2b:null 2c:["$","title",null,{"children":"Proposal: Explore the relationship between three economy factors(GDP, CPI and debt-to-GDP ration) and unemployment rate in different countries | LilyLi | Observable"}] 31:T5f8,## Introduction The economy is a complex system that is affected by a variety of factors. When economy has hard time, businesses may struggle to stay afloat, leading to layoffs and a rise in the unemployment rate. This can result in reduced consumer spending, as those who are out of work have less money to spend, further exacerbating the economic downturn. GDP stands for gross domestic product, which is meant to represent the total dollar value of all goods and services produced over a specific period of time. Debt-to-GDP ratio is the metric comparing a country's public debt to its gross domestic product The higher the debt-to-GDP ratio, the less likely the country will pay back its debt and the higher its risk of default, which could cause a financial panic in the domestic and international markets. The CPI, which stands for consumer price index, is a measure of a theoretical basket of goods meant to represent what people are buying The predetermined basket of goods is averaged and the goods are weighted against one anther based on how important they are to a household. Unemployment rates are often used as a key indicator of the health of the economy. In this project, **I want to explore the relationship between three economy factors(GDP, Debt and CPI) and unemployment rate in different countries**, **what factor can strongly indicate a coming massive layoff** and ultimately **try to find a pattern to be able to predict the coming layoffs** to prepare us for the potential stressful situation. 32:T437,- Description: I downloaded all the datasets from [Fred website](https://fred.stlouisfed.org/). There are 12 datasets(GDP, CPI, debtPercentage and unemployment rate of US, Canada and Brazil) and I already combine them together. - Links: - [US GDP](https://fred.stlouisfed.org/series/GDP) - [US CPI](https://fred.stlouisfed.org/series/CORESTICKM159SFRBATL) - [US total debt](https://fred.stlouisfed.org/series/GFDEBTN) - [US Unemployment rate](https://fred.stlouisfed.org/series/UNRATE) - [Canada GDP](https://fred.stlouisfed.org/series/NGDPRSAXDCCAQ) - [Canada CPI](https://fred.stlouisfed.org/series/CPALCY01CAM661N) - [Canada debt ratio](https://fred.stlouisfed.org/series/HDTGPDCAQ163N) - [Canada Unemployment rate](https://fred.stlouisfed.org/series/LRUNTTTTCAM156S) - [Brazil GDP](https://fred.stlouisfed.org/series/NGDPRSAXDCBRQ) - [Brazil CPI](https://fred.stlouisfed.org/series/CPALTT01BRM659N) - [Brazil debt ratio](https://fred.stlouisfed.org/series/GGGDTABRA188N) - [Brazil Unemployment rate](https://fred.stlouisfed.org/series/LRUNTTTTBRM156S)33:T4c5,us_unemployment_gdp_chart = { const onmouseover = vl .selectSingle() .on("mouseover") .encodings("x") .nearest(true) .empty({ created: [] }) const line_unemployment = vl.markLine().data(comboDataSet.filter(d => d.type=='unemployment')).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ).width(550) .height(250); const line_income = vl.markLine().data(comboDataSet.filter(d => d.type=='gdp')).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('US GDP'), vl.color().fieldN("type"), ).width(550) .height(250); const line = vl.layer(line_unemployment, line_income).resolve({scale: {y: 'independent'}}) // const points = line_unemployment.markPoint({ tooltip: true, size: 1 }) // .select(onmouseover) // .encode( // vl // .color() // .if(onmouseover, vl.color().value("firebrick")) // .value("steelblue") // ) return line // .render(); }34:T4f0,canada_unemployment_gdp_chart = { const onmouseover = vl .selectSingle() .on("mouseover") .encodings("x") .nearest(true) .empty({ created: [] }) const line_unemployment = vl.markLine().data(comboDataSet_canada.filter(d => d.type=='unemployment')).width(width - 250).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ).width(550) .height(250); const line_income = vl.markLine().data(comboDataSet_canada.filter(d => d.type=='gdp')).width(width - 250).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Canada GDP'), vl.color().fieldN("type"), ).width(550) .height(250); const line = vl.layer(line_unemployment, line_income).resolve({scale: {y: 'independent'}}) const points = line_unemployment.markPoint({ tooltip: true, size: 1 }) .select(onmouseover) .encode( vl .color() .if(onmouseover, vl.color().value("firebrick")) .value("steelblue") ) return line // return vl.layer(points, line) // .render(); }35:T523,japan_unemployment_gdp_chart = { const onmouseover = vl .selectSingle() .on("mouseover") .encodings("x") .nearest(true) .empty({ created: [] }) const line_unemployment = vl.markLine().data(comboDataSet_japan.filter(d => d.type=='unemployment')).width(width - 250).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ).width(550) .height(250); const line_income = vl.markLine().data(comboDataSet_japan.filter(d => d.type=='gdp')).width(width - 250).encode( vl.x().fieldT("date").timeUnit('yearmonth'), vl.y().fieldQ("value").title('Japan GDP'), vl.color().fieldN("type"), ).width(550) .height(250); const line = vl.layer(line_unemployment, line_income).resolve({scale: {y: 'independent'}}) // return line.render() const points = line_unemployment.markPoint({ tooltip: true, size: 1 }) .select(onmouseover) .encode( vl .color() .if(onmouseover, vl.color().value("firebrick")) .value("steelblue") ) return line // return vl.layer(points, line) // .render(); }36:T453,h1b_chart = { const onmouseover = vl .selectSingle() .on("mouseover") .encodings("x") .nearest(true) .empty({ created: [] }); const line_unemployment = vl.markLine().data(comboDataSet.filter(d => d.type=='unemployment' && d.date.getFullYear() >= 2009)).width(width - 250).encode( vl.x().fieldT("date").timeUnit('year'), vl.y().mean("value").title('Unemployment Percentage Rate'), vl.color().fieldN("type"), ) const line_h1b = vl .markLine() .data(h1b) .encode( vl.x().fieldT("date").title("Year").timeUnit('year'), vl.y().fieldQ('value').title("H1b Numbers"), vl.color().fieldN('type'), ).width(width - 250) const line = vl.layer(line_unemployment, line_h1b).resolve({scale: {y: 'independent'}}) // const points = line_h1b // .markPoint({ tooltip: true, size: 1 }) // .select(onmouseover) // .encode( // vl // .color() // .if(onmouseover, vl.color().value("firebrick")) // .value("steelblue") // ); return line.render() // return vl.layer(points, line).render(); 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2f:{"id":"cdf5799ff0767f97","slug":null,"title":"Proposal: Explore the relationship between three economy factors(GDP, CPI and debt-to-GDP ration) and unemployment rate in different countries","thumbnail":"ac5287c421d62e45a289cb1bbb1a9cc20ba4cf2ba67d2e0e05003ed307598e64","access_level":"public","create_time":"$D2023-02-22T23:31:10.970Z","update_time":"$D2023-04-05T00:30:56.857Z","view_time":null,"first_public_time":"$D2023-02-22T23:31:10.970Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"132517a03a3dd931","login":"lilyneu","name":"LilyLi","avatar_url":"https://avatars-next.observableusercontent.com/57d00e99e7bd16a222a22f6669c9ce03fafca3705a95350ea5f4e3608b1f0ec9","tier":"starter_2024"},"version":1249,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"d06cccb760acb8c3","login":"lilyneu","name":"LilyLi","avatar_url":"https://avatars-next.observableusercontent.com/57d00e99e7bd16a222a22f6669c9ce03fafca3705a95350ea5f4e3608b1f0ec9","tier":null}],"members":[{"type":"individual","id":"d06cccb760acb8c3","login":"lilyneu","name":"LilyLi","avatar_url":"https://avatars-next.observableusercontent.com/57d00e99e7bd16a222a22f6669c9ce03fafca3705a95350ea5f4e3608b1f0ec9","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Proposal: Explore the relationship between three economy factors(GDP, CPI and debt-to-GDP ration) and unemployment rate in different countries","cells":[{"id":0,"value":"# Proposal: Explore the relationship between three economy factors(GDP, CPI and debt-to-GDP ration) and unemployment rate in different countries\n\n- Team Member: Fengrui Gan, Jiangyan Li\n- CS7295 Final project proposal\n- tweet: https://twitter.com/neu7295/status/1631005860033077251","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":592,"value":"### Dataset Links:\n[Global Debt to GDP ratio](https://www.imf.org/external/datamapper/GG_DEBT_GDP@GDD/SWE)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":13,"value":"## Example insights we want to explore \n\n- Target Audience: Anyone who is interested in learning more about economy and unemployment rate.\n\n- **Insight 1:** Positive GDP percentage change, negative CPI monthly percentage change and negative debt-to-GDP ratio monthly percentage change indicate lower unemployment rate in different countries.\n - T1: **Compare** the shape of the GDP, CPI, debt-to-GDP ratio and unemployment rate over time per country **(compare, trend)**\n - T2: **Identify** which factor that affect the unemployment rate the most **(Identify, Features)**\n\n- **Insight 2:** GDP, CPI and debt-to-GDP ratio affect unemployment rate more in US and Canada compared to Brazil.\n - T1: **Present** the correlation of the GDP, CPI, debt-to-GDP ratio and unemployment rate over time in US, Canada and Brazil. **(present, trend)**\n - T2: **Compare** the correlation of the GDP, CPI, debt-to-GDP ratio and unemployment rate over time in US, Canada and Brazil. **(compare, trend)**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":15,"value":"## Dataset we plan to use","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":192,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":229,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":231,"value":"## Data Process (Worked on this on March 13)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":246,"value":"- combine gdp, debt, unemployment rate and cpi of each country into one dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":100,"value":"usData = {\n const data = []\n usunemployment.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type:'unemployment', value: d.UNRATE, country: 'US'})\n })\n \n usdebt.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type:'debt percentage', value: d.GFDEGDQ188S, country: 'US'})\n })\n\n usgdp.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type:'gdp', value: d.GDP, country: 'US'})\n })\n\n usCpi.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type:'cpi', value: d.CORESTICKM159SFRBATL, country: 'US'})\n })\n\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"canadaData = {\n const data = []\n canadaunemployment.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type: 'unemployment', value: d.LRUNTTTTCAM156S, country:'Canada'})\n })\n canadadebt.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type: 'debt percentage', value: d.DEBTTLCAA188A, country:'Canada'})\n })\n\n canadagdp.forEach(d => {\n const time = new Date(d.DATE)\n const yearMonth = new Date(time.getFullYear(), time.getMonth())\n data.push({date: yearMonth, type: 'gdp', value: ((d.NGDPRSAXDCCAQ)/1000)*0.73, country:'Canada'})\n })\n // canadaCpi.forEach(d => {\n // const time = new Date(d.DATE)\n // const yearMonth = new Date(time.getFullYear(), time.getMonth())\n // data.push({date: yearMonth, type: 'cpi', value: d.CPALCY01CAM661N, country:'Canada'})\n // })\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":800,"value":"japanData = {\n const res = []\n japanunemployment1.forEach(d => {\n res.push({\n date: new Date(d.DATE),\n type:'unemployment',\n value: d.LRUN64TTJPM156S,\n country: 'Japan'\n })\n })\n\n japanGDP.forEach(d => {\n res.push({\n date: new Date(d.DATE),\n type:'gdp',\n value: d.GDP,\n country: 'Japan'\n })\n })\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"- Combine brazilData, usData and canadaData into one dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":238,"value":"dataSet = {\n const res = []\n // eu_unemployment_rate.forEach(d => res.push(d))\n usData.forEach(d => res.push(d))\n canadaData.forEach(d => res.push(d))\n japanData.forEach(d => res.push(d))\n // ukData.forEach(d => res.push(d))\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":485,"value":"global_keys = Object.keys(Global_debt_gdp[1])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"debt_to_gdp_ratio_global = {\n const res = []\n const years = global_keys.slice(0, global_keys.length-1)\n Global_debt_gdp.forEach(data => {\n const country = data['General Government Debt (Percent of GDP)']\n \n if (country!=\"\"){\n for (let year of years){\n data[year]!== NaN && res.push({\n country: country,\n date: parseInt(year),\n type:'debt',\n value: parseInt(data[year])\n })\n }\n } \n })\n return res.filter(d => d.date >= 2000)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":505,"value":"global_countries = {\n const res = new Set()\n debt_to_gdp_ratio_global.forEach(d => res.add(d.country))\n const ret = []\n for (let r of res){ret.push(r)}\n return ret\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"global_years = {\n const res = new Set()\n debt_to_gdp_ratio_global.forEach(d => res.add(d.date))\n const ret = []\n for (let r of res){ret.push(r)}\n return ret\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":595,"value":"create_at_formatter = d3.timeParse(\"%m/%d/%Y %H:%M:%S %p\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":597,"value":"covid = united_states_covid19_cases_and_deaths_by_state_ov.map((d) => {\n d.parse_create_date = create_at_formatter(d.created_at) \n delete d.conf_cases\n delete d.prob_cases\n delete d.pnew_case\n delete d.conf_death\n delete d.pnew_death\n delete d.prob_death\n delete d.consent_deaths\n delete d.parse_crea\n delete d.parse_created_at\n delete d.parse_submission_date\n delete d.consent_cases\n delete d.submission_date\n return d;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":609,"value":"states = {\n const seen = new Set()\n const result = []\n covid.forEach(d => {\n if (!seen.has(d.state)){\n seen.add(d.state)\n result.push(d.state)\n }\n })\n return result\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":729,"value":"videoGame","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":648,"value":"videoGameData = {\n const res = []\n const temp = {}\n const years = new Set()\n const types = new Set()\n \n videoGame.forEach(d => {\n years.add(d.Year_of_Release)\n types.add(d.Genre)\n })\n for (let y of years){\n temp[y] = {}\n for (let t of types){\n temp[y][t] = 0\n }\n }\n\n videoGame.forEach(d => {\n temp[d.Year_of_Release][d.Genre] += d.NA_Sales\n })\n for (let i of Object.entries(temp)){\n const year = new Date(i[0])\n for (let j of Object.entries(temp[i[0]])){\n const type = j[0]\n res.push({\n year: year,\n type: type,\n sales:temp[i[0]][type]\n })\n }\n \n }\n return res\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":790,"value":"h1b_year = {\n const res = []\n for (let i = 2009; i<=2022; i++){\n res.push(i)\n }\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1112,"value":"h1b_dataset = {\n const h1bData = [h1b_2009, h1b_2010, h1b_2011, h1b_2012, h1b_2013, h1b_2014, h1b_2015, h1b_2016, h1b_2017, h1b_2018, h1b_2019, h1b_2020, h1b_2021, h1b_2022]\n \n const res = {}\n \n for (let i = 2009; i<=2022; i++){\n res[i] = {\n approvals: 0,\n denials: 0\n }\n }\n \n for (let h of h1bData){\n \n h.forEach(d => {\n if (Object.keys(d).includes('Initial Approvals')){\n res[d['Fiscal Year']]['approvals'] += isNaN(d['Initial Approvals']) ? 0 : d['Initial Approvals']\n }else{\n res[d['Fiscal Year']]['approvals'] += isNaN(d['Initial Approval']) ? 0 : d['Initial Approval']\n }\n\n if (Object.keys(d).includes('Initial Denials')){\n res[d['Fiscal Year']]['denials'] += isNaN(d['Initial Denials']) ? 0 : d['Initial Denials']\n }else{\n res[d['Fiscal Year']]['denials'] += isNaN(d['Initial Denial']) ? 0 : d['Initial Denial']\n }\n })\n }\n \n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1179,"value":"h1b = {\n const res = []\n for (let year of h1b_year){\n res.push({\n date: new Date(year, 1),\n type: 'approvals',\n value: h1b_dataset[year]['approvals']\n })\n res.push({\n date: new Date(year, 1),\n type: 'denials',\n value: h1b_dataset[year]['denials']\n })\n }\n return res\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":266,"value":"navio(dataSet)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":348,"value":"- Get all the countries from the dataSet","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":346,"value":"countries = {\n const res = new Set()\n dataSet.forEach(data => res.add(data.country))\n const res_list = []\n for (let i of res){ res_list.push(i)}\n return res_list\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":383,"value":"- Get all the types from the dataSet","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":380,"value":"types = {\n const res = new Set()\n dataSet.forEach(d => res.add(d.type))\n const res_list = []\n for (let i of res) res_list.push(i)\n return res_list\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":263,"value":"### Data Abstraction (Worked on March 13)\n\n- **Dataset type**\n - Table, Temporal\n- **Attributes**\n - date: ordinal, sequential\n - value: quantitative, sequential\n - country: categorical\n - type: categorical\n- **Derived Attributes**\n - None\n- **Data Availability**\n - Static (end date is Nov 11, 2022)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":291,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":293,"value":"## Task Abstraction\n\n- I choose insight 1 for this project\n- **Target Audience**: Anyone who is interested in learning more about economy and unemployment rate.\n- T1: **Present** the correlation of the GDP, CPI, debt-to-GDP ratio and unemployment rate over time in US, Canada and Brazil. **(present, trends)**\n- T2: **Identify** the correlation of the GDP, CPI, debt-to-GDP ratio and unemployment rate over time in US, Canada and Brazil. **(Identify, features)**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":257,"value":"

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":301,"value":"## How Abstraction\n- **Mark**: Line\n- **Channel**:\n - Date: horizontal position\n - Value: vertical position\n - Color Hue: Type\n- **Expressivity**: By using a line chart, we can see the trend over last three years and find out the number of certain date. Therefore, the line chart shows all the information in the dataset. So it is expressive.\n- **Effectiveness**: It is easy to compare the trends by looking at the line chart. So it is effective.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":299,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":314,"value":"## Visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":399,"value":"### Three countries' unemployment rate overtime","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":323,"value":"viewof time = html`\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":326,"value":"timeTruncateSelected = timePartOptions.slice(0, time).join(\"\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":615,"value":"viewof state = Inputs.select(\n states, { label: \"Choose the state: \", value: \"CA\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":617,"value":"viewof type = Inputs.select(\n ['tot_cases', 'tot_death', 'new_case', 'new_death'],\n {\n label: 'Select which type: ',\n value: 'new_case'\n }\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":613,"value":"covidChart = {\n const x = vl.x().fieldT(\"parse_create_date\");\n\n const onmouseover = vl\n .selectSingle()\n .on(\"mouseover\")\n .encodings(\"x\")\n .nearest(true)\n .empty({ created: [] });\n\n const line = vl\n\n .markLine()\n .data(\n covid\n // .filter(d => d.state == state)\n )\n .encode(\n x.timeUnit(timeTruncateSelected), \n vl.y().mean(type),\n vl.color().fieldN('state'),\n vl.opacity()\n .if(`datum.state == \"${state}\"`, vl.opacity().value(1.5)).value(0.1)\n )\n .width(width - 250);\n\n const points = line\n .markPoint({ tooltip: true, size: 1 })\n .select(onmouseover)\n .encode(\n vl\n .color()\n .if(onmouseover, vl.color().value(\"firebrick\"))\n .value(\"steelblue\")\n );\n\n return vl.layer(points, line).render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":884,"value":"- **Insight 1: Unemployment rate of Japan, Canada and US have similar trend since 2000. All three countries' unemployment rate went up in 2009 and 2020. Canada and US had the highest unemployment rates in 2020, while Japan had the highest unemployment rate in 2009.**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":409,"value":"unemployment_chart = {\n const onmouseover = vl\n .selectSingle()\n .on(\"mouseover\")\n .encodings(\"x\")\n .nearest(true)\n .empty({ created: [] });\n\n const line = vl.markLine().data(dataSet.filter(d => d.type=='unemployment')).width(width - 250)\n .encode(\n vl.x().fieldT(\"date\").timeUnit('yearmonth'),\n vl.y().fieldQ(\"value\").title('Unemployment Percentage Rate'),\n vl.color().fieldN(\"country\")\n )\n\n const points = line\n .markPoint({ tooltip: true, size: 1 })\n .select(onmouseover)\n .encode(\n vl\n .color()\n .if(onmouseover, vl.color().value(\"firebrick\"))\n .value(\"steelblue\")\n );\n\n return vl.layer(points, line).render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":513,"value":"// viewof selected_country = Inputs.checkbox(global_countries, { label: \"Choose the country: \", value: ['Japan', 'United States', 'Canada'] })\nviewof selected_country = Inputs.select(\n global_countries, { label: \"Choose another country: \", value: 'Japan' }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":887,"value":"- **Insight 2: Debt to gdp ratio of Japan, US and Canada are very similar too. Debt to gdp ratio both went up when unemployment rate went up (from 2009 to 2010 and from 2019 to 2020).**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":518,"value":"global_chart = {\n const x = vl.x().field(\"date\");\n\n const onmouseover = vl\n .selectSingle()\n .on(\"mouseover\")\n .encodings(\"x\")\n .nearest(true)\n .empty({ created: [] });\n\n const line = vl\n\n .markLine()\n .data(\n debt_to_gdp_ratio_global\n )\n .encode(\n // x.timeUnit('year'), \n vl.x().field(\"date\").title(\"Debt to GDP Ratio\"),\n vl.y().fieldQ('value'),\n vl.color().fieldN('country'),\n vl.opacity()\n .if(\n `datum.country == \"${selected_country}\" || datum.country == 'Japan' || datum.country == 'Canada' || datum.country == 'United States'` \n , vl.opacity().value(1.5)).value(0.1)\n \n )\n .width(width - 250);\n\n const points = line\n .markPoint({ tooltip: true, size: 1 })\n .select(onmouseover)\n .encode(\n vl\n .color()\n .if(onmouseover, vl.color().value(\"firebrick\"))\n .value(\"steelblue\")\n );\n\n return vl.layer(points, line).render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":893,"value":"- **Insight 3: Surprisingly, when unemployment rate peaked around 2019, North America area video sales dropped dramatically **","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":655,"value":"vl.markBar()\n .width(900).height(600)\n .data(videoGameData).encode(\n vl.y().aggregate('sum').title('sales').fieldQ('sales'),\n vl.x().timeUnit('year').field('year').title('Year'),\n vl.color().field('type')\n).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1216,"value":"h1b","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"comboDataSet = {\n const res = []\n dataSet.forEach(d => {\n if (d.country == 'US' && (d.type=='gdp' || d.type =='unemployment')){\n res.push(d)\n }\n })\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1020,"value":"comboDataSet_canada = {\n const res = []\n dataSet.forEach(d => {\n if (d.country == 'Canada' && (d.type=='gdp' || d.type =='unemployment')){\n res.push(d)\n }\n })\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1023,"value":"comboDataSet_japan = {\n const res = []\n dataSet.forEach(d => {\n if (d.country == 'Japan' && (d.type=='gdp' || d.type =='unemployment')){\n res.push(d)\n }\n })\n return res\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":913,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1015,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1026,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1088,"value":"- **Insight 4: For Canada, US and Japan, when GDP dropped in 2009 and 2020, the unemployment rate went up a lot **","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1059,"value":"vl\n .concat(us_unemployment_gdp_chart,canada_unemployment_gdp_chart,japan_unemployment_gdp_chart)\n .columns(1)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1225,"value":"unemployment_h1b_dataset = {\n const us = []\n h1b.forEach(d => {\n us.push({\n date:d.date,\n type:d.type,\n value:d.value,\n country: 'US',\n })\n })\n comboDataSet.forEach(d => us.push(d))\n return us\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1247,"value":"- **Insight 5**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1177,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":306,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":317,"value":"timePartOptions = [\"year\", \"month\", \"date\", \"hours\", \"minutes\", \"seconds\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":308,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":310,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":312,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":289,"value":"## Import libraries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":220,"value":"import {navio} from \"@john-guerra/navio\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":224,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":157,"value":"import {printTable} from '@uwdata/data-utilities'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"

\n## Dataset Attachment","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1028,"value":"japanGDP = await FileAttachment(\"JPNRGDPEXP.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"japanGDP","database":"$undefined"},{"id":477,"value":"Global_debt_gdp = await FileAttachment(\"Untitled spreadsheet - GG_DEBT_GDP.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"Global_debt_gdp","database":"$undefined"},{"id":75,"value":"brazildebt = await FileAttachment(\"BrazilDebt@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"brazildebt","database":"$undefined"},{"id":992,"value":"usgdp = await FileAttachment(\"GDP@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"usgdp","database":"$undefined"},{"id":639,"value":"videoGame = await FileAttachment(\"Video_Games_Sales_as_at_22_Dec_2016.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"videoGame","database":"$undefined"},{"id":203,"value":"usCpi = await FileAttachment(\"us cpi.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"usCpi","database":"$undefined"},{"id":97,"value":"usunemployment = await FileAttachment(\"USunemployment.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"usunemployment","database":"$undefined"},{"id":115,"value":"usdebt = await FileAttachment(\"usdebtpercentage.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"usdebt","database":"$undefined"},{"id":123,"value":"canadagdp = await FileAttachment(\"canadaGDP@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"canadagdp","database":"$undefined"},{"id":124,"value":"canadadebt = await FileAttachment(\"canadadebt.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"canadadebt","database":"$undefined"},{"id":125,"value":"canadaunemployment = await FileAttachment(\"canadaunemployment.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"canadaunemployment","database":"$undefined"},{"id":572,"value":"japanUnemploymentRate = await FileAttachment(\"japan-unemployment-rate.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"japanUnemploymentRate","database":"$undefined"},{"id":594,"value":"united_states_covid19_cases_and_deaths_by_state_ov = await FileAttachment(\"United_States_COVID-19_Cases_and_Deaths_by_State_over_Time_-_ARCHIVED (1).csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"united_states_covid19_cases_and_deaths_by_state_ov","database":"$undefined"},{"id":761,"value":"h1b_2022 = await FileAttachment(\"h1b_datahubexport-2022.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2022","database":"$undefined"},{"id":763,"value":"h1b_2020 = await FileAttachment(\"h1b_datahubexport-2020.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2020","database":"$undefined"},{"id":765,"value":"h1b_2018 = await FileAttachment(\"h1b_datahubexport-2018.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2018","database":"$undefined"},{"id":766,"value":"h1b_2016 = await FileAttachment(\"h1b_datahubexport-2016.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2016","database":"$undefined"},{"id":767,"value":"h1b_2014 = await FileAttachment(\"h1b_datahubexport-2014.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2014","database":"$undefined"},{"id":768,"value":"h1b_2012 = await FileAttachment(\"h1b_datahubexport-2012.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2012","database":"$undefined"},{"id":769,"value":"h1b_2010 = await FileAttachment(\"h1b_datahubexport-2010.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2010","database":"$undefined"},{"id":770,"value":"h1b_2009 = await FileAttachment(\"h1b_datahubexport-2009.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2009","database":"$undefined"},{"id":771,"value":"h1b_2021 = await FileAttachment(\"h1b_datahubexport-2021.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2021","database":"$undefined"},{"id":772,"value":"h1b_2019 = await FileAttachment(\"h1b_datahubexport-2019.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2019","database":"$undefined"},{"id":773,"value":"h1b_2017 = await FileAttachment(\"h1b_datahubexport-2017.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2017","database":"$undefined"},{"id":774,"value":"h1b_2015 = await FileAttachment(\"h1b_datahubexport-2015.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2015","database":"$undefined"},{"id":775,"value":"h1b_2013 = await FileAttachment(\"h1b_datahubexport-2013.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2013","database":"$undefined"},{"id":776,"value":"h1b_2011 = await FileAttachment(\"h1b_datahubexport-2011.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"h1b_2011","database":"$undefined"},{"id":895,"value":"medianincomeus = await FileAttachment(\"MedianIncomeUS.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"medianincomeus","database":"$undefined"},{"id":1034,"value":"japanunemployment1 = await FileAttachment(\"japanUnemployment@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"japanunemployment1","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 30:"$2f:body"