25:["$","title",null,{"children":"Second Exercise on Observable: Classification and Colors | Bret Barschak | Observable"}] 49: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; }4a:[{"id":"7f63cd60bd780fb581d9a0869fc0fa88e96e3b42d72a56a1b4dd371f15cfb3cd74b48884b9b96cade4aec2653338f9a9a264ff95ed638e2327f10a3350c6ce82","href":"https://static.observableusercontent.com/files/7f63cd60bd780fb581d9a0869fc0fa88e96e3b42d72a56a1b4dd371f15cfb3cd74b48884b9b96cade4aec2653338f9a9a264ff95ed638e2327f10a3350c6ce82","name":"Alaska.json","type":"application/json","lastModified":1677026265791.426,"size":25543},{"id":"cebcca49f80541efc6820907fd1b3096d5c95574ddbbdac4d57fe6bc342d89026ccd851d16cc186eee115565d800dfe8cb3589069d3d4dd16fdcbcc75db6dd3b","href":"https://static.observableusercontent.com/files/cebcca49f80541efc6820907fd1b3096d5c95574ddbbdac4d57fe6bc342d89026ccd851d16cc186eee115565d800dfe8cb3589069d3d4dd16fdcbcc75db6dd3b","name":"Alaska@1.csv","type":"text/csv","lastModified":1677026265791.426,"size":8290},{"id":"c0e8b806c4efa51b5e3fb60e366b9c6a7f8af4126c55cd7f6f07768ee15776003dd7247dc5ff622eb40e588ebb8659311f1f3f7c58f012499dd62e009f4de61d","href":"https://static.observableusercontent.com/files/c0e8b806c4efa51b5e3fb60e366b9c6a7f8af4126c55cd7f6f07768ee15776003dd7247dc5ff622eb40e588ebb8659311f1f3f7c58f012499dd62e009f4de61d","name":"geo_export_b8c4fd47-13d7-4f58-b1ad-aba80e36b8ad (1).json","type":"application/json","lastModified":1677026287336.243,"size":478954},{"id":"9808bc7ffc926cd1bb7eaf1151c20a28e7f5dc590efa7112fb88ee59ba24dfbde8ff3658646a793192733ad2fdfd638e74c626f5810fd987916b9b899335c83a","href":"https://static.observableusercontent.com/files/9808bc7ffc926cd1bb7eaf1151c20a28e7f5dc590efa7112fb88ee59ba24dfbde8ff3658646a793192733ad2fdfd638e74c626f5810fd987916b9b899335c83a","name":"2022(3) MO County health(as2).csv","type":"text/csv","lastModified":1677026295088.485,"size":86905}] 29:{"id":"b89c5e13a8d23c95","slug":null,"title":"Second Exercise on Observable: Classification and Colors","thumbnail":"469b9ddc23b57f445d684c770679cbbe2d9ebce50eaf4e066256f642cc85fff8","access_level":"public","create_time":"$D2023-02-22T00:37:45.791Z","update_time":"$D2023-02-27T17:37:40.309Z","view_time":null,"first_public_time":"$D2023-02-22T00:37:45.791Z","has_unpublished_edits":null,"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":"dc7a3d4cff7b4c1f","login":"bbarschak","name":"Bret Barschak","avatar_url":"https://avatars-next.observableusercontent.com/c2fad3b14573398bdc4202d8b02fe1ec3756aae1feb630c7b8034faf5aab2479","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":563,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"622a5a735cdc4017","login":"bbarschak","name":"Bret Barschak","avatar_url":"https://avatars-next.observableusercontent.com/c2fad3b14573398bdc4202d8b02fe1ec3756aae1feb630c7b8034faf5aab2479","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"622a5a735cdc4017","login":"bbarschak","name":"Bret Barschak","avatar_url":"https://avatars-next.observableusercontent.com/c2fad3b14573398bdc4202d8b02fe1ec3756aae1feb630c7b8034faf5aab2479","tier":null,"role":"editor"},{"type":"individual","id":"60e9af384dfce94c","login":"caglarkoylu","name":"Caglar Koylu","avatar_url":"https://avatars-next.observableusercontent.com/94b0c40bf8a6b04057088ea171864451b3e3b27b7346d866d2a7be21d077be76","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":513,"value":"This is a d3 library that gives us a base library for our formats and commands. The d3 being set to equal is creating a variable. This library is important because without it we can not execute the creation of our Linear Scales, Quantile classification, Jenks Natural breaks Classification, Quantize, and Threshold grid charts.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"topojson = require(\"topojson-client@3\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":515,"value":"This Topojson variable is importing a topojson library. This library is need if you ever want to use topojson.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":423,"value":"GeoMissouri = FileAttachment(\"geo_export_b8c4fd47-13d7-4f58-b1ad-aba80e36b8ad (1).json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":517,"value":"This is the GeoMissouri variable that holds all of the data from our topojson. We can access this topojson with the help of the topojson library that we imported a line above. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":18,"value":"csv_data = d3.csvParse(await FileAttachment(\"2022(3) MO County health(as2).csv\").text(),({FIPS, NumDeaths , Population}) => [FIPS, +NumDeaths/+Population])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":520,"value":"This is our JavaScript formula for understanding how our Csv file is interpreted. As well as interpretation it helps make a base formula that we use for the rest of the exercise. The focused data rows that I'm using are FIPS which is the Federal Information Processing Standard that gives the county a designation. Next is the Number of deaths that have occurred in the given Missouri counties. Number of Deaths will be divided by Population to determine the percentage of people in the county that had died. The data formula I created is a normalized data set because there is a significance when talking about death percentages within a county.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":400,"value":"csv_data_objects = Object.assign((d3.csvParse(await FileAttachment(\"2022(3) MO County health(as2).csv\").text(), d3.autoType)).map(({FIPS, NumDeaths,Population }) => ({FIPS: +FIPS, PercentofDeadPopulation: +NumDeaths/+Population})))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":522,"value":"After the data is extracted above we can now name the variables. FIPS is just named FIPS to avoid any confusion. Number of deaths divided by Population is is going to be called ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":415,"value":"viewof bins = Inputs.range([0, 20], {step: 1, label: \"Bins\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":524,"value":"This is the Bin output for our bar graph below. The bins in the bar graph are controlled by a quick input variable that creates a sliding bar that ranges from 0 to 20. This is set 0 to 20 to keep a normal amount of bins in the graph. If there were no cap there could be an infinite number of bins and that would make a chart that is completely unreadable. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":394,"value":"Plot.plot({\n marks: [\n Plot.rectY(csv_data_objects, Plot.binX({y: \"count\"}, {x: \"PercentofDeadPopulation\", thresholds: bins})),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":526,"value":"As you can see with this chart There is a large peak in the middle and it slopes down to the sides. This means it is a unimodal, partially symmetrical graph. There is a bit of an increase in the death percentage from .026 to .028. This histogram graph is created using our CSV data variable and a sorting tool that places each percentage in the given bin. It does this by putting it through the thresholds for the bins. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":25,"value":"Deathperpop = Array.from(csv_data.values(), d => d[1])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":528,"value":"This array data shows exactly all of the percentages that came from the deaths divided by population.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":28,"value":"data = Object.assign(new Map(csv_data), {title: [\"Percent of Dead population\"]})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":530,"value":"This Data variable projects the new percentages onto the FIPS that they are with. This now gives the Counties an exact total and they only have to look for the FIPS to find the percentage instead of rooting through a bunch of data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":174,"value":"md`# Linear Scale (Unclassed)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":242,"value":"linear = d3.scaleLinear()\n .domain(d3.extent(Deathperpop))\n .range([\"white\", \"green\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":532,"value":"An unclassed map is created by using a simple linear scale and the percentages we gathered. The domain is set so that while we continue through the data a new box is made and set to a color. The range of colors is distinguished above in the range variable. You can see that the two colors used are White and Green. This is used to create a contrast through the whole grid that makes each square at least a little different from the next. The unclassed Scale has no exact measurement and doesn't show any numerical value.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":199,"value":"chart(numericSort(Deathperpop), linear)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"md`# Quantile Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":541,"value":"The quantile classification is a method that takes and separates the set of values, in this case they are death percentages, into classified groups that have an equal number of values. These values are then distributed as equally as possible to create a division between all given classes. In this case we have three equal classes that contain different percentages but the cutoffs made between the percentages is seen with the change of color from white to light green and light green to dark green. The domain for this function is the set of values given which is our CSV data and more specifically the data we have used to create the death percentage variable. This data is set through the threshold and categorized from largest to smallest percentage points.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":272,"value":"quantile = d3.scaleQuantile()\n .domain(Deathperpop)\n .range([\"white\", \"lightgreen\", \"darkgreen\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":274,"value":"chart(numericSort(Deathperpop), quantile)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":323,"value":"md`# Jenks Natural Breaks Classification`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":373,"value":"naturalbreaks = simple.ckmeans(Deathperpop, 3).map(v => v.pop())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":319,"value":"jenks = d3\n .scaleThreshold()\n .domain(naturalbreaks)\n .range([\"white\", \"orange\", \"darkred\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":543,"value":"Jenks Natural Breaks creates natural breaks in the data. After seeing the given data it is analyzed by an algorithm and is given a determined cutoff point to show the most consistent and natural break. The domain for this function is the set of values given which is our CSV data and more specifically the data we have used to create the death percentage variable. This data is set through the threshold and categorized from largest to smallest percentage points.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":330,"value":"chart(numericSort(Deathperpop), jenks)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":280,"value":"md`# Equal Interval Classification (Quantize)`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":546,"value":"For the data type Equal Interval Classification we end up looking at a equally separated data class by number. The exact middle is found between values this time which is why we see the spike in the middle of the red data values. Like the histogram the red values are bunched in the middle where most of the data settles. The domain for this function is the set of values given which is our CSV data and more specifically the data we have used to create the death percentage variable. This data is set through the threshold and categorized from largest to smallest percentage points.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":276,"value":"quantize = d3.scaleQuantize() // this is for equal interval\n .domain([d3.min(Deathperpop),d3.max(Deathperpop)])\n .range([\"#white\", \"red\", \"purple\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":282,"value":"chart(numericSort(Deathperpop), quantize)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":295,"value":"md`# Threshold`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":500,"value":"d3.min(Deathperpop)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":503,"value":"d3.mean(Deathperpop)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":506,"value":"d3.max(Deathperpop)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":548,"value":"For Threshold I made three points to test and take a look at the minimum, median, and maximum to set the data. the main point of interest was the median since the min and max were to be included anyways. I did a simple variable find using d3 to see what each of those values were and then i rounded them to make a cleaner looking table. This shows how thresholds are made. For such classifications like Jenks. The domain for this function is the set of values from our death percentage variable that I hand selected. This data is set through the threshold and categorized from largest to smallest percentage points.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":289,"value":"threshold = d3.scaleThreshold()\n .domain([0.01, 0.017, 0.027])\n .range([\"white\", \"yellow\", \"purple\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"chart(numericSort(Deathperpop), threshold)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":550,"value":"Data Spread Chart","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":559,"value":"This data spread sheet shows the difference in each of the different classifications. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":355,"value":"showScaleGrouping(Deathperpop, {\n scaleQuantile: quantile,\n scaleThreshold: threshold,\n scaleJenks: jenks,\n scaleQuantize: quantize,\n scaleQuantizeNice: quantize.copy().nice()\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"md`# Annex`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":561,"value":"The Annex is all extra data points and libraries that didn't sit within the notebook format.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":261,"value":"function numericSort(x) {\n return (\n x\n // ensure the array is not changed in-place\n .slice()\n // comparator function that treats input as numeric\n .sort((a, b) => a - b)\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"simple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":357,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":420,"value":"moCountyHealthAs2 = await FileAttachment(\"2022(3) MO County health(as2).csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"moCountyHealthAs2","database":"$undefined"}],"files":"$W4a","stdlib":"1"}} 24:null