2b:null 2c:["$","title",null,{"children":"CS7295 hw4 Explore the relationship between layoffs and consumer price index(CPI) in different countries | Fengrui Gan (Simon) | Observable"}] 31:T558,## Introduction More than 77,000 workers in the US based tech companies have been laid off in mass job cuts so far in 2023, according to a Crunchbase News tally, and the year is just getting started. Heading into 2023, recession fears and economic uncertainties are still weighing heavily on consumers and policymakers’ minds, and interest rate hikes are expected to continue. Beyond that, the growing number of layoffs may also give certain tech companies some cover to take more severe steps to trim costs now than they may have otherwise done. The layoff situation in Silicon Valley remains in contrast to the economy as a whole. The Consumer Price Index(CPI) represents changes in prices of all goods and services purchased for consumption by urban households. User fees (such as water and sewer service) and sales and excise taxes paid by the consumer are also included. Income taxes and investment items (like stocks, bonds, and life insurance) are not included. The CPI is the most widely used measure of inflation and is sometimes viewed as an indicator of the effectiveness of government economic policy. In this homework, I want to explore the correlation between tech layoffs and the CPI. Will CPI invoke tech massive layoffs? Can tech layoffs help to lower the CPI? We can find it out with the visualization of layoff and CPI. 32:T9ca,- **Users**: Anyone interested in knowing the current layoff situation and how layoff is related to the CPI. - **Made-up insights** - Insight 1: *In all countries, higher CPI will cause at 10% increase in layoff within a month, with HR industry get hit worst.* - T1: **Compare** the **percentage change** in each country's layoff of all industries and CPI. - T2: **Look up** the **countries** that CPI can cause more than 10% increase in layoff. - Consideration: For me to test this, I need to have at least 2 years' data of Layoff and CPI. I would need each country's(category) layoff (quant) from all the companies(category), and CPI (quant) in each month(date - quant). I then will need to derive the percentage change in layoff and CPI. In terms of the idiom, I would need to display the percentage change in layoff and CPI of each country. A grouped stacked barchart with labels will work. - Insight 2: *In Germany and North America, CPI has more effect over layoff than China and South America.* - T1: **Compare** (**the percentage change in layoff)/(percentage change in CPI)** between Germany, North America and China, South America. - T2: **Summarize** the **effect of CPI over layoff** in different countries. - Consideration: For me to test this, I need to have at least 2 years' data of Layoff and CPI. I would need each country's(category) layoff (quant) from all the companies(category), and CPI (quant) in each month(date - quant). I then will need to derive the percentage change in layoff and CPI. In terms of the idiom, I would need to display the percentage change in layoff and CPI of each country, then group together Germany and North America in one chart, and China and South America in another chart. A grouped barchart with labels will work. - Insight 3: *In all countries, layoff can lower the CPI by at least 3% within a month.* - T1: **Compare** the **percentage change in layoff and CPI percentage change** in the next month. - T2: **Identify** the **country** where layoff can't lower the CPI. - Consideration: For me to test this, I need to have at least 2 years' data of Layoff and CPI. I would need each country's(category) layoff (quant) from all the companies(category), and CPI (quant) in each month(date - quant). I then will need to derive the percentage change in layoff and CPI. In terms of the idiom, I would need to display the percentage change in layoff and CPI of each country. A grouped barchart with labels will work.33:T502,- **Main Task**: Compare the percentage change in each country's layoff of all industries and CPI changes. - **Secondary Task**: Look up the countries where CPI can cause more than 10% increase in layoff. - **Target Audience**: Anyone interested in knowing the current layoff situation and how layoff is related to the CPI. - **Attributes**

Year-month: Sequential ordered ordinal
Country: Categorical
Industry: Categorical
Layoff: Ordered Quantitative sequential
CPI: ordered quantitative sequential

- **Derive Attributes**

monthly layoff percentage change(each country's total layoff number's changes compared to previous month): Ordered Quantitative sequential
monthly CPI percentage change(each country's CPI changes compared to previous month): Ordered Quantitative sequential

- **Marks** - Line - **Channels** - Horizontal Position: Year-Month date - Vertical Position: Layoff and CPI - Color hue: industry, Country - **Expressiveness and Effectiveness**: With the marks and channels I choose, I can display all the information that we care about(Expressiveness). After seeing the graphs, we can figure out if our insight is correct.(Effectivenss)34:T8bb,// Get each country's CPI cpiData = { let cpis = [] uscpi.forEach(data => { const date = new Date(data.DATE) const yearMonth = new Date(date.getFullYear(), date.getMonth()) if (date.getFullYear() > 2019){ cpis.push({country:'United States', date:yearMonth, cpi: data.USACPIALLMINMEI}) } }) chinacpi.forEach(data => { const date = new Date(data.DATE) const yearMonth = new Date(date.getFullYear(), date.getMonth()) if (date.getFullYear() > 2019){ cpis.push({country:'China', date: yearMonth, cpi: data.CCRETT01CNM661N}) } }) germany.forEach(data => { const date = new Date(data.DATE) const yearMonth = new Date(date.getFullYear(), date.getMonth()) if (date.getFullYear() > 2019){ cpis.push({country:'Germany', date: yearMonth, cpi: data.CCRETT01DEM661N}) } }) brazil.forEach(data => { const date = new Date(data.DATE) const yearMonth = new Date(date.getFullYear(), date.getMonth()) if (date.getFullYear() > 2019){ cpis.push({country:'Brazil', date: yearMonth, cpi: data.CCRETT01BRM661N}) } }) canada.forEach(data => { const date = new Date(data.DATE) const yearMonth = new Date(date.getFullYear(), date.getMonth()) if (date.getFullYear() > 2019){ cpis.push({country:'Canada', date: yearMonth, cpi: data.CCRETT01CAM661N}) } }) australia.forEach(data => { const date = new Date(data.DATE) const yearMonth = new Date(date.getFullYear(), date.getMonth()) if (date.getFullYear() > 2019){ cpis.push({country:'Australia', date: yearMonth, cpi: data.CCRETT01AUM661N}) } }) const countries = ['United States', 'China', 'Germany', 'Brazil', 'Australia', 'Canada'] let current_country = null let prev_cpi = null let cpi_percentage_changes = [] for (let data of cpis){ if (data.country === current_country){ cpi_percentage_changes.push({ type: 'cpi', country: current_country, date: data.date, percentage_change: (data.cpi-prev_cpi)/prev_cpi, }) prev_cpi = data.cpi }else{ current_country = data.country prev_cpi = data.cpi } } return {cpi: cpis, cpi_percentage_changes:cpi_percentage_changes} }35:T7d2,// Visualize Layoff : stacked barchart { const width = 450 const chinaLayoff = vl .markBar() .data(layoff.countryLayoff["China"]) .encode( vl.x().fieldT("date").timeUnit("yearmonth").title("Date"), vl.y().count().title("China Layoff Number"), vl.color().fieldN("industry") ) .width(width) .height(300); const usLayoff = vl .markBar() .data(layoff.countryLayoff["United States"]) .encode( vl.x().fieldT("date").timeUnit("yearmonth").title("Date"), vl.y().count().title("US Layoff Number"), vl.color().fieldN("industry") ) .width(width) .height(300); const germanyLayoff = vl .markBar() .data(layoff.countryLayoff["Germany"]) .encode( vl.x().fieldT("date").timeUnit("yearmonth").title("Date"), vl.y().count().title("Germany Layoff Number"), vl.color().fieldN("industry") ) .width(width) .height(300); const brazilLayoff = vl .markBar() .data(layoff.countryLayoff["Brazil"]) .encode( vl.x().fieldT("date").timeUnit("yearmonth").title("Date"), vl.y().count().title("Brazil Layoff Number"), vl.color().fieldN("industry") ) .width(width) .height(300); const canadaLayoff = vl .markBar() .data(layoff.countryLayoff["Canada"]) .encode( vl.x().fieldT("date").timeUnit("yearmonth").title("Date"), vl.y().count().title("Canada Layoff Number"), vl.color().fieldN("industry") ) .width(width) .height(300); const australiaLayoff = vl .markBar() .data(layoff.countryLayoff["Australia"]) .encode( vl.x().fieldT("date").timeUnit("yearmonth").title("Date"), vl.y().count().title("Australia Layoff Number"), vl.color().fieldN("industry") ) .width(width) .height(300); return vl .vconcat( vl.hconcat(brazilLayoff, canadaLayoff), vl.hconcat(australiaLayoff, chinaLayoff), vl.hconcat(usLayoff, germanyLayoff) ) .render(); }36:Tb8c,// Visualize CPI and CPI changes using line { const hover = vl.selectPoint('hover') .encodings('x') // limit selection to x-axis value .on('mouseover') // select on mouseover events .toggle(false) // disable toggle on shift-hover .nearest(true); // select data point nearest the cursor const isHovered = hover.empty(false); const line_chart_cpi = vl.markLine() .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('cpi').title("CPI").type('quantitative'), //scale({'domain':[70,105]}) vl.color().fieldN('country'), ) const base = line_chart_cpi.transform(vl.filter(isHovered)); const label = {align: 'left', dx: -70, dy: -5}; const white = {stroke: 'black', strokeWidth: 0.3}; const cpiVisual = vl.data(cpiData.cpi).layer( line_chart_cpi, vl.markRule({color: '#aaa'}) .transform(vl.filter(isHovered)) .encode(vl.x().fieldT('date')), line_chart_cpi.markCircle() .params(hover) // use as anchor points for selection .encode(vl.opacity().if(isHovered, vl.value(1)).value(0)), base.markText(label, white).encode(vl.text().fieldQ('cpi')), line_chart_cpi.markCircle() ) .width(600) .height(400) const layoffline = vl.markLine() .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('layoff').title("Layoff Total x 1,000,000"),//.scale({'domain': [0, 50000000]}), vl.color().fieldN('country'), ) const layoffTotal = vl.data(layoff.total).layer( layoffline, vl.markRule({color: '#aaa'}) .transform(vl.filter(isHovered)) .encode(vl.x().fieldT('date')), layoffline.markCircle() .params(hover) // use as anchor points for selection .encode(vl.opacity().if(isHovered, vl.value(1)).value(0)), layoffline.transform(vl.filter(isHovered)).markText(label, white).encode(vl.text().fieldQ('layoff')), layoffline.markCircle() ) .width(600) .height(400) const line_chart_percentage = vl.markLine() .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date").type('temporal'), vl.y().fieldQ('percentage_change').title("CPI Change Percentage").type('quantitative'), vl.color().fieldN('country'), ) const base_1 = line_chart_percentage.transform(vl.filter(isHovered)); const cpiChangeVisual = vl.data(cpiData.cpi_percentage_changes).layer( line_chart_percentage, vl.markRule({color: '#aaa'}) .transform(vl.filter(isHovered)) .encode(vl.x().fieldT('date')), line_chart_percentage.markCircle() .params(hover) // use as anchor points for selection .encode(vl.opacity().if(isHovered, vl.value(1)).value(0)), base_1.markText(label, white).encode(vl.text().fieldQ('percentage_change')), line_chart_percentage.markCircle() ) .width(600) .height(400) return vl.vconcat(vl.hconcat(cpiVisual, layoffTotal), cpiChangeVisual).render(); }37:T698,// Now we put CPI percentage changes and layoff percentage changes together and see their trend is similar // Combine data first percentageData = { let china_cpi_percentage = cpiData.cpi_percentage_changes.filter(data => data.country == 'China') let china_layoff_percentage = layoff.layoffPercentageChange.China let us_cpi_percentage = cpiData.cpi_percentage_changes.filter(data => data.country == 'United States') let us_layoff_percentage = layoff.layoffPercentageChange['United States'] let brazil_cpi_percentage = cpiData.cpi_percentage_changes.filter(data => data.country == 'Brazil') let brazil_layoff_percentage = layoff.layoffPercentageChange.Brazil let germany_cpi_percentage = cpiData.cpi_percentage_changes.filter(data => data.country == 'Germany') let germany_layoff_percentage = layoff.layoffPercentageChange.Germany let canada_cpi_percentage = cpiData.cpi_percentage_changes.filter(data => data.country == 'Canada') let canada_layoff_percentage = layoff.layoffPercentageChange.Canada let australia_cpi_percentage = cpiData.cpi_percentage_changes.filter(data => data.country == 'Australia') let australia_layoff_percentage = layoff.layoffPercentageChange.Australia return { 'China': {cpi: china_cpi_percentage, layoff: china_layoff_percentage}, 'US': {cpi: us_cpi_percentage, layoff: us_layoff_percentage}, 'Brazil': {cpi: brazil_cpi_percentage, layoff: brazil_layoff_percentage}, 'Germany': {cpi: germany_cpi_percentage, layoff: germany_layoff_percentage}, 'Canada': {cpi: canada_cpi_percentage, layoff: canada_layoff_percentage}, 'Australia': {cpi: australia_cpi_percentage, layoff: australia_layoff_percentage}, } }38:T1581,{ const width = 400 const chinaLayoff = vl.markBar() .data(layoff.countryLayoff['China']) .encode( vl.x().fieldT('date').timeUnit("yearmonth").title('Date'), vl.y().count().title("China Layoff Number").axis({orient: 'left'}), vl.color().fieldN('industry') ) .width(width) .height(300) const chinaLayoffPercentage = vl.markLine().data(percentageData.China.layoff) .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('percentage').title("CPI Change Percentage").axis({orient: 'right'}), ) const china = vl.markLine().data(percentageData.China.cpi) .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('percentage_change').title("CPI Change Percentage").axis({orient: 'right'}), ) const china_layer = vl.layer(chinaLayoff, china).resolve( {scale : {y: 'independent'}} ) const usLayoff = vl.markBar() .data(layoff.countryLayoff['United States']) .encode( vl.x().fieldT('date').timeUnit("yearmonth").title('Date'), vl.y().count().title("US Layoff Number").axis({orient: 'left'}), vl.color().fieldN('industry') ) .width(width) .height(300) const us = vl.markLine() .data(percentageData.US.cpi) .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('percentage_change').title("CPI Change Percentage").axis({orient: 'right'}), ) const us_layer = vl.layer(usLayoff, us).resolve({scale : {y: 'independent'}}) const germanyLayoff = vl.markBar() .data(layoff.countryLayoff['Germany']) .encode( vl.x().fieldT('date').timeUnit("yearmonth").title('Date'), vl.y().count().title("Germany Layoff Number").axis({orient: 'left'}), vl.color().fieldN('industry') ) .width(width) .height(300) const germany = vl.markLine() .data(percentageData.Germany.cpi) .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('percentage_change').title("CPI Change Percentage").axis({orient: 'right'}), ) const germany_layer = vl.layer(germanyLayoff, germany).resolve({scale : {y: 'independent'}}) const canadaLayoff = vl.markBar() .data(layoff.countryLayoff['Canada']) .encode( vl.x().fieldT('date').timeUnit("yearmonth").title('Date'), vl.y().count().title("Canada Layoff Number").axis({orient: 'left'}), vl.color().fieldN('industry') ) .width(width) .height(300) const canada = vl.markLine() .data(percentageData.Canada.cpi) .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('percentage_change').title("CPI Change Percentage").axis({orient: 'right'}), ) const canada_layer = vl.layer(canadaLayoff, canada).resolve({scale : {y: 'independent'}}) const brazilLayoff = vl.markBar() .data(layoff.countryLayoff['Brazil']) .encode( vl.x().fieldT('date').timeUnit("yearmonth").title('Date'), vl.y().count().title("Brazil Layoff Number").axis({orient: 'left'}), vl.color().fieldN('industry') ) .width(width) .height(300) const brazil = vl.markLine() .data(percentageData.Brazil.cpi) .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('percentage_change').title("CPI Change Percentage").axis({orient: 'right'}), ) const brazil_layer = vl.layer(brazilLayoff, brazil).resolve({scale : {y: 'independent'}}) const australiaLayoff = vl.markBar() .data(layoff.countryLayoff['Australia']) .encode( vl.x().fieldT('date').timeUnit("yearmonth").title('Date'), vl.y().count().title("Australia Layoff Number").axis({orient: 'left'}), vl.color().fieldN('industry') ) .width(width) .height(300) const australia = vl.markLine() .data(percentageData.Australia.cpi) .encode( vl.x().fieldT('date').timeUnit('yearmonth').title("Date"), vl.y().fieldQ('percentage_change').title("CPI Change Percentage").axis({orient: 'right'}), ) const australia_layer = vl.layer(australiaLayoff, australia).resolve({scale : {y: 'independent'}}) const first_row = vl.hconcat(us_layer, canada_layer) const second_row = vl.hconcat(brazil_layer, germany_layer) const third_row = vl.hconcat(australia_layer, china_layer) return vl.vconcat(first_row, second_row, third_row).render() }39:T552,data = { const countries = [ 'US', 'Germany', 'Canada', 'Brazil','Australia', 'China'] let combinedData = { 'US':[], 'Germany': [], 'Canada': [], 'Brazil': [], 'Australia': [], 'China':[] } let charts = { 'US':null, 'Germany': null, 'Canada': null, 'Brazil': null, 'Australia': null, 'China':null } for (let country of countries){ const layoffPercentage = vl.markLine().data(percentageData[country].layoff).encode( vl.x().fieldT('date').timeUnit("yearmonth"), vl.y().fieldQ('percentage_change').title(country + " Layoff Changes").axis({orient: 'left'}), vl.color({value: 'red'}) ).width(300).height(300) const cpiPercentage = vl.markLine().data(percentageData[country].cpi).encode( vl.x().fieldT('date').timeUnit("yearmonth").title(country+' Date'), vl.y().fieldQ('percentage_change').title(country + " CPI Changes").axis({orient: 'right'})//.scale({domain: [-0.1, 0.1]}), ).width(300).height(300) charts[country] = vl.layer(layoffPercentage, cpiPercentage) } const first_row = vl.hconcat(charts['US'], charts['Canada'], charts['Brazil']) const second_row = vl.hconcat(charts['Germany'], charts['Australia'], charts['China']) return { visual: vl.vconcat(first_row, second_row).resolve({scale: {y: 'independent'}}), combinedData} }3a:T65e,### Conclusion #### recap I chose : - Insight: In all countries, higher CPI will cause at 10% increase in layoff within a month, with HR industry get hit worst. - T1: Compare the percentage change in each country's layoff of all industries and CPI. - T2: Look up the countries that CPI can cause more than 10% increase in layoff. - Consideration: For me to test this, I need to have at least 2 years' data of Layoff and CPI. I would need each country's(category) layoff (quant) from all the companies(category), and CPI (quant) in each month(date - quant). I then will need to derive the percentage change in layoff and CPI. In terms of the idiom, I would need to display the percentage change in layoff and CPI of each country. A grouped stacked barchart with labels will work. #### T1 I compared CPI, CPI change percentage with layoff total number in Visualization 2. we can see that US has the highest CPI and highest layoff number. Brazil has the least stable CPI. #### T2 In Visualization 3, I put together the CPI change percentage and total layoff number. We can't reach the conclusion that CPI can cause the layoff directly. But we can see that layoff spikes are around CPI change percentage spikes. #### Insight After visualizing the layoff and CPI dataset, my made-up insight seems to be wrong. I was thinking higher CPI means higher inflation which might be the direct reason to invoke layoff across that world. I can't say CPI can directly cause the layoff by studying those visualization. But we could say that the layoff is more likely to happen when CPI spikes. This might be a helpful indicator in the future.3b:T809,// get each country's layoff number and layoff percentage change each month layoff = { let countryLayoff = { 'United States': [], 'China': [], 'Germany': [], 'Brazil': [], 'Australia': [], 'Canada': [], } let totalLayoff = { 'United States': {}, 'China': {}, 'Germany': {}, 'Brazil': {}, 'Australia': {}, 'Canada': {}, } let total = [] let layoffPercentageChange = { 'United States': [], 'China': [], 'Germany': [], 'Brazil': [], 'Australia': [], 'Canada': [], } layoffdata.forEach(data => { const countryName = data.country const industry = data.industry const numberLaidOff = data.numberLaidOff const dateDate = new Date(data.date) const yearMonth = new Date(dateDate.getFullYear(), dateDate.getMonth()) if (!numberLaidOff || countryLayoff[countryName] === undefined || dateDate.getFullYear() <= 2019) return for (let i=0; i","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":101,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":22,"value":"## Data Overview","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":43,"value":"### Layoff data\nI found the [Layoffs.fyi](https://layoffs.fyi/) website contains very almost all the layoff data since 2020. This is great dataset because the economy has been changing a lot since Covid hit. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":46,"value":"### Consumer Price Index (CPI) Data\n\nSimon (Fengrui Gan) recommended [Fred website](https://fred.stlouisfed.org/series/) to me because this website has a lot of CPI dataset. After the checking the data in Layoff.fyi, I downloaded China CPI, Germany CPI, Canada CPI, US CPI, Brazil CPI and Australia CPI dataset. Those country can roughly represents Asia, Europe, North America, South America and Australia situation. Here is an example of Germany CPI.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"screenshot20230219At124020Pm = FileAttachment(\"Screenshot 2023-02-19 at 12.40.20 PM.png\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"I attached my 7 datasets at the end of this Notebook. It is pretty long and I don't want it to be in the middle of the homework :) \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":106,"value":"### Data Abstraction (What)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":108,"value":"- Dataset Type\n - Table; Spatial Temporal\n- Dataset Attributes\n - **Year-month**: Sequential ordered ordinal\n - **Country**: Categorical\n - **Company**: Categorical\n - **Industry**: Categorical\n - **Layoff**: Ordered Quantitative sequential\n - **CPI**: ordered quantitative sequential\n- Derive Attributes:\n - **Total Monthly layoff**(each country's total layoff number in one month): Ordered Quantitative sequential\n - **monthly layoff percentage change**(each country's total layoff number's changes compared to previous month): Ordered Quantitative sequential\n - **monthly CPI percentage change**(each country's CPI changes compared to previous month): Ordered Quantitative sequential\n- Datset Availability:\n - Static","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":125,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":127,"value":"### Users, Tasks and Made-up insights (Why)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":131,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":177,"value":"- **My Choice**: I am more interested in the **insight 1: In all countries, higher CPI will cause at 10% increase in layoff within a month, with HR industry get hit worst.**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":185,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":188,"value":"### How","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":190,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":200,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":202,"value":"### Data Process and Visualization with vega-lite api","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":231,"value":"import {vl} from '@vega/vega-lite-api-v5'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":390,"value":"import {printTable} from '@uwdata/data-utilities'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":804,"value":"#### Data process\n- Get each country's CPI data\n- Calculate CPI change percentage each month (derived attribute)\n- Get each country's layoff data\n- Calculate layoff change percentage each month(derived attribute)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":397,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":838,"value":"layoff","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":846,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":852,"value":"#### Visualization 1: Visualize each country's layoff in all industries\n- I use stacked bar chart to visualize the layoff.\n- We can tell that layoff had happened in all kinds of industries.\n- Layoff in Finance, Retail and Recruiting seem to be the worst.\n- Except China, other 6 countries have similar layoff trend. Layoff spiked around April 2020, 2022 summer and 2023 spring.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":546,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":859,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":850,"value":"#### Visualization 2: CPI, CPI change percentage and layoff total\n- use line chart to visualize the CPI, CPI, CPI change percentage and layoff total\n- I added tooltip to help with reading the graph.\n- In CPI charts, we can see that US has highest CPI, and Brazil has the least stable CPI.\n- In layoff total chart, we can see that US has laid off way more people than other countries.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":425,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":848,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":861,"value":"#### Visualization 3: Layer each country's layoff and CPI change percentage\n- By putting layoff bar chart and CPI change line chart together, we can see that layoff spikes seem to be around CPI spikes.\n- But we can't come to a conclusion that layoff is caused by CPI from this visualization.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":580,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":612,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":863,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":869,"value":"#### Visualization 4: Visualize layoff change percentage and CPI change percentage\n- From those charts, I would say layoff change percentage doesn't have much to do with the CPI change percentage.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":661,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":722,"value":"data.visual.render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":871,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":873,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":891,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":96,"value":"

Dataset Attachment

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":654,"value":"uscpi = await FileAttachment(\"USACPIALLMINMEI.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"uscpi","database":"$undefined"},{"id":92,"value":"layoffdata = await FileAttachment(\"layoffdata@2.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"layoffdata","database":"$undefined"},{"id":85,"value":"brazil = await FileAttachment(\"Brazil@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"brazil","database":"$undefined"},{"id":86,"value":"canada = await FileAttachment(\"canada@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"canada","database":"$undefined"},{"id":87,"value":"chinacpi = await FileAttachment(\"ChinaCPI@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"chinacpi","database":"$undefined"},{"id":88,"value":"germany = await FileAttachment(\"Germany@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"germany","database":"$undefined"},{"id":84,"value":"australia = await FileAttachment(\"Australia@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"australia","database":"$undefined"},{"id":211,"value":"$3b","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3c","stdlib":"1","titling":"auto"}} 30:"$2f:body"