28:null 29:["$","title",null,{"children":"coronavirus-data | Zhi Keng He | Observable"}] 2f:T67c,html`${["Deaths"].map((type, i) => { const dataset = data.filter((d) => d.type === type); return Plot.plot({ height: 400, grid: true, marginLeft: 60, marginRight: 20, marginTop: 0, marginBottom: 40, width: width, x: { axis: true, anchor: "bottom", tickSpacing: 30, domain: [new Date(2020, 2, 0), new Date(2023, 3, 1)] }, y: { label: null, min: 0 }, marks: [ Plot.line( dataset.filter((d) => d.date < new Date(2023, 3, 1)), { x: "date", y: "avg_count", stroke: "black", strokeWidth: 1 } ), Plot.areaY( dataset.filter((d) => d.date < new Date(2023, 3, 1)), { x: "date", y: "count", fill: "darkgrey", opacity: 0.6 } ), Plot.text( dataset, Plot.selectFirst({ text: "type", frameAnchor: "top-right", dx: -6, dy: 6 }) ), Plot.dot( dataset, Plot.selectMaxY({ y: "count", x: "date", dy: 6 }) ), Plot.text( dataset, Plot.selectMaxY({ y: "count", x: "date", textAnchor: "start", text: (d) => `${d3.format(".3s")(d["count"])} ${d[ "type" ].toLowerCase()} per day - ${d["date"].toLocaleDateString("en-US", { month: "short", day: "numeric", year: "numeric" })}`, dx: 5, dy: 10 }) ), Plot.frame() ] }); })}`30:T5ad,html`${["Cases", "Hospitalizations", "Deaths"].map((type, i) => { const dataset = data.filter((d) => d.type === type); return Plot.plot({ height: 140, marginLeft: 60, marginRight: 20, marginTop: 0, marginBottom: i === 2 ? 35 : 10, width: width, x: { axis: i === 2, anchor: "bottom", tickSpacing: 30, domain: [new Date(2020, 2, 0), new Date(2023, 3, 1)] }, y: { label: null, min: 0 }, marks: [ Plot.areaY( dataset.filter((d) => d.date < new Date(2023, 3, 1)), { x: "date", y: "count", fill: "darkgrey", opacity: 0.6 } ), Plot.text( dataset, Plot.selectFirst({ text: "type", frameAnchor: "top-right", dx: -6, dy: 6 }) ), Plot.dot( dataset, Plot.selectMaxY({ y: "count", x: "date", dy: 6 }) ), Plot.text( dataset, Plot.selectMaxY({ y: "count", x: "date", textAnchor: "start", text: (d) => `${d3.format(".3s")(d["count"])} ${d[ "type" ].toLowerCase()} per day - ${d["date"].toLocaleDateString("en-US", { month: "short", day: "numeric" })}`, dx: 5, dy: 10 }) ), Plot.frame() ] }); })}`31:T54d,html`${["Cases", "Hospitalizations", "Deaths"].map((type, i) => { const dataset = data.filter((d) => d.type === type); return Plot.plot({ grid: true, height: 140, marginLeft: 60, marginRight: 20, marginTop: 0, marginBottom: i === 2 ? 35 : 10, width: width, caption: i === 2 ? html`Total cases, hospitalizations, and deaths per day and rolling averages in New York City
Data: NYC Department of Health; Chart: BetaNYC` : "", x: { axis: i === 2, anchor: "bottom", tickSpacing: 30, domain: [new Date(2020, 2, 0), new Date(2023, 3, 1)] }, y: { label: null, min: 0 }, marks: [ Plot.line( dataset.filter((d) => d.date < new Date(2023, 3, 1)), { x: "date", y: "avg_count", stroke: "black", strokeWidth: 1, curve: "step-before" } ), Plot.areaY( dataset.filter((d) => d.date < new Date(2023, 3, 1)), { x: "date", y: "count", fill: "darkgrey", opacity: 0.6 } ), Plot.text( dataset, Plot.selectFirst({ text: "type", frameAnchor: "top-right", dx: -6, dy: 6 }) ), Plot.frame() ] }); })}`32:T6ea,Plot.plot({ grid: true, height: 300, marginRight: 70, marginLeft: 200, width: width, style: { fontSize: 30 }, caption: html`% population compared to & of deaths in New York State; NY State of Health (Data as of ${cases .at(-1) ["date"].toLocaleDateString()})`, x: { label: "Percent of deaths/ population", domain: [0, 38] }, y: { label: null, min: 0, domain: fatalities_state.map((d) => d.group) }, marks: [ Plot.link(fatalities_state, { x1: "pop", x2: "deaths", y: "group", stroke: "grey" }), Plot.dot(fatalities_state, { x: "deaths", y: "group", fill: "black", r: 5 }), Plot.dot(fatalities_state, { x: "pop", y: "group", fill: "white", stroke: "black" }), Plot.text( fatalities_state.filter((d) => d.deaths > d.pop), { x: "deaths", y: "group", dx: 7, textAnchor: "start", text: (d) => d3.format(".0f")(d["deaths"]) + "%" } ), Plot.text( fatalities_state.filter((d) => d.deaths <= d.pop), { x: "deaths", y: "group", dx: -7, textAnchor: "end", text: (d) => d3.format(".0f")(d["deaths"]) + "%" } ), Plot.text( fatalities_state.filter((d) => d.deaths > d.pop), { x: "pop", y: "group", dx: -7, textAnchor: "end", text: (d) => d3.format(".0f")(d["pop"]) + "%" } ), Plot.text( fatalities_state.filter((d) => d.deaths <= d.pop), { x: "pop", y: "group", dx: 7, textAnchor: "start", text: (d) => d3.format(".0f")(d["pop"]) + "%" } ) ] })33:T5d3,html`${["Cases"].map((type, i) => { const dataset = data.filter((d) => d.type === type); return Plot.plot({ height: 400, grid: true, marginLeft: 60, marginRight: 20, marginTop: 0, marginBottom: 40, width: width, x: { axis: true, anchor: "bottom", tickSpacing: 30, domain: [new Date(2020, 2, 0), new Date()] }, y: { label: null, min: 0 }, marks: [ Plot.line(dataset, { x: "date", y: "avg_count", stroke: "black", strokeWidth: 1 }), Plot.areaY(dataset, { x: "date", y: "count", fill: "darkgrey", opacity: 0.6 }), Plot.text( dataset, Plot.selectFirst({ text: "type", frameAnchor: "top-right", dx: -6, dy: 6 }) ), Plot.dot( dataset, Plot.selectMaxY({ y: "count", x: "date", dy: 6 }) ), Plot.text( dataset, Plot.selectMaxY({ y: "count", x: "date", textAnchor: "start", text: (d) => `${d3.format(".3s")(d["count"])} ${d[ "type" ].toLowerCase()} per day - ${d["date"].toLocaleDateString("en-US", { month: "short", day: "numeric", year: "numeric" })}`, dx: 5, dy: 10 }) ), Plot.frame() ] }); })}`34:[] 2c:{"id":"bfe8168c11b3af5d","slug":null,"title":"coronavirus-data","thumbnail":"21254317ebb1dd605795a0c4c229a9a9404c8c24441dd38504f0b27e317fb07f","access_level":"public","create_time":"$D2023-07-03T15:29:03.329Z","update_time":"$D2023-07-26T18:33:27.205Z","view_time":null,"first_public_time":"$D2023-07-03T15:29:03.329Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"16506c8d98fe0d4f","login":"zhik","name":"Zhi Keng He","avatar_url":"https://avatars-next.observableusercontent.com/1921c3cb8ffa82cde953aa4417dc8b601dbd05384629c1f8ecf0544be068a204","tier":"starter_2024"},"version":735,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"215e59083f0e53f8","login":"zhik","name":"Zhi Keng He","avatar_url":"https://avatars-next.observableusercontent.com/1921c3cb8ffa82cde953aa4417dc8b601dbd05384629c1f8ecf0544be068a204","tier":null}],"members":[{"type":"individual","id":"215e59083f0e53f8","login":"zhik","name":"Zhi Keng He","avatar_url":"https://avatars-next.observableusercontent.com/1921c3cb8ffa82cde953aa4417dc8b601dbd05384629c1f8ecf0544be068a204","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"coronavirus-data","cells":[{"id":0,"value":"# coronavirus-data ","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"cases = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/data-by-day.csv\",\n d3.autoType\n );\n\n return data.map((d) => {\n d.date = new Date(d.date_of_interest);\n return d;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":32,"value":"hosp = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/hosp-by-day.csv\",\n d3.autoType\n );\n\n return data.map((d) => {\n d.date = new Date(d.date_of_interest);\n return d;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"deaths = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/deaths-by-day.csv\",\n d3.autoType\n );\n\n return data.map((d) => {\n d.date = new Date(d.date_of_interest);\n return d;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":46,"value":"data = {\n const c = cases.map((d) => {\n return {\n date: d.date,\n type: \"Cases\",\n count: +d[\"CASE_COUNT\"],\n avg_count: +d[\"CASE_COUNT_7DAY_AVG\"]\n };\n });\n const d = deaths.map((d) => {\n return {\n date: d.date,\n type: \"Deaths\",\n count: +d[\"DEATH_COUNT\"],\n avg_count: +d[\"DEATH_COUNT_7DAY_AVG\"]\n };\n });\n\n const h = hosp.map((d) => {\n return {\n date: d.date,\n type: \"Hospitalizations\",\n count: +d[\"HOSPITALIZED_COUNT\"],\n avg_count: +d[\"HOSP_COUNT_7DAY_AVG\"]\n };\n });\n\n return [...c, ...d, ...h];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":558,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":12,"value":"### I) Graph: cases, hospitalizations and deaths for NY City over time at:\n ","pinned":false,"hidden":false,"mode":"md"},{"id":711,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":669,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":178,"value":"by_race = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/nychealth/coronavirus-data/master/totals/by-race.csv\",\n d3.autoType\n );\n\n return data.sort(\n (a, b) => b[\"CONFIRMED_CASE_RATE_ADJ\"] - a[\"CONFIRMED_CASE_RATE_ADJ\"]\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":264,"value":"by_race_state = {\n const data = await d3.json(\n \"https://health.data.ny.gov/resource/n2f5-zm5f.json?$query=SELECT%0A%20%20%60week_starting%60%2C%0A%20%20%60race_or_hispanic_ethnicity%60%2C%0A%20%20sum(%60number_of_admissions%60)%20AS%20%60sum_number_of_admissions%60%0AGROUP%20BY%20%60week_starting%60%2C%20%60race_or_hispanic_ethnicity%60\"\n );\n\n const formatted = data.map((d) => ({\n date: new Date(d.week_starting),\n race_or_hispanic_ethnicity: d.race_or_hispanic_ethnicity,\n count: +d.sum_number_of_admissions\n }));\n\n return formatted;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":274,"value":"sum_by_race_state = {\n const cleaned = [\n ...new Set(by_race_state.map((d) => d.race_or_hispanic_ethnicity))\n ].map((d) => ({\n RACE_GROUP: d,\n count: d3.sum(\n by_race_state.filter((r) => r.race_or_hispanic_ethnicity === d),\n (r) => r.count\n )\n }));\n\n return cleaned.sort((a, b) => b.count - a.count);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":300,"value":"norm_by_race_state = sum_by_race_state\n .filter((d) => acs1_2021.map((r) => r.group).includes(d.RACE_GROUP))\n .map((d) => {\n d.norm =\n (d.count / acs1_2021.find((r) => r.group === d.RACE_GROUP)[\"count\"]) *\n 100000;\n return d;\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":176,"value":"### III) Chart: Death Rates by Race and Ethnicity, January 2020 to May 2023, for New York City and NY State\n","pinned":false,"hidden":false,"mode":"md"},{"id":181,"value":"Plot.plot({\n grid: true,\n height: 200,\n marginRight: 70,\n marginLeft: 200,\n width: width,\n caption: html`Deaths per 100k people in NYC
Data: NYC Department of Health (as of ${cases\n .at(-1)\n [\"date\"].toLocaleDateString()}); Chart: BetaNYC`,\n x: { label: \"Deaths per 100,000 people\" },\n y: { label: null, min: 0, domain: by_race.map((d) => d[\"RACE_GROUP\"]) },\n marks: [\n Plot.barX(by_race, {\n x: \"CONFIRMED_CASE_RATE_ADJ\",\n y: \"RACE_GROUP\"\n }),\n Plot.text(by_race, {\n x: \"CONFIRMED_CASE_RATE_ADJ\",\n y: \"RACE_GROUP\",\n dx: 3,\n textAnchor: \"start\",\n text: (d) => d3.format(\".0f\")(d[\"CONFIRMED_CASE_RATE_ADJ\"])\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":427,"value":"{\n const width = 300,\n height = 18;\n const svg = d3\n .create(\"svg\")\n .attr(\"height\", height)\n .attr(\"width\", width)\n .attr(\"viewBox\", \"0 0 \" + width + \" \" + height);\n\n svg\n .append(\"line\")\n .attr(\"x1\", 110)\n .attr(\"x2\", 150)\n .attr(\"y1\", 10)\n .attr(\"y2\", 10)\n .attr(\"stroke\", \"grey\");\n svg\n .append(\"circle\")\n .attr(\"r\", 4)\n .attr(\"cx\", 110)\n .attr(\"cy\", 10)\n .attr(\"stroke\", \"black\")\n .attr(\"fill\", \"white\");\n\n svg\n .append(\"text\")\n .attr(\"x\", 0)\n .attr(\"y\", 10 + 4)\n .text(\"% of popuation\")\n .attr(\"font-size\", \"0.9rem\")\n .attr(\"text-anchor\", \"start\");\n\n svg\n .append(\"circle\")\n .attr(\"r\", 6)\n .attr(\"cx\", 150)\n .attr(\"cy\", 10)\n .attr(\"fill\", \"black\");\n\n svg\n .append(\"text\")\n .attr(\"x\", 150 + 8)\n .attr(\"y\", 10 + 4)\n .text(\"% of COVID deaths\")\n .attr(\"font-size\", \"0.9rem\")\n .attr(\"text-anchor\", \"start\");\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":347,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":513,"value":"### IV) Chart: NY City Vaccination doses","pinned":false,"hidden":false,"mode":"md"},{"id":542,"value":"Plot.plot({\n grid: true,\n height: 300,\n marginRight: 20,\n marginLeft: 55,\n width: width,\n caption: html`Daily and rolling averages of COVID-19 vaccine doses administered
Data: NYC Department of Health; Chart: BetaNYC`,\n x: {\n anchor: \"bottom\",\n tickSpacing: 30,\n domain: [new Date(2020, 2, 0), new Date(2023, 3, 1)]\n },\n y: { label: null, min: 0, tickFormat: \".2s\" },\n marks: [\n Plot.line(\n vax.filter((d) => d.date < new Date(2023, 3, 1)),\n {\n x: \"date\",\n y: \"ADMIN_ALLDOSES_7DAYAVG\",\n stroke: \"black\",\n strokeWidth: 1,\n curve: \"step-before\"\n }\n ),\n Plot.areaY(\n vax.filter((d) => d.date < new Date(2023, 3, 1)),\n {\n x: \"date\",\n y: \"ADMIN_ALLDOSES_DAILY\", //\"ADMIN_ALLDOSES_7DAYAVG\",\n fill: \"darkgrey\",\n opacity: 0.6\n }\n ),\n\n Plot.frame()\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":521,"value":"Plot.plot({\n grid: true,\n height: 300,\n marginRight: 20,\n width: width,\n caption:\n \"Cumulative number of first, second, first booster, and second booster doses; NYC Department of Health\",\n x: {\n anchor: \"bottom\",\n tickSpacing: 30,\n domain: [new Date(2020, 2, 0), deaths.at(-1).date]\n },\n y: { label: null, min: 0, tickFormat: \".2s\" },\n marks: [\n Plot.areaY(vax, {\n x: \"date\",\n y: \"ADMIN_DOSE1_CUMULATIVE\",\n fill: \"grey\"\n }),\n Plot.areaY(vax, {\n x: \"date\",\n y: \"ADMIN_DOSE2_CUMULATIVE\",\n fill: \"black\"\n }),\n Plot.areaY(vax, {\n x: \"date\",\n y: \"ADMIN_ADDITIONAL1_CUMULATIVE\",\n fill: \"red\"\n }),\n Plot.areaY(vax, {\n x: \"date\",\n y: \"ADMIN_ADDITIONAL2_CUMULATIVE\",\n fill: \"blue\"\n }),\n Plot.text(\n Plot.selectFirst({\n text: \"type\",\n frameAnchor: \"top-right\",\n dx: -6,\n dy: 6\n })\n ),\n Plot.frame()\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":515,"value":"vax = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/nychealth/covid-vaccine-data/main/doses/doses-by-day.csv\",\n d3.autoType\n );\n\n return data.map((d) => {\n d.date = new Date(d[\"DATE\"]);\n\n return d;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":340,"value":"fatalities_state = [\n { group: \"Hispanic Or Latino\", deaths: 34, pop: 29 },\n { group: \"Black Or African American\", deaths: 28, pop: 22 },\n { group: \"White\", deaths: 27, pop: 32 },\n { group: \"Asian\", deaths: 7, pop: 14 },\n { group: \"Other\", deaths: 4, pop: 3 }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":336,"value":"### Ignore ! ","pinned":false,"hidden":false,"mode":"md"},{"id":282,"value":"Plot.plot({\n grid: true,\n height: 200,\n marginRight: 70,\n marginLeft: 200,\n width: width,\n caption: `NY State Department of Health (Data as of ${d3\n .max(by_race_state, (d) => d.date)\n\n .toLocaleDateString()})`,\n x: { label: \"Deaths per 100,000 people\" },\n y: {\n label: null,\n min: 0,\n domain: acs1_2021.map((d) => d[\"group\"])\n },\n marks: [\n Plot.barX(norm_by_race_state, {\n x: \"norm\",\n y: \"RACE_GROUP\"\n }),\n Plot.text(norm_by_race_state, {\n x: \"norm\",\n y: \"RACE_GROUP\",\n dx: 3,\n textAnchor: \"start\",\n text: (d) => d3.format(\".0f\")(d.norm)\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":732,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":294,"value":"acs1_2021 = [\n { group: \"Black Or African American\", count: 2651928 },\n { group: \"White\", count: 10603657 },\n { group: \"Asian\", count: 1713659 },\n { group: \"Hispanic Or Latino\", count: 3864337 }\n // { group: \"Other Race\", count: 196044 },\n // { group: \"American Indian Or Alaskan Native\", count: 35726 },\n // { group: \"Native Hawaiian Or Other Pacific Islander\", count: 5656 }\n]","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}} 2d:"$2c:body"