3 indicates there are three groups in the classification.","create_time":"$D2023-02-21T04:46:50.273Z","update_time":"$D2023-02-21T04:47:59.106Z","resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"2350bbe835c1f738","reply_to_id":null,"content":"This assigns colors to our natural breaks classification created in the previous line of code. Each grouping is assigned a color value.","create_time":"$D2023-02-21T04:48:27.314Z","update_time":"$D2023-02-21T04:49:21.602Z","resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"278d3a5a62f888be","reply_to_id":null,"content":"This graphs the function and classification made in the two previous lines of code in a linear fashion","create_time":"$D2023-02-21T04:49:56.034Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"fe241b4150a6fe97","reply_to_id":null,"content":"This creates a function using the original array we created. \nThe domain is created with the minimum and maximum values of the array\n- .domain([d3.min(pctSNAP),d3.max(pctSNAP)])\nThe range is set by the number of colors you provide, in this case, it is three.","create_time":"$D2023-02-21T04:51:44.734Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"2f0f202fb428604c","reply_to_id":null,"content":"This is the graph of the equal interval data","create_time":"$D2023-02-21T04:52:02.264Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"0f52f77e0cef9f2f","reply_to_id":null,"content":"This creates a graph of our created array and sections it off into user-created groups. \nIn this example, the groups are divided between 0.02, 0.4, and 2.5","create_time":"$D2023-02-21T04:57:51.630Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"9807ca22dd2f3d74","reply_to_id":null,"content":"This shows the entire number line of the array using all of the graph classifications we created.","create_time":"$D2023-02-21T04:59:08.670Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"3e8a2f754466821c","reply_to_id":null,"content":"this adds a library that creates a simple Jenks natural break algorithm.","create_time":"$D2023-02-21T05:00:50.706Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"f9d60deea3905357","reply_to_id":"9807ca22dd2f3d74","content":"HISTOGRAM COMMENT: The histogram of this data set is heavily skewed toward zero with one outlier at 2.5%. This is because there is a very small percentage of people that qualify for SNAP benefits. The one outlier may be a census block that has a relatively high rate of poverty.","create_time":"$D2023-02-21T05:06:48.922Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}},{"id":"0682fc487e43f3bb","reply_to_id":"b2b6de53953e6423","content":"COMMENT ON HISTOGRAM:\nThis histogram is heavily skewed towards zero. This is because of a single outlier that has 2.5% of the census block's population on SNAP benefits.\n\nThis outlier will heavily skew my scales.\n- The unclassed scheme from Min - Max creates a very light-colored data set other than the highest value\n- In the quantile classification, it creates the illusion that there are a higher amount of census blocks with a higher percentage of SNAP users than there actually are. \n- The natural breaks classification creates an understandable dataset, with the outlier(s) highlighted. \n- In the equal interval because the outlier skews the data so much, the split down the middle of the data divides the classes into non-outliers and the outlier. The outlier skews the data so much, the middle class of the three has no values in it. \n- For threshold I was able to choose my own values, they split the 'normal' data well and isolate the outlier as well.","create_time":"$D2023-03-01T03:46:12.165Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"9fc5cbeb4e527300","login":"enthomas18","name":"Eric Thomas","avatar_url":"https://avatars-next.observableusercontent.com/c8981eb4ac458e5dc256f495cd29d0d120febf983b0c2061b79e2533ba0a7680","tier":null}}],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Second Exercise on Observable: Classification and Colors","cells":[{"id":0,"value":"md`# Second Exercise on Observable: Classification and Colors`","pinned":true,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":10,"value":"MI_Counties = FileAttachment(\"MI_Counties.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":12,"value":"county_features = topojson.feature(MI_Counties, MI_Counties.objects.MI_Counties)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":18,"value":"csv_data = d3.csvParse(await FileAttachment(\"MI_FoodIns.csv\").text(),({FIPS, TractSNAP, Pop2010}) => [FIPS, +TractSNAP/+Pop2010])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":400,"value":"csv_data_objects = Object.assign((d3.csvParse(await FileAttachment(\"MI_FoodIns.csv\").text(), d3.autoType)).map(({FIPS, TractSNAP, Pop2010}) => ({FIPS: +FIPS, pctSNAP: +TractSNAP/+Pop2010})))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":415,"value":"viewof bins = Inputs.range([0,1000 ], {step: .1, label: \"Bins\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":394,"value":"Plot.plot({\n marks: [\n Plot.rectY(csv_data_objects, Plot.binX({y: \"count\"}, {x: \"pctSNAP\", thresholds: bins})),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":450,"value":"d3.median(pctSNAP)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":25,"value":"pctSNAP = Array.from(csv_data.values(), d => d[1])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":28,"value":"data = Object.assign(new Map(csv_data), {title: [\"Percent of Population using SNAP Benefits\"]})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":174,"value":"md`# Linear Scale (Unclassed)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":242,"value":"linear = d3.scaleLinear()\n .domain(d3.extent(pctSNAP))\n .range([\"white\", \"green\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":199,"value":"chart(numericSort(pctSNAP), linear)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":267,"value":"md`# Quantile Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":272,"value":"quantile = d3.scaleQuantile()\n .domain(pctSNAP)\n .range([\"#e0f3db\", \"#a8ddb5\", \"#43a2ca\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":274,"value":"chart(numericSort(pctSNAP), quantile)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":323,"value":"md`# Jenks Natural Breaks Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":373,"value":"naturalbreaks = simple.ckmeans(pctSNAP, 3).map(v => v.pop())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":319,"value":"jenks = d3\n .scaleThreshold()\n .domain(naturalbreaks)\n .range([\"#e7e1ef\", \"#c994c7\", \"maroon\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":330,"value":"chart(numericSort(pctSNAP), jenks)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":280,"value":"md`# Equal Interval Classification (Quantize)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":276,"value":"quantize = d3.scaleQuantize()\n .domain([d3.min(pctSNAP),d3.max(pctSNAP)])\n .range([\"#fde0dd\", \"#fa9fb5\", \"#c51b8a\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":282,"value":"chart(numericSort(pctSNAP), quantize)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":295,"value":"md`# Threshold`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":289,"value":"threshold = d3.scaleThreshold()\n .domain([0.1, 1, 2])\n .range([\"#efedf5\", \"#bcbddc\", \"#756bb1\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":297,"value":"chart(numericSort(pctSNAP), threshold)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":355,"value":"showScaleGrouping(pctSNAP, {\n scaleQuantile: quantile,\n scaleThreshold: threshold,\n scaleJenks: jenks,\n scaleQuantize: quantize,\n scaleQuantizeNice: quantize.copy().nice()\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":201,"value":"function chart(data, scale) {\n const w = 10,\n cols = Math.floor(Math.min(800, width) / w),\n lines = Math.ceil(2800 / cols);\n const chart = d3\n .create(\"svg\")\n .attr(\"width\", cols * w)\n .attr(\"height\", lines * w);\n\n chart\n .append(\"g\")\n .attr(\"transform\", \"translate(2,2)\")\n .attr(\"style\", \"stroke:black; fill:white;\")\n .selectAll(\"rect\")\n .data(data)\n .join(\"rect\")\n .attr(\"width\", w - 3)\n .attr(\"height\", w - 3)\n .attr(\"x\", (_, i) => w * (i % cols))\n .attr(\"y\", (_, i) => w * ((i / cols) | 0))\n .style(\"fill\", d => (scale ? scale(d) : \"#ddd\"));\n return chart.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$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"