2f:T8af,main_chart = { const v1 = (d) => d.spending_per_cap; const v2 = (d) => d[selection]; const y2 = d3.scaleLinear(d3.extent(statewide_data, v2), [ 0, d3.max(statewide_data, v1) ]); return Plot.plot({ marginTop: 35, x: { tickFormat: "" }, // no comma for years y: { axis: "left", tickFormat: "$0f", label: "spending per capita" }, marks: [ Plot.axisY(y2.ticks(), { color: "steelblue", anchor: "right", label: selection, y: y2, tickFormat: ".0%" }), Plot.ruleY([0]), Plot.rectY(statewide_data, { x: "year", y: v1, fill: "#ccc", interval: 1, title: (d) => `${d.year}: $${d3.format(".0f")( d.spending_per_cap )} per person \nClearance rate for ${main_chart_input}: ${d3.format( ".0%" )(d[selection])}`, tip: true }), Plot.lineY( statewide_data, Plot.mapY((D) => D.map(y2), { x: "year", y: v2, stroke: "steelblue", strokeWidth: 4 }) ), Plot.dot( statewide_data, Plot.mapY((D) => D.map(y2), { x: "year", y: v2, stroke: "steelblue", r: 4, strokeWidth: 2, fill: "white" }) ), Plot.text( statewide_data.filter((d) => [1985, 1990, 1998, 2010, 2014, 2019, 2022].includes(d.year) ), Plot.mapY( (D) => D.map(y2), { x: "year", y: v2, dy: -11, text: (d) => `${d3.format(".0%")(d[selection])}` } ) ), Plot.text( statewide_data.filter((d) => [2006].includes(d.year)), Plot.mapY( (D) => D.map(y2), { x: "year", y: v2, dy: 14, text: (d) => `${d3.format(".0%")(d[selection])}` } ) ), Plot.text( statewide_data.filter((d) => d.year % 3 == 0), { x: "year", y: v1, dy: -11, text: (d) => `$${d3.format(".0f")(d.spending_per_cap)}` } ) ] }); }30:Tbee,change_over_time_county_static = { const plot = Plot.plot({ style: { fontSize: "14px", fontFamily: "'Open Sans', sans-serif" }, marginLeft: 100, marginTop: 30, marginBottom: 75, x: { grid: false, domain: [-300, 300], label: `Percentage change of clearance rates & \n change in inflation adjusted per capita spending`, labelOffset: 50, ticks: 0 // tickFormat: percent_format }, y: { label: null, domain: [ "California", " ", " ", "San Mateo", "Orange", "Solano", "Fresno", "San Francisco", "San Bernardino", "Los Angeles", "Santa Barbara", "Tulare", "San Diego", "Contra Costa", "Sonoma", "Ventura", "San Joaquin", "Santa Clara", "Monterey", "Riverside", "Kern", "Sacramento", "Alameda", "Stanislaus", "Placer" ], tickSize: 0 }, marks: [ Plot.barX(county_change_over_time_data, { x: "spending_per_cap_change", y: "county", fill: "#1864aa", title: (d) => `${d.county} \n spends ${dollar_format( d.spending_per_cap_change )} more per capita than in 2003` }), Plot.text(county_change_over_time_data, { x: (d) => d.spending_per_cap_change > 0 ? d.spending_per_cap_change + 28 : d.spending_per_cap_change + 58, y: "county", text: (d) => d.spending_per_cap_change > 0 ? `+ ${dollar_format(d.spending_per_cap_change)}` : `${dollar_format(d.spending_per_cap_change)}` }), Plot.dot(county_change_over_time_data, { x: 0, y: "county", dx: 0, dy: 0, fill: "red", r: 4 }), Plot.arrow(county_change_over_time_data, { x2: (d) => d[county_offense_type] * 300, y1: "county", x1: 0, y2: "county", stroke: "red", strokeWidth: 1.5, title: (d) => `Clearances for ${county_rate_input} \n went from \n ${percent_format( d[county_static_rate03] )} to ${percent_format(d[county_static_rate22])}` }), Plot.text(county_change_over_time_data, { x: (d) => d[county_offense_type] < 0 ? d[county_offense_type] * 300 : 0, y: "county", dx: -20, dy: 0, text: (d) => `${percent_format(d[county_offense_type])}`, title: (d) => `Clearances for ${county_rate_input} \n went from \n ${percent_format( d[county_static_rate03] )} to ${percent_format(d[county_offense_type])}` }) ], caption: html`

Source: CJCJ analysis of California Department of Justice Data and California Comptroller Data, 2022 vs 2003.

`, tooltip: { opacity: 0.7 } }); return plot; }31:T16b6,unique_list = [ "Adelanto PD", "Alameda PD", "Alhambra PD", "Aliso Viejo PD", "Anaheim PD", "Antioch PD", "Apple Valley PD", "Arcadia PD", "Atascadero PD", "Atwater PD", "Azusa PD", "Bakersfield PD", "Baldwin Park PD", "Banning PD", "Barstow PD", "Beaumont PD", "Bell PD", "Bellflower PD", "Bell Gardens PD", "Belmont PD", "Benicia PD", "Berkeley PD", "Beverly Hills PD", "Brawley PD", "Brea PD", "Brentwood PD", "Buena Park PD", "Burbank PD", "Burlingame PD", "Calexico PD", "Camarillo PD", "Campbell PD", "Carlsbad PD", "Carson PD", "Cathedral City PD", "Ceres PD", "Cerritos PD", "Chico PD", "Chino PD", "Chino Hills PD", "Chula Vista PD", "Citrus Heights PD", "Claremont PD", "Clovis PD", "Coachella PD", "Colton PD", "Compton PD", "Concord PD", "Corona PD", "Costa Mesa PD", "Covina PD", "Culver City PD", "Cupertino PD", "Cypress PD", "Daly City PD", "Dana Point PD", "Danville PD", "Davis PD", "Delano PD", "Desert Hot Springs PD", "Diamond Bar PD", "Downey PD", "Dublin PD", "East Palo Alto PD", "Eastvale PD", "El Cajon PD", "El Centro PD", "El Cerrito PD", "Elk Grove PD", "El Monte PD", "Encinitas PD", "Escondido PD", "Eureka PD", "Fairfield PD", "Folsom PD", "Fontana PD", "Foster City PD", "Fountain Valley PD", "Fremont PD", "Fresno PD", "Fullerton PD", "Galt PD", "Gardena PD", "Garden Grove PD", "Gilroy PD", "Glendale PD", "Glendora PD", "Goleta PD", "Hanford PD", "Hawthorne PD", "Hayward PD", "Hemet PD", "Hercules PD", "Hesperia PD", "Highland PD", "Hollister PD", "Huntington Beach PD", "Huntington Park PD", "Imperial Beach PD", "Indio PD", "Inglewood PD", "Irvine PD", "Jurupa Valley PD", "Lafayette PD", "Laguna Hills PD", "Laguna Niguel PD", "La Habra PD", "Lake Elsinore PD", "Lake Forest PD", "Lakewood PD", "La Mesa PD", "La Mirada PD", "Lancaster PD", "La Puente PD", "La Quinta PD", "Lathrop PD", "La Verne PD", "Lawndale PD", "Lemon Grove PD", "Lemoore PD", "Lincoln PD", "Livermore PD", "Lodi PD", "Loma Linda PD", "Lompoc PD", "Long Beach PD", "Los Altos PD", "Los Angeles PD", "Los Banos PD", "Los Gatos PD", "Lynwood PD", "Madera PD", "Manhattan Beach PD", "Manteca PD", "Martinez PD", "Maywood PD", "Menifee PD", "Menlo Park PD", "Merced PD", "Milpitas PD", "Mission Viejo PD", "Modesto PD", "Monrovia PD", "Montclair PD", "Montebello PD", "Monterey PD", "Monterey Park PD", "Moorpark PD", "Moreno Valley PD", "Morgan Hill PD", "Mountain View PD", "Murrieta PD", "Napa PD", "National City PD", "Newark PD", "Newport Beach PD", "Norwalk PD", "Novato PD", "Oakland PD", "Oakley PD", "Oceanside PD", "Ontario PD", "Orange PD", "Oxnard PD", "Pacifica PD", "Palmdale PD", "Palm Desert PD", "Palm Springs PD", "Palo Alto PD", "Paramount PD", "Pasadena PD", "Paso Robles PD", "Perris PD", "Petaluma PD", "Pico Rivera PD", "Pittsburg PD", "Placentia PD", "Pleasant Hill PD", "Pleasanton PD", "Pomona PD", "Porterville PD", "Poway PD", "Rancho Cordova PD", "Rancho Cucamonga PD", "Rancho Palos Verdes PD", "Rancho Santa Margarita PD", "Redding PD", "Redlands PD", "Redondo Beach PD", "Redwood City PD", "Rialto PD", "Richmond PD", "Ridgecrest PD", "Riverside PD", "Rocklin PD", "Rohnert Park PD", "Rosemead PD", "Roseville PD", "Sacramento PD", "Salinas PD", "San Bernardino PD", "San Bruno PD", "San Carlos PD", "San Clemente PD", "San Diego PD", "San Dimas PD", "San Francisco PD", "San Gabriel PD", "Sanger PD", "San Jacinto PD", "San Jose PD", "San Juan Capistrano PD", "San Leandro PD", "San Luis Obispo PD", "San Marcos PD", "San Mateo PD", "San Pablo PD", "San Rafael PD", "San Ramon PD", "Santa Ana PD", "Santa Barbara PD", "Santa Clara PD", "Santa Clarita PD", "Santa Cruz PD", "Santa Maria PD", "Santa Monica PD", "Santa Paula PD", "Santa Rosa PD", "Santee PD", "Saratoga PD", "Seal Beach PD", "Seaside PD", "Simi Valley PD", "Soledad PD", "South Gate PD", "South Pasadena PD", "South San Francisco PD", "Stanton PD", "Stockton PD", "Suisun City PD", "Sunnyvale PD", "Temecula PD", "Temple City PD", "Thousand Oaks PD", "Torrance PD", "Tracy PD", "Tulare PD", "Turlock PD", "Tustin PD", "Twentynine Palms PD", "Union City PD", "Upland PD", "Vacaville PD", "Vallejo PD", "Ventura PD", "Victorville PD", "Visalia PD", "Vista PD", "Walnut PD", "Walnut Creek PD", "Wasco PD", "Watsonville PD", "West Covina PD", "West Hollywood PD", "Westminster PD", "West Sacramento PD", "Whittier PD", "Wildomar PD", "Windsor PD", "Woodland PD", "Yorba Linda PD", "Yuba City PD", "Yucaipa PD", "Alameda Sheriff", "Butte Sheriff", "Calaveras Sheriff", "Contra Costa Sheriff", "El Dorado Sheriff", "Fresno Sheriff", "Humboldt Sheriff", "Imperial Sheriff", "Kern Sheriff", "Kings Sheriff", "Lake Sheriff", "Los Angeles Sheriff", "Madera Sheriff", "Marin Sheriff", "Mendocino Sheriff", "Merced Sheriff", "Monterey Sheriff", "Nevada Sheriff", "Orange Sheriff", "Placer Sheriff", "Riverside Sheriff", "Sacramento Sheriff", "San Bernardino Sheriff", "San Diego Sheriff", "San Joaquin Sheriff", "San Luis Obispo Sheriff", "San Mateo Sheriff", "Santa Barbara Sheriff", "Santa Clara Sheriff", "Santa Cruz Sheriff", "Shasta Sheriff", "Sonoma Sheriff", "Stanislaus Sheriff", "Tehama Sheriff", "Tulare Sheriff", "Tuolumne Sheriff", "Ventura Sheriff", "Yolo Sheriff", "Yuba Sheriff" ]32:T876,main_chart_old = Plot.plot({ marks: [ Plot.ruleY([0]), Plot.rectY(statewide_data, { x: "year", y: "spending_per_cap", interval: 1, insetLeft: 1, insetRight: 1, fill: "#ccc", title: (d) => `${d.year}: $${d3.format(".0f")( d.spending_per_cap )} per person \nClearance rate for ${main_chart_input}: ${d3.format( ".0%" )(d[selection])}`, tip: true }), Plot.text( statewide_data.filter((d) => d.year % 3 == 0), { x: "year", y: "spending_per_cap", dy: -11, text: (d) => `$${d3.format(".0f")(d.spending_per_cap)}` } ), () => Plot.plot({ // dimensions marginLeft: 70, marginRight: 50, marginBottom: 50, width: Math.min(width, 780), height: 400, marks: [ Plot.line(statewide_data, { x: "year", y: selection, stroke: "steelblue", strokeWidth: 4, interval: 1 }), Plot.dot(statewide_data, { x: "year", y: selection, stroke: "steelblue", r: 4, strokeWidth: 2, fill: "white" }), Plot.text( statewide_data.filter((d) => [1985, 1990, 1998, 2010, 2014, 2019, 2022].includes(d.year) ), { x: "year", y: selection, dy: -11, text: (d) => `${d3.format(".0%")(d[selection])}` } ), Plot.text( statewide_data.filter((d) => [2006].includes(d.year)), { x: "year", y: selection, dy: 14, text: (d) => `${d3.format(".0%")(d[selection])}` } ) ], x: { axis: null, label: null }, y: { axis: "right", tickFormat: ".0%" } }) ], width: Math.min(width, 780), height: 400, x: { tickFormat: "", label: null }, y: { axis: "left", tickFormat: "$0f", label: "spending per capita" } })33:[{"id":"d4548b6b3ca8cdd0934056bdcfb7d680440e1cff894b8465094a56e0717e1f0aa6144a32c0eb3bd242ced376a39ff87f402f37451b95699a70fc67ac2eb2bc3e","href":"https://static.observableusercontent.com/files/d4548b6b3ca8cdd0934056bdcfb7d680440e1cff894b8465094a56e0717e1f0aa6144a32c0eb3bd242ced376a39ff87f402f37451b95699a70fc67ac2eb2bc3e","name":"statewide 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2d:{"title":"Budget and Clearance Data","cells":[{"id":0,"value":"# Budget and Clearance Data","pinned":false,"hidden":false,"mode":"md"},{"id":359,"value":"February 2024","pinned":false,"hidden":false,"mode":"md"},{"id":505,"value":"chart1_title = md`### Statewide per capita spending on police vs clearance rates, 1985-2022 (real 2022 dollars)`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":52,"value":"viewof main_chart_input = Inputs.select(\n [\"All Offenses\", \"Violent Offenses\", \"Property Offenses\"],\n { sort: false, value: \"All Offenses\", label: \"Select a Category of Offenses\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":539,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":515,"value":"chart2_title = md`#### California Spending on Police, 1985-2022, Real 2022 Dollars`","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"spending_chart = Plot.plot({\n grid: false,\n marginLeft: 60,\n y: { label: \"2022 dollars (billions)\", tickFormat: \"$0f\" },\n x: { tickFormat: \"0f\", label: null },\n facet: { label: null },\n marks: [\n Plot.line(statewide_data, {\n x: \"year\",\n y: (d) => d[\"spending\"] / 1000000000,\n title: (d) =>\n `${d.year}: $${d3.format(\",.1f\")(d[\"spending\"] / 1000000000)} billion`,\n stroke: \"red\",\n tip: true\n }),\n Plot.text(\n statewide_data.filter((d) =>\n [1987, 1990, 1995, 2000, 2005, 2010, 2015, 2021].includes(d.year)\n ),\n {\n x: \"year\",\n y: (d) => d[\"spending\"] / 1000000000,\n text: (d) => `$${d3.format(\",.1f\")(d[\"spending\"] / 1000000000)}`,\n dy: -13\n }\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":510,"value":"chart3_title = md`#### Percent Change in Clearance Rates and Absolute Change in Spending, 2003 vs 2022`","pinned":false,"hidden":false,"mode":"ojs"},{"id":183,"value":"viewof county_rate_input = Inputs.select(\n [\"All Offenses\", \"Violent Offenses\", \"Property Offenses\"],\n { sort: false, value: \"All Offenses\", label: \"Select a Category of Offenses\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":179,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":489,"value":"## Change in Police Spending vs Change in Clearance Rates, 1985-2022","pinned":false,"hidden":false,"mode":"md"},{"id":517,"value":"local_title = md`# Local Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":522,"value":"local_desc = md`View the data for your jurisdiction. This tool allows you to compare per capita spending, clearance rates, and crime rates by offense type for jurisdictions with populations larger than 25,000.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":231,"value":"viewof jurisdiction = Inputs.text({\n label: \"Select a jurisdiction\",\n placeholder: \"Enter a city or county\",\n datalist: unique_list\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":261,"value":"viewof local_input = Inputs.select(\n [\"All Offenses\", \"Violent Offenses\", \"Property Offenses\"],\n { sort: false, value: \"All Offenses\", label: \"Select a Category of Offenses\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":297,"value":"legend2 = svg``","pinned":false,"hidden":false,"mode":"ojs"},{"id":526,"value":"local1_title = md`#### Per Capita Spending, ${jurisdiction} vs California Average, 2003-2022`","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"local_spending_chart = Plot.plot({\n height: 400,\n y: { label: \"Spending per capita\", tickFormat: \"$0,f\" },\n x: { label: null, tickFormat: \"\" },\n marks: [\n Plot.barY(\n local_data.filter((d) => d.jurisdiction == jurisdiction),\n {\n x: \"year\",\n y: \"spending_per_cap\",\n title: (d) =>\n `${d.jurisdiction}, ${d.year}: ${dollar_format(d.spending_per_cap)}`,\n fill: \"#ccc\",\n tip: true\n }\n ),\n Plot.line(\n statewide_data.filter(\n (d) => d.year > 2002 && d.jurisdiction == \"California\"\n ),\n {\n x: \"year\",\n y: \"spending_per_cap\",\n stroke: \"red\",\n title: (d) =>\n `${d.jurisdiction}, ${d.year}: ${dollar_format(d.spending_per_cap)}`,\n tip: true\n }\n )\n ],\n caption: ``\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":528,"value":"local2_title = md`#### Clearance Rates for ${local_input}, ${jurisdiction} vs. California Average, 2003-2022`","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"local_clearance_chart = Plot.plot({\n height: 400,\n y: { label: local_input, tickFormat: percent_format },\n x: { label: null, tickFormat: \"\" },\n marks: [\n Plot.line(\n local_data\n .filter((d) => d.jurisdiction == jurisdiction)\n .concat(statewide_data.filter((d) => d.year > 2002)),\n {\n x: \"year\",\n y: local_selection,\n title: (d) =>\n `${d.jurisdiction} in ${d3.format(\"\")(d.year)} \\n${percent_format(\n d[local_selection]\n )}`,\n tip: true,\n stroke: (d) => (d.jurisdiction == \"California\" ? \"red\" : \"black\")\n }\n )\n ],\n caption: ``\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":534,"value":"local3_title = md`#### Crime Rates for ${local_input}, ${jurisdiction} vs. California Average, 2003-2022`","pinned":false,"hidden":false,"mode":"ojs"},{"id":275,"value":"local_crime_rate_chart = Plot.plot({\n height: 400,\n y: {\n label: `${local_input} per 10k`,\n tickFormat: \"0,f\",\n nice: true\n },\n x: { label: null, tickFormat: \"\" },\n marks: [\n Plot.line(\n local_data\n .filter((d) => d.jurisdiction == jurisdiction)\n .concat(statewide_data.filter((d) => d.year > 2002)),\n {\n x: \"year\",\n y: local_selection_crime_rate,\n title: (d) =>\n `${d.jurisdiction} in ${d3.format(\"\")(d.year)}\\n${d3.format(\".0f\")(\n d[local_selection_crime_rate]\n )} ${local_input} per 10k`,\n tip: true,\n stroke: (d) => (d.jurisdiction == \"California\" ? \"red\" : \"black\")\n }\n )\n ],\n caption: ``\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"### Data","pinned":false,"hidden":false,"mode":"md"},{"id":6,"value":"statewide_data = FileAttachment(\"statewide summary@3.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":181,"value":"county_change_over_time_data = FileAttachment(\n \"county-change-over-time@2.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":233,"value":"local_data = FileAttachment(\"local-data@2.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":414,"value":"indexed_data = FileAttachment(\"indexed-chart@1.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":185,"value":"### Dependencies","pinned":false,"hidden":false,"mode":"md"},{"id":218,"value":"import {\n addTooltips,\n hover,\n id_generator\n} from \"@cyrusobrien/property-crime-rates-by-state-1985-2020\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":220,"value":"Plot = tooltipPlugin(await require(\"@observablehq/plot\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":222,"value":"tooltipPlugin = (Plot) => {\n const { plot } = Plot;\n Plot.plot = ({ tooltip, ...options }) => addTooltips(plot(options), tooltip);\n return Plot;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":187,"value":"### Local logic","pinned":false,"hidden":false,"mode":"md"},{"id":227,"value":"selection = {\n if (main_chart_input == \"All Offenses\") {\n return \"total\";\n } else if (main_chart_input == \"Violent Offenses\") {\n return \"violent\";\n } else if (main_chart_input == \"Property Offenses\") {\n return \"property\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"county_offense_type = {\n if (county_rate_input == \"All Offenses\") {\n return \"total_change\";\n } else if (county_rate_input == \"Violent Offenses\") {\n return \"violent_change\";\n } else if (county_rate_input == \"Property Offenses\") {\n return \"property_change\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":205,"value":"county_static_rate03 = {\n if (county_rate_input == \"All Offenses\") {\n return \"total03\";\n } else if (county_rate_input == \"Violent Offenses\") {\n return \"violent03\";\n } else if (county_rate_input == \"Property Offenses\") {\n return \"property03\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":208,"value":"county_static_rate22 = {\n if (county_rate_input == \"All Offenses\") {\n return \"total22\";\n } else if (county_rate_input == \"Violent Offenses\") {\n return \"violent22\";\n } else if (county_rate_input == \"Property Offenses\") {\n return \"property22\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":263,"value":"local_selection = {\n if (local_input == \"All Offenses\") {\n return \"total\";\n } else if (local_input == \"Violent Offenses\") {\n return \"violent\";\n } else if (local_input == \"Property Offenses\") {\n return \"property\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":311,"value":"local_selection_crime_rate = {\n if (local_input == \"All Offenses\") {\n return \"total_crime_rate\";\n } else if (local_input == \"Violent Offenses\") {\n return \"violent_crime_rate\";\n } else if (local_input == \"Property Offenses\") {\n return \"property_crime_rate\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":434,"value":"dollar_format_big = (number) => d3.format(\"$.2s\")(number).replace(\"G\", \"B\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":198,"value":"dollar_format = d3.format(\"$,.0f\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":200,"value":"percent_format = d3.format(\".0%\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":235,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":406,"value":"html``","pinned":false,"hidden":false,"mode":"html"},{"id":603,"value":"#### Discarded charts","pinned":false,"hidden":false,"mode":"md"},{"id":4,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":493,"value":"indexed_chart2 = Plot.plot({\n height: 800,\n y: {\n axis: null,\n label: null,\n grid: false,\n ticks: null\n },\n x: {\n tickFormat: \"\",\n label: null,\n axis: null\n },\n marks: [\n Plot.line(\n indexed_data,\n Plot.normalizeY({\n x: \"year\",\n y: \"value\",\n stroke: (d) => (d.name == \"spending\" ? \"#9CD4FF\" : \"#FF5F00\"),\n strokeWidth: 10\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}