29:null 2a:["$","title",null,{"children":"Introduction to data wrangling and analysis in JavaScript — Session 2 follow-along notebook | Flower's Workspace | Observable"}] 2f:[{"id":"ea49c479fcac9cba031521069332caba185b01c89a71f3af01f05229c5965051b08144f012bc7a2afbb9b09009dcdd6760c00ead78562c761b2465a670cfa730","href":"https://static.observableusercontent.com/files/ea49c479fcac9cba031521069332caba185b01c89a71f3af01f05229c5965051b08144f012bc7a2afbb9b09009dcdd6760c00ead78562c761b2465a670cfa730","name":"world_bank_data.csv","type":"text/csv","lastModified":1696442678081.215,"size":606010}] 2d:{"id":"bc923470e00e66e2","slug":null,"title":"Introduction to data wrangling and analysis in JavaScript — Session 2 follow-along 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Workspace","avatar_url":"https://avatars-next.observableusercontent.com/3e6606efd3316c7d9d86007f9b1721ea3d28be804657430350b77a5861d4d10b","tier":"starter_2024"},"version":300,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"4bf1f0c7fd6658c3","login":"flower2","name":"Flower","avatar_url":"https://avatars-next.observableusercontent.com/f9f324ed4b0dac409c4d0720a458c59aa1931869b67056fb96a4b2175e0cdc3d","tier":null}],"members":[{"type":"individual","id":"4bf1f0c7fd6658c3","login":"flower2","name":"Flower","avatar_url":"https://avatars-next.observableusercontent.com/f9f324ed4b0dac409c4d0720a458c59aa1931869b67056fb96a4b2175e0cdc3d","tier":null,"role":"editor"},{"type":"team","id":"d09ae74fae02abd5","login":"flower2-ws","name":"Flower's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/3e6606efd3316c7d9d86007f9b1721ea3d28be804657430350b77a5861d4d10b","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":{"id":"de79c9cfc5c050e6","slug":"daisys-data-visualization-class","title":"Daisy's Data Visualization Class","owner":{"id":"d09ae74fae02abd5","login":"flower2-ws","name":"Flower's Workspace"}},"type":"notebook","body":{"title":"Introduction to data wrangling and analysis in JavaScript — Session 2 follow-along notebook","cells":[{"id":12,"value":"# Introduction to data wrangling and analysis in JavaScript — Session 2 follow-along notebook\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":18,"value":"- [Session 2 slide deck](https://docs.google.com/presentation/d/e/2PACX-1vREbGCYcWAUeQeTdWuEmogi2iHio2anw890AY0v1fH3s1HkOfxzlm6FbrqqPl8qjnVG_U1BIKgiR0iP/pub?start=false&loop=false&delayms=3000)\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":22,"value":"\n---\n## Activity 1 - Warm up\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":26,"value":"Here's an array (**biking**) containing multiple objects:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"biking = [\n { day: \"Monday\", miles: 6.2, time_hr: 0.53 },\n { day: \"Tuesday\", miles: 10.0, time_hr: 1.02 },\n { day: \"Wednesday\", miles: 4.9, time_hr: 0.48 },\n { day: \"Thursday\", miles: 0, time_hr: 0 },\n { day: \"Friday\", miles: 18.5, time_hr: 1.59 },\n { day: \"Saturday\", miles: 7.3, time_hr: 0.86 },\n { day: \"Sunday\", miles: 0, time_hr: 0 }\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"Let's refresh our skills from Session 1 in the cells below:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":38,"value":"// Find the miles biked on Friday:\nbiking[4].miles\n\n// Alternatively: biking[biking.map(d => d.day).indexOf(\"Friday\")].miles","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"showMe(`biking[4].miles`, {dependencies: {biking}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"// For each day, find the average biking speed.\nbiking.map(d => d.miles / d[\"time_hr\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"showMe(`biking.map(d => d.miles / d.time_hr)`, {dependencies: {biking}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"---\n## Activity 2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":151,"value":"Continuing with the **biking** array created above:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":149,"value":"// Only keep objects (rows) where miles is greater than 10:\nbiking.filter(d => d.miles > 10)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":146,"value":"showMe(`biking.filter(d => d.miles > 10)`, {dependencies: {biking}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":144,"value":"// Keep rows for Saturday and Monday:\nbiking.filter(d => d.day === \"Saturday\"|| d.day === \"Monday\" )","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"showMe(`biking.filter(d => d.day == \"Saturday\" || d.day == \"Monday\")`, {dependencies: {biking}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":137,"value":"// Keep rows where miles is less than 10 AND time_hr is less than 0.5:\nbiking.filter(d => d.miles < 10 && d.time_hr < .5 )","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":135,"value":"showMe(`biking.filter(d => d.miles < 10 && d.time_hr < 0.5)`, {dependencies: {biking}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"// Keep rows *except* for Thursday:\nbiking.filter(d => d.day !== \"Thursday\" )","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"showMe(`biking.filter(d => d.day != \"Thursday\")`, {dependencies: {biking}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"---\n## Activity 3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":124,"value":"// Keep all existing properties; add a new one (...d) named 'km' with miles converted to kilometers;\nbiking_km = biking.map(d => ({...d, km: d.miles*1.61, dayanimal: d.day+\"Cat\"}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"showMe(`biking_km = biking.map(d => ({...d, km: d.miles * 1.61}))`, {dependencies: {biking}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"---\n## Activity 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":270,"value":"biking_km","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":186,"value":"carData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":115,"value":"Here, we create an Arquero table from the **carData** array shown above:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":112,"value":"carTable = aq.from(carData)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":188,"value":"Let's do the following wrangling steps together using Arquero verbs:\n\n- Only keep properties (columns) for \"name\", \"economy (mpg)\", \"cylinders\", and \"weight (lb)\"\n- Only keep rows for cars with 4 cylinders, and where \"economy (mpg)\" is not `null` (empty)\n- Add a new column called weight_kg, with \"weight (lb)\" converted to kilograms (multiply the pounds by 0.45)\n- Sort the data by increasing value of \"economy (mpg)\"\n- Rename the \"economy (mpg)\" column to \"mpg\"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":107,"value":"// Add your data wrangling (using Arquero) here:\ncarTable\n .select(\"name\", \"economy (mpg)\", \"cylinders\", \"weight (lb)\")\n .filter(d => d.cylinders == 4 && d[\"economy (mpg)\"] != null)\n .derive({weight_kg: d => d[\"weight (lb)\"] * .45})\n .orderby(\"economy (mpg)\")\n .rename({'economy (mpg)' : \"MPG\"})\n .view()\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":109,"value":"showMe(`carTable\n .select(\"name\", \"economy (mpg)\", \"cylinders\", \"weight (lb)\")\n .filter((d) => d.cylinders == 4 && d['economy (mpg)'] != null)\n .derive({weight_kg: d => d['weight (lb)'] * 0.45})\n .orderby('economy (mpg)')\n .rename({'economy (mpg)': 'mpg'})\n .view()`,\n {dependencies: {carTable}})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":101,"value":"\n---\n## Activity 5\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":97,"value":"The data table below previews **wb**, an array with several indicators (CO2 emissions, annual GDP, life expectancy, and population) for different countries over time. Data: World Bank Open Data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":104,"value":"wb = await FileAttachment(\"world_bank_data.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"wb","database":"$undefined"},{"id":191,"value":"Using only Array map and filter methods, do the following: \n\n- Create a new array named wb2019...\n- ...that includes columns \"country\", \"year\", \"co2\", \"region\" and \"co2_thousands\". The first four should contain the same values currently in those properties; \"co2_thousands\" should contain the co2 property value divided by 1000.\n- Filter to only include objects where the year value is 2019","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":94,"value":"// Use Array map and filter to do the wrangling steps above, storing the output as wb2019:\nwb2019 = wb.map(d => ({country : d.country, year: d.year, co2: d.co2, region: d.region, co2_thousands: d.co2/1000}))\n.filter(d => d.year == 2019)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"showMe(`wb2019 = wb\n .map((d) => ({\n country: d.country,\n year: d.year,\n co2: d.co2,\n region: d.region,\n co2_thousands: d.co2 / 1000\n }))\n .filter(\n (d) =>\n d.year == 2019\n )`, \n {dependencies: {wb}})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"\nNow let's try this with Arquero! First, we'll convert to an Arquero table:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":79,"value":"wbTable = aq.from(wb)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"In the cell below, perform the same wrangling steps as above using Arquero verbs:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":74,"value":"// Use Arquero verbs to do the same wrangling steps:\nwbEAPArquero = wbTable\n .select(\"country\", \"year\", \"co2\", \"region\")\n .derive({ co2_thousands: (d) => d.co2 / 1000 })\n .filter(d => d.year == 2019)\n .view()\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"showMe(`wbEAPArquero = wbTable\n .select(\"country\", \"year\", \"co2\", \"region\")\n .derive({ co2_thousands: (d) => d.co2 / 1000 })\n .filter(d => d.year == 2019)\n .view()`,\n {dependencies: {wbTable}})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"\n---\n## Activity 6\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":64,"value":"// Create a chart of the top 10 CO2 emitting countries in 2019 (using the wb2019 array created above)\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":292,"value":"Plot.plot({\n marks: [\n Plot.barX(wb2019, {x: \"co2\", y: \"country\", fill: \"blueviolet\", stroke:\"pink\", sort: {y: \"x\", reverse: true, limit: 10}}),\n\n\n \n Plot.ruleX([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":201,"value":"Note: the ShowMe! below depends on wb2019 being created above, so once we've made wb2019, the error below should be resolved! ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":59,"value":"showMe(\n `Plot.plot({\n marks: [\n Plot.barX(wb2019, {\n x: (d) => d.co2 / 1e6,\n y: \"country\",\n fill: \"blueviolet\",\n stroke: \"gray\",\n sort: { y: \"x\", reverse: true, limit: 10 }\n }),\n Plot.text(wb2019, {\n x: (d) => d.co2 / 1e6,\n y: \"country\",\n text: (d) => d.co2 / 1e6,\n dx: 2,\n textAnchor: \"start\"\n }),\n Plot.ruleX([0])\n ],\n marginLeft: 200,\n x: { domain: [0, 12], grid: true, label: \"CO2\" }\n})`,\n { dependencies: { wb2019 } }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"

\n---\n### Appendix\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":53,"value":"carData = cars","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"import {aq, op} from \"@uwdata/arquero\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"import { showMe } from \"@observablehq/show-me\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W2f","stdlib":"1","titling":"auto"}} 2e:"$2d:body"