2b:null 2c:["$","title",null,{"children":"Ethnic Power Relations | Jesse Johnson's Workspace | Observable"}] 31:T11d9,leafletMap = { let container = DOM.element("div", { style: `width:${width}px;height:${width / 1.6}px` }); yield container; let map = L.map(container).setView([0, 0], 2.0); let osmLayer = L.tileLayer( "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { attribution: //'© OpenStreetMap contributors' "" } ).addTo(map); /// Additional Base Layers let world_imagry = L.tileLayer( "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}", { attribution: // "Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community", "", minZoom: 2, maxZoom: 17 } ); let world_topo = L.tileLayer( "https://server.arcgisonline.com/ArcGIS/rest/services/World_Topo_Map/MapServer/tile/{z}/{y}/{x}", { attribution: //"Tiles © Esri — Esri, DeLorme, NAVTEQ, TomTom, Intermap, iPC, USGS, FAO, NPS, NRCAN, GeoBase, Kadaster NL, Ordnance Survey, Esri Japan, METI, Esri China (Hong Kong), and the GIS User Community", "", minZoom: 2, maxZoom: 17 } ); let baseLayers = { "Open Street Maps": osmLayer, "ESRI World Imagery": world_imagry, "ESRI World Topo": world_topo }; var geojson; geojson = L.geoJSON(yearFilteredGeoepr, { onEachFeature: onEachFeature, style: function (feature) { return { fillColor: colorBy != "Religion" ? colorMaps[colorBy]["colors"][ colorMaps[colorBy]["dictionary"][feature.properties.gwgroupid] ] : colorMaps[colorBy]["colors"][ religionNumbers[feature.properties.gwgroupid] % 10 ], weight: 1, opacity: 1, color: "white", dashArray: "1", fillOpacity: 0.5 }; } }).addTo(map); function highlightFeature(e) { var layer = e.target; layer.setStyle({ weight: 3, color: "#666", dashArray: "", fillOpacity: 0.25 }); info.update(layer.feature.properties); } function resetHighlight(e) { geojson.resetStyle(e.target); info.update(); } function zoomToFeature(e) { map.fitBounds(e.target.getBounds()); e.target.bringToBack(); } function onEachFeature(feature, layer) { layer.on({ mouseover: highlightFeature, mouseout: resetHighlight, click: zoomToFeature }); } var info = L.control(); info.onAdd = function (map) { this._div = L.DomUtil.create("div", "info"); // create a div with a class "info" this.update(); return this._div; }; // method that we will use to update the control based on feature properties passed info.update = function (props) { this._div.innerHTML = "

" + String(year) + " World Ethnic Groups

" + (props ? "Ethnic Group: " + props.group + "
" + "Country : " + props.statename + "
" + "Political Status: " + powerDictionary[props.gwgroupid] + "
" + "Language: " + languageDictionary[props.gwgroupid] + "
" + "Religion: " + religionDictionary[props.gwgroupid] + "
" + "Phenotype: " + phenotypeDictionary[props.gwgroupid] + "
" + "Population: " + (sizeDictionary[props.gwgroupid] * 100).toPrecision(2) + "% of country." : "Hover over a region for data,
or click to zoom.
Some regions overlap, clicking swaps them."); }; info.addTo(map); map.addLayer(world_topo); var legend = L.control({ position: "bottomright" }); legend.onAdd = function (map) { var div = L.DomUtil.create("div", "info legend"); // loop through our density intervals and generate a label with a colored square for each interval if (colorMaps[colorBy]["grades"].length < 10) { for (var i = 0; i < colorMaps[colorBy]["grades"].length; i++) { div.innerHTML += ' ' + colorMaps[colorBy]["labels"][i] + "
"; } } else { div.innerHTML = "Too many lables
"; } return div; }; legend.addTo(map); }32:T9ec,## Definitions From the [EPR codebook](https://icr.ethz.ch/data/epr/core/EPR_2021_Codebook_EPR.pdf): **Monopoly:** Elite members hold monopoly power in the executive to the exclusion of members of all other ethnic groups. **Dominance:** Elite members of the group hold dominant power in the executive but there is some limited inclusion of “token” members of other groups who however do not have real influence on decision making. **Senior Partner:** Representatives of the group participate as senior partners in a formal or informal power-sharing arrangement. By power sharing, we mean any arrangement that divides executive power among leaders who claim to represent particular ethnic groups and who have real influence on political decision making. **Junior Partner:** Representatives participate as junior partners in government. **Powerless:** Elite representatives hold no political power (or do not have influence on decision making) at the national level of executive power - although without being explicitly discriminated against. **Discrimination:** Group members are subjected to active, intentional, and targeted discrimination by the state, with the intent of excluding them from political power. Such active discrimination can be either formal or informal, but always refers to the domain of public politics (excluding discrimination in the socio-economic sphere). **Self-exclusion:** The special category of self-exclusion applies to groups that have excluded themselves from central state power, in the sense that they control a particular territory of the state which they have declared independent from the central government. **Irrelevant:** We list groups that became irrelevant, meaning they were politically relevant at the state level in a previous period. We do not record groups a) that never sought political representation at the national level or never were discriminated, b) before they gain political relevance or c) after they emigrated. In countries where ethnicity is not politicized we list the largest group as irrelevant, such as the Germans in Germany. **State collapse:** State collapse refers to a situation “where no authority is recognizable either internally to a country’s inhabitants or externally to the international community.” We code as state collapse those situations where there is a vacuum of authority caused by a total disintegration of state institutions, which makes it impossible to code access to government, such as in Liberia or Sierra Leone during the 1990s.33:T508,function trait_per_capita(trait) { var dictionary = {}; if (trait == "Phenotype") { trait = phenoTypes; dictionary = phenotypeDictionary; } else if (trait == "Language") { trait = iso6393Dictionary; dictionary = languageDictionary; } else if (trait == "Religion") { trait = religions; dictionary = religionDictionary; } else { return null; } const populations = Object.entries(trait).map((p) => ({ group: p[1], total_gdp: yearFilteredEpr.reduce( (a, c) => countryProperties[c.statename] != undefined && p[1] == dictionary[c.gwgroupid] ? (a = a + countryProperties[c.statename]["gdp_md"] * c.size * 1e6) : (a = a), 0 ), total_pop: yearFilteredEpr.reduce( (a, c) => countryProperties[c.statename] != undefined && p[1] == dictionary[c.gwgroupid] ? (a = a + parseInt(countryProperties[c.statename]["pop_est"] * c.size)) : (a = a), 0 ) })); return populations .map((d) => ({ group: d.group, gdp_per_capita: isNaN(parseInt(d.total_gdp / d.total_pop)) ? -9999 : parseInt(d.total_gdp / d.total_pop) })) .sort((a, b) => { return b.gdp_per_capita - a.gdp_per_capita; }); }34:T713,colorMaps = { const colorMaps = { Power: { dictionary: powerDictionary, labels: [ "Irrelevant", "State Collapse", "Self-exclusion", "Powerless", "Discriminated", "Junior Partner", "Senior Partner", "Dominant", "Monopoly" ], grades: [ "IRRELEVANT", "STATE COLLAPSE", "SELF-EXCLUSION", "POWERLESS", "DISCRIMINATED", "JUNIOR PARTNER", "SENIOR PARTNER", "DOMINANT", "MONOPOLY" ], colors: { IRRELEVANT: "#a37c40", "STATE COLLAPSE": "#36F1CD", "SELF-EXCLUSION": "#39A0ED", POWERLESS: "#06070E", DISCRIMINATED: "#29524A", "JUNIOR PARTNER": "#e28413", "SENIOR PARTNER": "#de3c4b", DOMINANT: "#bcc1ba", MONOPOLY: "#fff9a5 " } }, Phenotype: { dictionary: phenotypeDictionary, labels: [ "Americans", "East Asians", "Europeans", "Middle Eastern & Northern African", "Oceanian", "South Asian", "Sub-Saharan African" ], grades: [ "Americans", "East Asians", "Europeans", "Middle Eastern & Northern African", "Oceanian", "South Asian", "Sub-Saharan African" ], colors: { Americans: "brown", "East Asians": "tan", Europeans: "pink", "Middle Eastern & Northern African": "olive", Oceanian: "purple", "South Asian": "orange", "Sub-Saharan African": "black" } }, Religion: { dictionary: religionDictionary, labels: religion_names, grades: religion_abbr, colors: d3.schemeCategory10 } }; return colorMaps; }35:[{"id":"4828d526e9e1b15fbefebe2d200bec1935bdcad33377ed7e7cce1df300045b9d142ac9b0211eb807755afc1c49227949982e58bb3d77c4030c9c7e6544e3f69e","href":"https://static.observableusercontent.com/files/4828d526e9e1b15fbefebe2d200bec1935bdcad33377ed7e7cce1df300045b9d142ac9b0211eb807755afc1c49227949982e58bb3d77c4030c9c7e6544e3f69e","name":"Country to ISO 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2f:{"id":"8e885ef265300e21","slug":null,"title":"Ethnic Power Relations","thumbnail":"a9022929c4da12f0f855916674b201c9718b1331246a02567d598335c962298f","access_level":"public","create_time":"$D2024-04-23T13:40:12.583Z","update_time":"$D2024-11-18T16:01:27.753Z","view_time":null,"first_public_time":"$D2024-04-23T13:56:55.264Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"518a19f15bc65c30","login":"jesse-johnson-ws","name":"Jesse Johnson's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/5f664e52e040271f29fdaa246480cfd9c714e2aec9109302c80d5b2af6ee0522","tier":"starter_2024"},"version":1366,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"42197f0a73bea471","login":"jesse-johnson","name":"Jesse 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dataset provides insight into the area of settlement, political status, dominant language, religion, and phenotype of ethnic groups from 1946 to 2021.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":219,"value":"viewof year = slider({\n min: 1948,\n max: 2021,\n step: 1,\n value: 1985,\n format: \"\",\n title: \"Year\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1082,"value":"viewof colorBy = Inputs.radio([\"Power\", \"Phenotype\", \"Religion\"], {\n value: \"Power\",\n label: \"Color Map By:\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1223,"value":"## Rank By\nRank phenotype, religion, or language by GPD per capita. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1227,"value":"viewof rankBy = Inputs.radio([\"Language\", \"Phenotype\", \"Religion\"], {\n value: \"Language\",\n label: \"GDP per Capita Rankings By:\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1230,"value":"Inputs.table(trait_per_capita(rankBy), {\n columns: [\"group\", \"gdp_per_capita\"],\n header: { group: rankBy, gdp_per_capita: \"GDP per Capita (2019 Dollars)\" },\n format: {\n Group: (d) => d.group,\n gdp_per_capita: sparkbar(\n d3.max(trait_per_capita(rankBy).map((d) => d.gdp_per_capita)),\n (d) => d.gdp_per_capita\n )\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":355,"value":"## Minority Rule\n\nConsider ethnic groups holding either monopoly or dominant power positions. Sort them by the fraction of the total population they constitute. The combination of low percentage of total population and political dominance appears to be a recipe for an unstable state. It would be interesting to take the lowest percentage states here and consider the political violence that resulted in the end of their rule. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":332,"value":"Inputs.table(minorityRule, {\n columns: [\"Group\", \"State\", \"End of Rule\", \"Percentage of Population\"],\n format: {\n \"Percentage of Population\": sparkbar(\n d3.max(minorityRule, (d) => d[\"Percentage of Population\"])\n ),\n \"End of Rule\": (d) => d3.timeFormat(\"%Y\")(new Date(d, 12, 1))\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":921,"value":"## Ethnic Entropy\n\nConsider the idea of entropy applied to ethnic groups within each state. It is accessible with this dataset, using the `size` field which is expressed as a fraction of the total population, . Think of this as a probability distribution. The classic formula for entropy can be applied to each nation as follows\n\nI then get the gross domestic product per person for each nation. These are 2019 data. I've plotted them below. Small gray dots are individual countries. Large red dots show the average within each bin.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":893,"value":"Plot.plot({\n grid: false,\n marginLeft: 60,\n x: {\n label: \"Ethnic Entropy →\"\n },\n y: {\n label: \"↑ GDP per Capita (2019 dollars)\",\n type: \"linear\"\n },\n marks: [\n Plot.dot(eeBins, {\n x: (d) => d[\"total_entropy\"] / d[\"count\"],\n y: (d) => (d[\"total_gdp\"] / d[\"total_population\"]) * 1e6,\n fill: \"red\"\n }),\n Plot.dot(ethnicEntropy, {\n x: \"entropy\",\n y: \"gdp_percapita\",\n fill: \"gray\",\n r: 2,\n channels: { Nation: (d) => d.state },\n tip: true\n }),\n Plot.ruleX(d3.range(0, 2.6, 0.2)),\n Plot.ruleY([0, 115000])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1357,"value":"## Ideas/To dos\n\n* It'd be interesting to see what forms of power sharing provide the best economic outcomes. Do power sharing arrangements actually work?\n* Need to bring in the other EPR dataset, the one that spans national borders. That's a good dataset and will alter some outcomes.\n* Still need to do language on the main map.\n* Clean up the hover on display to highlight the map highlight, eg religion is highlighted in the info box when the map is based on religion.\n* Make definitions easier to hop to. Add definitions for phenotype and possibly religions.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":431,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":384,"value":"## Filters\nTrying to squeeze some other interesting information out of this dataset with filtering and reducing. Can bring in national level data for GDP and population in 2019 from the `national_boundaries` dataset. Joins on these end up being complicated by the non-standard naming and numbering conventions used in EPR.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1186,"value":"$33","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":645,"value":"ethnicEntropy = countries.map((d) => ({\n state: d,\n entropy: yearFilteredEpr.reduce(\n (a, c) =>\n d == c.statename ? (a = a + -c.size * Math.log(c.size)) : (a = a),\n 0\n ),\n gdp: parseInt(countryProperties[d][\"gdp_md\"]),\n gdp_percapita: parseInt(\n (countryProperties[d][\"gdp_md\"] / countryProperties[d][\"pop_est\"]) * 1e6\n ),\n population: parseInt(countryProperties[d][\"pop_est\"])\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":843,"value":"myBinFunction = d3\n .bin()\n .thresholds(10)\n .value((d) => {\n return d.entropy;\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":836,"value":"ethnicEntropyBinned = myBinFunction(ethnicEntropy)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":866,"value":"eeBins = ethnicEntropyBinned.map((d) => ({\n total_entropy: d.reduce((a, c) => (a = a + c.entropy), 0),\n total_population: d.reduce((a, c) => (a = a + c.population), 0),\n total_gdp: d.reduce((a, c) => (a = a + c.gdp), 0),\n count: d.reduce((a, c) => (a = a + 1), 0)\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":251,"value":"## File imports\nThe files that create the ethnic boundaries and the status of groups.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"geoepr2021 = FileAttachment(\"GeoEPR-2021.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"ed2021 = FileAttachment(\"ED-2021.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"iso6393 = FileAttachment(\"iso639-3.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"epr2021 = FileAttachment(\"EPR-2021.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"national_boundaries = FileAttachment(\"national_boundaries.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":468,"value":"countryToIsoCode = await FileAttachment(\"Country to ISO Code.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"countryToIsoCode","database":"$undefined"},{"id":480,"value":"countryISODictionary = Object.fromEntries(\n countryToIsoCode.map((d) => [d.statename, d[\"iso 3166-1\"]])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"## Filtered data\nFor placement in code or just experimentation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":477,"value":"// create a dictionary with keys of country name (EPR name) and\n// values being all properties from national_boundaries dataset access like this:\n// countryProperties[\"Canada\"][\"pop_est\"]\n\ncountryProperties = Object.fromEntries(\n countries.map((d) => [\n d,\n national_boundaries.features.filter(\n (nd) => nd.properties.iso_n3_eh == countryISODictionary[d]\n )[0].properties\n ])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":663,"value":"countries = [...new Set(yearFilteredEpr.map((item) => item.statename))]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"// Filter on slider value year and sort so that big regions appear under smaller ones, allowing hoverover to be free of occlusions.\nyearFilteredGeoepr = geoepr2021.features\n .filter((d) => d.properties.from <= year && d.properties.to >= year)\n .sort((a, b) => {\n return b.properties.sqkm - a.properties.sqkm;\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":590,"value":"// Filter on slider value year and sort so that big regions appear under smaller ones, allowing hoverover to be free of occlusions.\nyearFilteredEpr = epr2021.data.filter((d) => d.from <= year && d.to >= year)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"minorityRule = epr2021.data\n .filter((d) => d.status == \"MONOPOLY\" || d.status == \"DOMINANT\")\n .sort((a, b) => {\n return a.size - b.size;\n })\n .map((d) => ({\n State: d.statename,\n Group: d.group,\n \"Percentage of Population\": d.size * 100,\n \"End of Rule\": d.to\n }))\n .reduce((unique, o) => {\n if (!unique.some((obj) => obj.State === o.State)) {\n unique.push(o);\n }\n return unique;\n }, [])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":322,"value":"minorityRule","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":247,"value":"## Dictionaries\nThese allow quick translation of ``gwgroupid`` to the corresponding status, such as religion, language, or power.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":100,"value":"sizeDictionary = Object.fromEntries(\n epr2021.data\n .filter((d) => d.from <= year && d.to >= year)\n .map((d) => [d.gwgroupid, d.size])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"powerDictionary = Object.fromEntries(\n epr2021.data\n .filter((d) => d.from <= year && d.to >= year)\n .map((d) => [d.gwgroupid, d.status])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"iso6393Dictionary = Object.fromEntries(iso6393.map((d) => [d.code, d.name]))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"languageDictionary = Object.fromEntries(\n ed2021.data.map((d) => [d.gwgroupid, iso6393Dictionary[d.language1]])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":114,"value":"religions = FileAttachment(\"religions_dictionary.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1135,"value":"religion_names = Object.values(religions)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1144,"value":"religion_abbr = Object.keys(religions)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1177,"value":"religion_numbers = Object.fromEntries(religion_abbr.map((d, i) => [d, i]))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"religionDictionary = Object.fromEntries(\n ed2021.data.map((d) => [d.gwgroupid, religions[d.religion1]])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1174,"value":"religionNumbers = Object.fromEntries(\n ed2021.data.map((d) => [d.gwgroupid, religion_numbers[d.religion1]])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"phenotypeDictionary = Object.fromEntries(\n ed2021.data.map((d) => [d.gwgroupid, phenoTypes[d.phenotype1]])\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"phenoTypes = {\n const phenoTypes = {\n ame: \"Americans\",\n eas: \"East Asians\",\n eur: \"Europeans\",\n men: \"Middle Eastern & Northern African\",\n oce: \"Oceanian\",\n sas: \"South Asian\",\n ssa: \"Sub-Saharan African\"\n };\n return phenoTypes;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"## Miscellaneous \nIncludes a style segment that applies important CSS stuff to the visual","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":22,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":330,"value":"function sparkbar(max) {\n return (x) => htl.html`

${x.toLocaleString(\"en\")}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"import { slider } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1017,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 30:"$2f:body"