2b:null 2c:["$","title",null,{"children":"Intro to Observable | 🪦 dis-2026-summer | Observable"}] 31:[{"id":"7d706e09fc3178a1ba1e0503873e3bd9c98f10c042b9de023a15391a640e7972350155bff29b169cfb2c69f36c844e9827fa8dd4aaa41cf3a74e05b98d9a8fd6","href":"https://static.observableusercontent.com/files/7d706e09fc3178a1ba1e0503873e3bd9c98f10c042b9de023a15391a640e7972350155bff29b169cfb2c69f36c844e9827fa8dd4aaa41cf3a74e05b98d9a8fd6","name":"file1.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1780251853648.239,"size":9664},{"id":"7d706e09fc3178a1ba1e0503873e3bd9c98f10c042b9de023a15391a640e7972350155bff29b169cfb2c69f36c844e9827fa8dd4aaa41cf3a74e05b98d9a8fd6","href":"https://static.observableusercontent.com/files/7d706e09fc3178a1ba1e0503873e3bd9c98f10c042b9de023a15391a640e7972350155bff29b169cfb2c69f36c844e9827fa8dd4aaa41cf3a74e05b98d9a8fd6","name":"file1@2.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1780253629957.868,"size":9664}] 2f:{"id":"1fa6d08459a58e83","slug":null,"title":"Intro to Observable","thumbnail":"0e00d5d4e4aba3dc8fd386dd27224c9bbc8efc0c4472e0c0eb341926039ae1a5","access_level":"public","create_time":"$D2026-05-31T17:51:09.464Z","update_time":"$D2026-05-31T19:03:48.085Z","view_time":null,"first_public_time":"$D2026-05-31T18:50:11.936Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"7798254538ffe6ba","login":"dis-2026-summer","name":"🪦 dis-2026-summer","avatar_url":"https://avatars-next.observableusercontent.com/ca468dc6a7c3135fb5c1b1e7b63a23e1f78da7014b68bc50114f6823acc4bf7b","tier":"education_2026"},"version":165,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"eb4f7bbe1223d626","login":"soooooren","name":"Soren","avatar_url":"https://avatars-next.observableusercontent.com/a0cbfb7accf01d8280351b1e9a55cb12658703115e12f4ec4c99b2d06e51d5ad","tier":null}],"members":[{"type":"team","id":"7798254538ffe6ba","login":"dis-2026-summer","name":"🪦 dis-2026-summer","avatar_url":"https://avatars-next.observableusercontent.com/ca468dc6a7c3135fb5c1b1e7b63a23e1f78da7014b68bc50114f6823acc4bf7b","tier":"education_2026","role":"editor"},{"type":"individual","id":"eb4f7bbe1223d626","login":"soooooren","name":"Soren","avatar_url":"https://avatars-next.observableusercontent.com/a0cbfb7accf01d8280351b1e9a55cb12658703115e12f4ec4c99b2d06e51d5ad","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Intro to Observable","cells":[{"id":0,"value":"# Intro to Observable\n\n## I'm a little slow and did not see the template site on the video description, but here is my own plain workbook where I followed along with the video :)\n### 1. Markdown Cell\nPlain text\nThis notebook is ${width} pixels wide. Adding these functions is similar to latex","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":28,"value":"

2. HTML cell

This is what pages are made of

But look what it can do!

\n \n\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":32,"value":"{\n while(true) {\n await Promises.delay(1000);\n yield \"The time is now \" + d3.timeFormat(\"%H:%M:%S, %m/%d/%Y\")(Date.now())\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"Plot.plot({\n marks: [\n Plot.dot(cars, {x: \"power (hp)\", y: \"economy (mpg)\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"mypenguins = penguins","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"smolPenguins = mypenguins","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"smolPenguins","database":"$undefined"},{"id":63,"value":"Plot.plot({\n title: \"Penguin Smolness\",\n caption: \"caption\",\n color: {\n scheme: \"Set1\",\n legend: true\n },\n marks: [\n Plot.auto(smolPenguins, {x: \"body_mass_g\", fx: \"species\", fy: \"sex\", color: \"sex\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":101,"value":"workbook = FileAttachment(\"file1@2.xlsx\").xlsx()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"workbook.sheetNames","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"data = workbook.sheet(0, {\n headers: true,\n // range: \"A1:J10\"\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"data1 = data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"data1","database":"$undefined"},{"id":107,"value":"Plot.plot({\n x: {\n grid: true\n },\n y: {\n grid: true\n },\n marks: [\n Plot.auto(data1, {x: \"x\", y: \"y\", fx: \"class\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":142,"value":"viewof confidence = Inputs.range([0, 0.99], {label: \"Confidence Interval\", step: 0.01})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":114,"value":"cellToExport = Plot.plot({\n title: \"Quartet\",\n sytle : {fontsize:16},\n marginBottom : 50,\n marginTop : 40,\n width : width,\n height : 800,\n x : { grid: true, domain: [2, 21]},\n y : { grid: true, domain: [2, 21]},\n marks: [\n Plot.frame({ strokeOpacity: 1 }),\n Plot.dot(data1, { \n fx: (d) => niceFx(d.class), \n fy: (d) => niceFy(d.class), \n x: \"x\", \n y: \"y\", \n stroke:'red',\n fill: \"orange\", \n r:5, \n tip: true }),\n Plot.linearRegressionY(data1, {\n fx: (d) => niceFx(d.class),\n fy: (d) => niceFy(d.class), \n x: \"x\", \n y: \"y\", \n stroke: \"blue\", \n fill: \"orange\", \n r:5, \n tip: true,\n ci: confidence\n \n })\n ]\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"niceFx = (key) => key == \"A\" || key == \"C\" ? 1 : 2;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":123,"value":"niceFy = (key) => key == \"A\" || key == \"B\" ? 1 : 2;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"Plot.plot({\n marks: [\n Plot.dot(penguins, {x: \"culmen_length_mm\", y: \"culmen_depth_mm\", stroke: \"species\"}),\n Plot.linearRegressionY(penguins, {x: \"culmen_length_mm\", y: \"culmen_depth_mm\", stroke: \"species\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W31","stdlib":"1"}} 30:"$2f:body"