2d:null 2e:["$","title",null,{"children":"2023 NSW Greens Election Analysis | Jack Caperon | Observable"}] 33:T702,plotDeckMap = ({ electionData, divisionGeoJson, getArrowLength, showNegativeSwings, scaleBooth }) => { electionData = showNegativeSwings ? electionData : electionData.filter((d) => d.Swing > 0); const iconLayer = new deck.IconLayer({ id: `icon-layer-${Date.now()}`, data: electionData.objects(), pickable: true, // iconAtlas and iconMapping are required iconAtlas: FileAttachment("arrow@1.png").url(), iconMapping: { arrow: { x: 0, y: 0, width: 128, height: 128, mask: true } }, getIcon: (d) => "arrow", sizeScale: 2, getPosition: (d) => [d.Longitude, d.Latitude], getAngle: (d) => (Math.sign(d.Swing - Math.abs(d.Swing)) * 180), getSize: (d) => // We want arrows to be all the same size across all maps getArrowLength( scaleBooth ? Math.abs(d.Swing) * d.OrdinaryVotes : Math.abs(d.Swing) ), getColor: getPartyColor }); const geojsonLayer = new deck.GeoJsonLayer({ id: "geojson-layer", data: divisionGeoJson, opacity: 1, filled: false, stroked: true, wireframe: true, lineWidthMinPixels: 2 }); const layers = [iconLayer, geojsonLayer]; const container = html`

`; // https://deck.gl/docs/api-reference/core/deckgl const deckgl = new deck.DeckGL({ container, map: mapboxgl, mapboxAccessToken: "", mapboxApiAccessToken: "pk.eyJ1IjoibWF4Ym8iLCJhIjoiY2lsd2JnZDRyMDFxNHZna3MwZDZmN2R0ZCJ9.32XWATCazaiDzdW6bXvBxw", mapStyle: "mapbox://styles/mapbox/light-v10", layers, initialViewState: { longitude: 146, latitude: -32, zoom: 5 }, controller: true, getTooltip: ({ object }) => object && getTooltip(object) }); deckgl.setProps({ layers }); return container; 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31:{"id":"d272663a0afd95d5","slug":"2023-nsw-greens-election-analysis","title":"2023 NSW Greens Election Analysis","thumbnail":"108d8d91a6983856dd822d099af2e8ee7e807388664cb742e812de16f29700df","access_level":"public","create_time":"$D2022-06-30T07:15:03.155Z","update_time":"$D2022-07-01T05:09:13.109Z","view_time":null,"first_public_time":"$D2022-06-30T08:37:36.500Z","has_unpublished_edits":null,"fork":{"id":"64faaf59581cecfb","slug":"2019-2022-australian-federal-election-swings","title":"2019/2022 Australian federal election House of Representatives polling booth swings","owner":{"id":"cc48459e4293f942","login":"mjbo","name":"Max Bo"}},"stars":1,"starred":false,"owner":{"type":"team","id":"d24bb2ff9556d4dc","login":"jdcaperon","name":"Jack Caperon","avatar_url":"https://avatars-next.observableusercontent.com/b1e41b301bb52aeeffa13f134b3c6735b4d25dbcabde3f999e8b380aa17d71f6","tier":"starter_2024"},"version":2283,"publish_version":2283,"role":null,"authored":false,"authors":[{"type":"individual","id":"fe552fca829a8ad1","login":"jdcaperon","name":"Jack Caperon","avatar_url":"https://avatars-next.observableusercontent.com/b1e41b301bb52aeeffa13f134b3c6735b4d25dbcabde3f999e8b380aa17d71f6","tier":null}],"members":[{"type":"individual","id":"fe552fca829a8ad1","login":"jdcaperon","name":"Jack Caperon","avatar_url":"https://avatars-next.observableusercontent.com/b1e41b301bb52aeeffa13f134b3c6735b4d25dbcabde3f999e8b380aa17d71f6","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"2023 NSW Greens Election Analysis","cells":[{"id":0,"value":"# 2023 NSW Greens Election Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":753,"value":"This notebook attempts to provide insights into the 2022 NSW State Election for the NSW Greens.\n\nThis notebook attempts to visualize individual polling booth swings of the [2019](https://en.wikipedia.org/wiki/2019_Australian_federal_election) and [2022](https://en.wikipedia.org/wiki/2022_Australian_federal_election) Australian federal elections with respect to the NSW state boundaries. \n\nAdditionally, it also attempts to visualize the individual polling booth swings of the [2019](https://en.wikipedia.org/wiki/2023_New_South_Wales_state_election) and [2015](https://en.wikipedia.org/wiki/2015_New_South_Wales_state_election) NSW state elections.\n\nThis work is inspired and heavily plagiarised from [@mjbo](https://observablehq.com/@mjbo)'s amazing work on booth swings for the federal elections. You can read his analysis [here](https://observablehq.com/@mjbo/2019-2022-australian-federal-election-swings).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2142,"value":"## 2022 Federal Election\nThe first piece of useful data we might all think of is the 2022 Federal Election. It is the most recent and maybe the most indicative of the current political climate.\n\nFirst, I'll show the results for the Greens in the 2022 Federal Election with the boundaries of those divisions. This will also include a graph of each division and the swings at each booth within that division.\n\nSecondly, I'll take this booth level data and apply it to the 2023 NSW State Election boundaries. Simply applying a new boundary layer.\n\nFinally, we'll calculate the overall swings for each NSW State Division based on the booth swings of each.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2191,"value":"### Booth swings NSW","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1458,"value":"viewof firstPreferenceSettings = makeMapSettings()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":531,"value":"_Scroll to zoom, Command + Shift + click to pan and rotate. **You may need to zoom at least once before arrows appear.**_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2147,"value":"firstSwingPreferenceDeckMap = {\n await visibility();\n return plotDeckMap({\n electionData: greensFirstPreference2022,\n divisionGeoJson: divisionGeojson2022,\n getArrowLength: firstPreferenceStaticMap.scale(\"length\").apply,\n showNegativeSwings: firstPreferenceSettings.showNegativeSwings,\n scaleBooth: firstPreferenceSettings.scaleBooth\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2193,"value":"### Booth Swings By Division","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1834,"value":"_Plot intention_: To identify \"consequential swings\": swings at booths that resulted in large gains or losses of votes, indicated by larger arrows & identify \"outlier booths\": booths that swung against the rest of the electorate, indicated by arrows not part of the standard cluster","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1058,"value":"viewof searchedDivisions = Inputs.search(\n greensFirstPreference2022,\n {\n label: \"Filter by division or state\",\n columns: [\"DivisionNm_1\"],\n datalist: [...divisions2022]\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"Plot.plot({\n width,\n color: { ...partyScale, legend: true, label: \"Party\" },\n caption:\n \"The size of the arrow is proportional to the number of votes gained or lost. Hover for more details.\",\n facet: {\n data: searchedDivisions,\n x: \"Group\"\n },\n x: {\n tickFormat: \"%\",\n transform: (d) => d / 100\n },\n y: {\n label: \"Division\",\n // group by state\n domain: _.sortBy(searchedDivisions, \"StateAb\").map((d) => d.DivisionNm_1)\n },\n marks: [\n Plot.ruleX([0]),\n Plot.frame(),\n Plot.vector(searchedDivisions, {\n y: \"DivisionNm_1\",\n x: \"Swing\",\n // if Swing is 100 point right, if Swing is 0, point up, if Swing is -100 point left\n rotate: (d) => (d.Swing / 100) * 90,\n // arrow length should match the magnitude of the swing,\n length: (d) => Math.abs(d.Swing) * d.OrdinaryVotes,\n stroke: (d) => d.PartyAb,\n title: getTooltip\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":276,"value":"### Booth swings with 2023 NSW state division boundaries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2206,"value":"viewof state2023BoothSwings = makeMapSettings()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2235,"value":"_Plot intention_: This plot intends to show first preference momentum for the Greens in the 2023 State Election. There is no grasp of winnability in this plot as this requires more in depth analysis (and access to the full preference flow data).\n\n_Scroll to zoom, Command + Shift + click to pan and rotate. **You may need to zoom at least once before arrows appear.**_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":428,"value":"firstPreferenceDeckMap = {\n await visibility();\n return plotDeckMap({\n electionData: greensFirstPreference2022,\n divisionGeoJson: divisionGeoJsonNSW2023,\n getArrowLength: firstPreferenceStaticMap.scale(\"length\").apply,\n showNegativeSwings: state2023BoothSwings.showNegativeSwings,\n scaleBooth: state2023BoothSwings.scaleBooth\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2279,"value":"This map obfuscates successful campaigning and Greens momentum in seats where the Greens first preference vote may be suppressed by an independent candidate. Hence, we should wait for full preference flows before analysing true momentum or areas of winnability.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":564,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"## Implementation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2054,"value":"Plot051 = require(\"@observablehq/plot@0.5.1\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1361,"value":"- [2019 House of Representatives data](https://results.aec.gov.au/24310/Website/HouseDownloadsMenu-24310-Csv.htm)\n- [2022 House of Representatives data](https://tallyroom.aec.gov.au/HouseDownloadsMenu-27966-Csv.htm)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":101,"value":"### Polling place data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1236,"value":"/**\n * Helper method for getting data directly from the AEC website.\n */\nasync function fetchAecCsv(url) {\n // AEC has CORS on all its resources.\n // https://observablehq.com/@severo/setup-your-own-cors-proxy\n const corsAnywhere = \"https://jdcaperon-observablehq.herokuapp.com/\";\n const csv = await fetch(corsAnywhere + url);\n\n // strip metadata on the 0th row\n const text = await csv.text();\n const cleaned = text.split(\"\\n\").slice(1).join(\"\\n\");\n\n // cram into Aquero\n return aq.fromCSV(cleaned);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2013,"value":"The AEC's CSV's have a metadata line at the top of the file that we have to strip, otherwise CSV parsers will try and treat the 0th line as the column name listing. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1926,"value":"/**\n * Helper method for cleaning AEC CSVs.\n */\nasync function cleanAecCsv(file) {\n // strip metadata on the 0th row\n const text = await file.text();\n const cleaned = text.split(\"\\n\").slice(1).join(\"\\n\");\n\n // cram into Aquero\n return aq.fromCSV(cleaned);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1311,"value":"STATES = [\"NSW\", \"VIC\", \"QLD\", \"WA\", \"SA\", \"TAS\", \"ACT\", \"NT\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1925,"value":"pollingPlaces2019 = await cleanAecCsv(\n await FileAttachment(\"GeneralPollingPlacesDownload-2019.csv\")\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1380,"value":"pollingPlaces2022 = await cleanAecCsv(\n await FileAttachment(\"GeneralPollingPlacesDownload-2022.csv\")\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1854,"value":"### Federal division data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1858,"value":"divisionGeojson2019 = fetch(\n \"https://raw.githubusercontent.com/pmcau/AustralianElectorates/main/Data/Maps/2019/australia_20.geojson\"\n).then((r) => r.json())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1860,"value":"// no redrawings that I know of\ndivisionGeojson2022 = divisionGeojson2019","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1391,"value":"getDivisions = (pollingPlaces) => [\n ...pollingPlaces.groupby(\"DivisionNm\").rollup().values(\"DivisionNm\")\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1079,"value":"divisions2019 = getDivisions(pollingPlaces2019)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1388,"value":"divisions2022 = getDivisions(pollingPlaces2022)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2154,"value":"### State division data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2254,"value":"divisionGeoJsonNSW2023 = (await FileAttachment(\"nsw2.json\")).json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2247,"value":"### Greens local group division data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2211,"value":"greensDivisionGeoJsonNSW = (await FileAttachment(\"2023_NSW_divisions@2.geojson\")).json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1711,"value":"### Federal Election 2022 - NSW first preference data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2169,"value":"mergeFirstPreferenceTables = async (tables) => {\n const australiaTable = tables.reduce((a, b) => a.concat(b));\n\n return (\n australiaTable\n .join(pollingPlaces2019, [\"PollingPlaceID\"])\n // cull outliers with data assertions\n .filter((d) => d.Latitude < 90)\n .filter((d) => 0 < d.Longitude)\n .derive({\n Group: (d) => {\n switch (d.PartyAb) {\n case \"CLP\":\n case \"LNP\":\n case \"LP\":\n case \"NP\":\n return \"Coalition\";\n case \"ALP\":\n return \"ALP\";\n case \"GRN\":\n return \"GRN\";\n }\n return \"Minor party / independent\";\n }\n })\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1944,"value":"nsw2019 = cleanAecCsv(await FileAttachment(\"HouseStateFirstPrefsByPollingPlaceDownload-2019-NSW.csv\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1410,"value":"nsw2022 = cleanAecCsv(await FileAttachment(\"HouseStateFirstPrefsByPollingPlaceDownload-2022-NSW.csv\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":651,"value":"majorPartyFirstPreference2019 = (await mergeFirstPreferenceTables([nsw2019]))\n .params({ MAJOR_PARTY_COLORS })\n .filter((d) => !!MAJOR_PARTY_COLORS[d.PartyAb])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1418,"value":"majorPartyFirstPreference2022 = (await mergeFirstPreferenceTables([nsw2022]))\n .params({ MAJOR_PARTY_COLORS })\n .filter((d) => !!MAJOR_PARTY_COLORS[d.PartyAb])\n .filter((d) => d.PollingPlaceTypeID == 1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2158,"value":"#### Greens first preference data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2160,"value":"greensFirstPreference2019 = majorPartyFirstPreference2019\n .filter((d) => d.PartyAb == \"GRN\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2164,"value":"greensFirstPreference2022 = majorPartyFirstPreference2022\n .filter((d) => d.PartyAb == \"GRN\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1715,"value":"### Two candidate preferred data NSW","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":133,"value":"twoCandidatePreferred2019 = (await cleanAecCsv(\n await FileAttachment(\"HouseTcpByCandidateByPollingPlaceDownload-2019.csv\")\n))\n .join(pollingPlaces2019, \"PollingPlaceID\")\n // cull outliers with data assertions\n .filter((d) => d.Latitude < 90)\n .filter((d) => 0 < d.Longitude)\n // Filter for nsw\n .filter((d) => d.StateAb === \"NSW\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1421,"value":"twoCandidatePreferred2022 = (await cleanAecCsv(\n await FileAttachment(\"HouseTcpByCandidateByPollingPlaceDownload-2022.csv\")\n))\n .join(pollingPlaces2022, \"PollingPlaceID\")\n // cull outliers with data assertions\n .filter((d) => d.Latitude < 90)\n .filter((d) => 0 < d.Longitude)\n // Filter for nsw\n .filter((d) => d.StateAb === \"NSW\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":547,"value":"### Party metadata","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":351,"value":"culori = import(\"https://unpkg.com/culori@2.0.0/bundled/culori.min.mjs?module\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2017,"value":"These colors were extracted from the Wikipedia page [2022 Australian federal election](https://en.wikipedia.org/wiki/2022_Australian_federal_election) with some minor tweaks to improve contrast against a white background.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":230,"value":"MAJOR_PARTY_COLORS = ({\n ALP: \"#DE3533\",\n CLP: \"#f8981d\",\n GRN: \"#10C25B\",\n IND: \"#888888\",\n KAP: \"#b50204\",\n LNP: \"#1456F1\",\n LP: \"#0047AB\",\n NP: \"#007700\",\n ON: \"#f36c21\",\n UAPP: \"#ccbe00\",\n XEN: \"#000000\",\n Coalition: \"#1456F1\" // same as LNP\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":358,"value":"culori.parse(MAJOR_PARTY_COLORS[\"ALP\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":858,"value":"function partyAbbreviation(abbr) {\n return html`${abbr}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":864,"value":"partyAbbreviation(\"ALP\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":975,"value":"partyScale = {\n // This is a bit of a gnarly hack to set custom categorical colors\n // If there's a better way then I'd love to hear it\n const plot = Plot.plot({\n marks: [\n Plot.tickX(majorPartyFirstPreference2022, {\n stroke: (d) => d.PartyAb\n })\n ]\n });\n\n const scale = plot.scale(\"color\");\n scale.range = scale.domain.map((d) => MAJOR_PARTY_COLORS[d]);\n\n return scale;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":572,"value":"### Plot implementation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":243,"value":"import { Plot } from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1905,"value":"getPartyColor = (d) => {\n const { r, g, b } = culori.parse(MAJOR_PARTY_COLORS[d.PartyAb]);\n return [r * 255, g * 255, b * 255];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":368,"value":"function getTooltip(d) {\n return `${d.Swing}% ${d.Swing > 0 ? \"to\" : \"from\"} ${d.GivenNm} ${\n d.Surname\n } (${d.PartyAb})\n Division: ${d.DivisionNm_1} (${d.PremisesStateAb})\n Votes at polling place for candidate: ${d.OrdinaryVotes}\n Elected?: ${d.Elected}\n Previously elected?: ${d.HistoricElected}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":161,"value":"plotMap = ({ data, showNegativeSwings, scaleBooth }) => {\n data = showNegativeSwings ? data : data.filter((d) => d.Swing > 0);\n\n return Plot.plot({\n color: { ...partyScale, legend: true, label: \"Party\" },\n caption:\n \"Swings away from a candidate point left, swings to a candidate point right. Hover for more details.\",\n marks: [\n Plot.vector(data, {\n x: \"Longitude\",\n y: \"Latitude\",\n // if Swing is postive point up (rotate 0), if Swing is negative point down (rotate 180)\n rotate: (d) => (Math.sign(d.Swing - Math.abs(d.Swing)) * 180),\n // arrow length should match the magnitude of the swing,\n length: (d) =>\n scaleBooth ? Math.abs(d.Swing) * d.OrdinaryVotes : Math.abs(d.Swing),\n stroke: (d) => d.PartyAb,\n title: getTooltip\n })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":302,"value":"import { mapboxgl, deck } from \"@johnhaldeman/tutorial-on-deckgl\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1488,"value":"makeMapSettings = () =>\n Inputs.form({\n showNegativeSwings: Inputs.toggle({\n label: \"Show negative swings\",\n value: false\n }),\n scaleBooth: Inputs.select(\n new Map(\n [\n [\"Arrow size proportional to swing magnitude at booth\", false],\n [\"Arrow size proportional to votes gained or lost at booth\", true]\n ],\n { value: false, label: \"Arrow sizing scheme\" }\n )\n )\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2019,"value":"Plot has a more coherent mark sizing algorithm, so we render them here, and extract out the rendered sizes for use in the Deck.gl map. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":267,"value":"firstPreferenceStaticMap = {\n return plotMap({\n data: greensFirstPreference2022,\n showNegativeSwings: firstPreferenceSettings.showNegativeSwings,\n scaleBooth: firstPreferenceSettings.scaleBooth\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 32:"$31:body"