29:null 2a:["$","title",null,{"children":"IEA (OECD) - Part 1 | Sandraviz | Observable"}] 2f:[{"id":"063311c8b924094d1b3000e1d2c14ffbc70ba616ba5560159520f087d406125ca0f768b40462f1ffd36b04e61011812ec3d934c83273795e4c3348afc88a1d78","href":"https://static.observableusercontent.com/files/063311c8b924094d1b3000e1d2c14ffbc70ba616ba5560159520f087d406125ca0f768b40462f1ffd36b04e61011812ec3d934c83273795e4c3348afc88a1d78","name":"data@4.csv","type":"text/csv","lastModified":1738001630805.363,"size":29687}] 2d:{"id":"af9c0cb1dcd75383","slug":null,"title":"IEA (OECD) - Part 1","thumbnail":"e5ccf866f385852da6e9dce28edae08dee82f183744fcdd7bda00e85eb7ffac9","access_level":"public","create_time":"$D2025-01-27T18:13:50.805Z","update_time":"$D2025-01-27T19:16:55.505Z","view_time":null,"first_public_time":"$D2025-04-30T09:35:15.861Z","fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"6661932e6ac85aef","login":"sandraviz","name":"Sandraviz","avatar_url":"https://avatars-next.observableusercontent.com/e6a2480f89a40dcfbe75f19d5081ae2296848971c4b934ed0499fd3b7f1973eb","tier":"starter_2024"},"version":2565,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"3d1beb3a90fbfe98","login":"sandraviz","name":"Sandraviz","avatar_url":"https://avatars-next.observableusercontent.com/9b19f1a074469dcaafa9bd24c7b08f4bc969889b551fe70c209497f1d03c6514","tier":null}],"members":[{"type":"individual","id":"3d1beb3a90fbfe98","login":"sandraviz","name":"Sandraviz","avatar_url":"https://avatars-next.observableusercontent.com/9b19f1a074469dcaafa9bd24c7b08f4bc969889b551fe70c209497f1d03c6514","tier":null,"role":"editor"},{"type":"team","id":"6661932e6ac85aef","login":"sandraviz","name":"Sandraviz","avatar_url":"https://avatars-next.observableusercontent.com/e6a2480f89a40dcfbe75f19d5081ae2296848971c4b934ed0499fd3b7f1973eb","tier":"starter_2024","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":1,"collection":{"id":"151e90e874803b0f","slug":"arquero-js","title":"Arquero.js","owner":{"id":"6661932e6ac85aef","login":"sandraviz","name":"Sandraviz"}},"type":"notebook","body":{"title":"IEA (OECD) - Part 1","cells":[{"id":0,"value":"# IEA (OECD) - Part 1\n## Data visualisation designer - assignment\n\nIn this notebook I check the data. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":953,"value":"### Data loading\nI load the provided dataset to the notebook to check the variables included in the dataset. There is one numeric variable: range, four different categorial variables and one time variable.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":12,"value":"data = aq.fromCSV(await FileAttachment(\"data@4.csv\").text())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1307,"value":"data.view(5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":956,"value":"### Data cleaning \nRange and year are numeric variables, which appear as string variables, hence I change it back to numeric variables. Then I exclude missing values and clean out wrong values by using filter on range and year. Last but not least I order the dataset by the variable \"range\" to check extreme values.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1702,"value":"data\n .derive({ range: (d) => `${d[\"range\"]} ` * 1 })\n .derive({ year: (d) => `${d[\"year\"]} ` * 1 })\n .filter((d) => d.range > 0)\n .filter((d) => d.year > 0)\n .orderby(aq.desc(\"range\"))\n .view(5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2206,"value":"The two highest values seem to be outliers and are the only two values available for the year 2024, therefore I decided to exclude them for further analysis.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2209,"value":"data\n .derive({ range: (d) => `${d[\"range\"]} ` * 1 })\n .derive({ year: (d) => `${d[\"year\"]} ` * 1 })\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .orderby(aq.desc(\"range\"))\n .view(5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2212,"value":"Looking at the minimum values they seem at least from a statistical point of view ok.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2086,"value":"data\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .derive({ year: (d) => `${d[\"year\"]} ` * 1 })\n .filter((d) => d.range2 > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .orderby(\"range2\")\n .view(5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1742,"value":"Saving the file as an array of objects. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":949,"value":"data_final = data\n .derive({ range: (d) => `${d[\"range\"]} ` * 1 })\n .filter((d) => d.range > 0)\n .derive({ year: (d) => `${d[\"year\"]} ` * 1 })\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .orderby(aq.desc(\"range\"))\n .objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1788,"value":"### Categorial variables\nNow I check the sample size and average values by the different categories: technology, year, region, category and type.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1658,"value":"#### 1. Technology\nThe dataset includes far less data points with the technology fuel cell. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1526,"value":"data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"technology\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1581,"value":"data_tec = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"technology\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1777,"value":"Fuel has a range on average twice as big as electric. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1559,"value":"Plot.plot({\n marks: [\n Plot.barX(data_tec, {\n x: \"mean\",\n y: \"technology\",\n fill: \"technology\"\n }),\n Plot.text(data_tec, {\n x: \"mean\",\n y: \"technology\",\n fill: \"technology\",\n text: (d) => d.mean.toFixed(0),\n dx: 20\n }),\n Plot.text(data_tec, {\n x: \"mean\",\n y: \"technology\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 600],\n axis: null,\n reverse: false\n },\n color: {\n type: \"ordinal\",\n range: [\"#06d6a0\", \"#ff312e\"],\n legend: false,\n width: 150,\n height: 40,\n tickSize: 0,\n fontsize: 18\n },\n height: 100,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1653,"value":"#### 2. Year\nAlready having removed the year 2024 all other category sample sizes are at least 10. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1512,"value":"data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"year\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .view(6)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2560,"value":"All","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1793,"value":"Plot.plot({\n marks: [\n Plot.barX(data_year, {\n x: \"mean\",\n y: \"year\",\n fill: \"black\"\n }),\n Plot.text(data_year, {\n x: \"mean\",\n y: \"year\",\n fill: \"black\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_year, {\n x: \"mean\",\n y: \"year\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2275,"value":"Electric does not increase much by year.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1842,"value":"Plot.plot({\n marks: [\n Plot.barX(data_year_electric, {\n x: \"mean\",\n y: \"year\",\n fill: \"#06d6a0\"\n }),\n Plot.text(data_year_electric, {\n x: \"mean\",\n y: \"year\",\n fill: \"#06d6a0\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_year_electric, {\n x: \"mean\",\n y: \"year\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1876,"value":"Fuel by year has generally very small sample size less than 10 but we see an increase year by year.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1815,"value":"Plot.plot({\n marks: [\n Plot.barX(data_year_fuel, {\n x: \"mean\",\n y: \"year\",\n fill: \"#ff312e\"\n }),\n Plot.text(data_year_fuel, {\n x: \"mean\",\n y: \"year\",\n fill: \"#ff312e\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_year_fuel, {\n x: \"mean\",\n y: \"year\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1669,"value":"#### Region\n\nAustralia and Latin America have very small sample sizes. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1528,"value":"data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"region\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2562,"value":"All","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1891,"value":"Plot.plot({\n marks: [\n Plot.barX(data_region, {\n x: \"mean\",\n y: \"region\",\n fill: \"black\"\n }),\n Plot.text(data_region, {\n x: \"mean\",\n y: \"region\",\n fill: \"black\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_region, {\n x: \"mean\",\n y: \"region\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n domain: d3.sort(data_region, (d) => -d.mean).map((d) => d.region),\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2564,"value":"Electric","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1903,"value":"Plot.plot({\n marks: [\n Plot.barX(data_region_electric, {\n x: \"mean\",\n y: \"region\",\n fill: \"#06d6a0\"\n }),\n Plot.text(data_region_electric, {\n x: \"mean\",\n y: \"region\",\n fill: \"#06d6a0\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_region_electric, {\n x: \"mean\",\n y: \"region\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n domain: d3.sort(data_region_electric, (d) => -d.mean).map((d) => d.region),\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1673,"value":"#### Category\n\nLooks good.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1913,"value":"data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"category\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2548,"value":"All","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1922,"value":"Plot.plot({\n marks: [\n Plot.barX(data_category, {\n x: \"mean\",\n y: \"category\",\n fill: \"black\"\n }),\n Plot.text(data_category, {\n x: \"mean\",\n y: \"category\",\n fill: \"black\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_category, {\n x: \"mean\",\n y: \"category\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n domain: d3.sort(data_category, (d) => -d.mean).map((d) => d.category),\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2550,"value":"Electric","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1930,"value":"Plot.plot({\n marks: [\n Plot.barX(data_category_electric, {\n x: \"mean\",\n y: \"category\",\n fill: \"#06d6a0\"\n }),\n Plot.text(data_category_electric, {\n x: \"mean\",\n y: \"category\",\n fill: \"#06d6a0\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_category_electric, {\n x: \"mean\",\n y: \"category\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n domain: d3\n .sort(data_category_electric, (d) => -d.mean)\n .map((d) => d.category),\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1676,"value":"#### Vehicle type\nLooks good.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1482,"value":"data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"vehicle_type\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2552,"value":"All","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1954,"value":"Plot.plot({\n marks: [\n Plot.barX(data_vehicle_type, {\n x: \"mean\",\n y: \"vehicle_type\",\n fill: \"black\"\n }),\n Plot.text(data_vehicle_type, {\n x: \"mean\",\n y: \"vehicle_type\",\n fill: \"black\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_vehicle_type, {\n x: \"mean\",\n y: \"vehicle_type\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n domain: d3\n .sort(data_vehicle_type, (d) => -d.mean)\n .map((d) => d.vehicle_type),\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2554,"value":"Electric","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1958,"value":"Plot.plot({\n marks: [\n Plot.barX(data_vehicle_type_electric, {\n x: \"mean\",\n y: \"vehicle_type\",\n fill: \"#06d6a0\"\n }),\n Plot.text(data_vehicle_type_electric, {\n x: \"mean\",\n y: \"vehicle_type\",\n fill: \"#06d6a0\",\n text: (d) => d.mean.toFixed(0),\n dx: 15\n }),\n Plot.text(data_vehicle_type_electric, {\n x: \"mean\",\n y: \"vehicle_type\",\n fill: \"white\",\n text: (d) => d.count.toFixed(0),\n dx: -15\n })\n ],\n y: {\n domain: d3\n .sort(data_vehicle_type_electric, (d) => -d.mean)\n .map((d) => d.vehicle_type),\n label: null,\n tickSize: 0,\n padding: 0.06\n },\n x: {\n domain: [0, 1600],\n axis: null,\n reverse: false\n },\n height: 200,\n width: width,\n marginLeft: 80\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":16,"value":"### Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1785,"value":"data_year = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"year\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1837,"value":"data_year_electric = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .filter((d) => d.technology == \"Electric\")\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"year\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1817,"value":"data_year_fuel = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .filter((d) => d.technology == \"Fuel_Cell\")\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"year\")\n .rollup({\n mean: (d) => op.mean(d.range),\n median: (d) => op.median(d.range),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1897,"value":"data_region = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"region\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1905,"value":"data_region_electric = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .filter((d) => d.technology == \"Electric\")\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"region\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1919,"value":"data_category = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"category\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1925,"value":"data_category_electric = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .filter((d) => d.technology == \"Electric\")\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"category\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1951,"value":"data_vehicle_type = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"vehicle_type\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1960,"value":"data_vehicle_type_electric = data\n .filter((d) => d.range > 0)\n .filter((d) => (d.year > 0) & (d.year < 2024))\n .filter((d) => d.technology == \"Electric\")\n .derive({ range2: (d) => `${d[\"range\"]} ` * 1 })\n .groupby(\"vehicle_type\")\n .rollup({\n mean: (d) => op.mean(d.range2),\n median: (d) => op.median(d.range2),\n max: (d) => op.max(d.range2),\n count: op.count()\n })\n .orderby(aq.desc(\"mean\"))\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1611,"value":"width = 900","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2416,"value":"color_iea = \"#0000FF\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":9,"value":"d3 = require(\"d3@7\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":920,"value":"import { aq, op, table } from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2481,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"}],"files":"$W2f","stdlib":"1","titling":"auto"}} 2e:"$2d:body"