31:T4b2,{ const myDiv = html`

`; const dates = Overall_Employment_Level.map(d => d.Label); const yval = Overall_Employment_Level.map(d => d.Value* 1000); const trace1 = { type: "scatter", mode: "lines", name: 'All employees', x: dates, y: yval, line: { color: '#17BECF' } }; const data = [trace1]; const layout = { autosize: false, width: 900, height: 550, showlegend: true, annotations: [ { x: dates[13], y: yval[13]+4500000, xref: 'x', yref: 'y', text: "Jobs lost", showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval[13]+3000000, xref: 'x', yref: 'y', text: "since Jan. '20:", showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval[13]+1000000, xref: 'x', yref: 'y', text: yval[13] - yval[0], showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, ], yaxis: { title: { text: 'Employment Level'}, }, }; Plotly.newPlot(myDiv, data, layout); return myDiv; }32:T4e5,{ const myDiv = html`

`; const dates = Overall_Employment_Level.map(d => d.Label); const yval_1 = Unemployment_Rate_Black.map(d => d.Value); const yval_2 = Unemployment_Rate_Asian.map(d => d.Value); const yval_3 = Unemployment_Rate_Hispanic.map(d => d.Value); const yval_4 = Unemployment_Rate_White.map(d => d.Value); const trace1 = { type: "scatter", mode: "lines", name: 'Black', x: dates, y: yval_1, line: { color: '#17BECF' } }; const trace2 = { type: "scatter", mode: "lines", name: 'Asian', x: dates, y: yval_2, line: { color: 'green' } }; const trace3 = { type: "scatter", mode: "lines", name: 'Latino', x: dates, y: yval_3, line: { color: 'orange' } }; const trace4 = { type: "scatter", mode: "lines", name: 'White', x: dates, y: yval_4, line: { color: 'blue' } }; const data = [trace1, trace2, trace3, trace4]; const layout = { autosize: false, width: 850, height: 650, showlegend: true, yaxis: { title: { text: 'Unemployment Rate (percents)'}, }, }; Plotly.newPlot(myDiv, data, layout); return myDiv; }33:T667,{ const myDiv = html`

`; const dates = Overall_Employment_Level.map(d => d.Label); const yval_1 = Employment_Level_Men.map(d => d.Value * 1000); const yval_2 = Employment_Level_Women.map(d => d.Value * 1000); const trace1 = { type: "scatter", mode: "lines", name: 'Men', x: dates, y: yval_1, line: { color: 'navy' } }; const trace2 = { type: "scatter", mode: "lines", name: 'Women', x: dates, y: yval_2, line: { color: 'red' } }; const data = [trace1, trace2]; const layout = { autosize: false, width: 850, height: 600, showlegend: true, yaxis: { title: { text: 'Employment Level'}, }, annotations: [ { x: dates[13], y: yval_1[13]+4500000, xref: 'x', yref: 'y', text: "Jobs lost", showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_1[13]+3000000, xref: 'x', yref: 'y', text: "since Feb. '20:", showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_1[13]+1000000, xref: 'x', yref: 'y', text: yval_1[13] - yval_1[1], showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_2[13]+1000000, xref: 'x', yref: 'y', text: yval_2[13] - yval_2[1], showarrow: false, arrowhead: 7, ax: 0, ay: -40 } ] }; Plotly.newPlot(myDiv, data, layout); return myDiv; }34:Tb80,{ const myDiv = html`

`; const dates = Overall_Employment_Level.map(d => d.Label); const yval_1 = Employment_16to24.map(d => d.Value*1000); const yval_2 = Employment_25to34.map(d => d.Value*1000); const yval_3 = Employment_35to44.map(d => d.Value*1000); const yval_4 = Employment_45to54.map(d => d.Value*1000); const yval_5 = Employment_55Up.map(d => d.Value*1000); const trace1 = { type: "scatter", mode: "lines", name: '16-24', x: dates, y: yval_1, line: { color: '#17BECF' } }; const trace2 = { type: "scatter", mode: "lines", name: '25-34', x: dates, y: yval_2, line: { color: 'green' } }; const trace3 = { type: "scatter", mode: "lines", name: '35-44', x: dates, y: yval_3, line: { color: 'orange' } }; const trace4 = { type: "scatter", mode: "lines", name: '45-54', x: dates, y: yval_4, line: { color: 'blue' } }; const trace5 = { type: "scatter", mode: "lines", name: '50 & Up', x: dates, y: yval_5, line: { color: 'red' } }; const data = [trace1, trace2, trace3, trace4, trace5]; const layout = { autosize: false, width: 850, height: 650, showlegend: true, yaxis: { title: { text: 'Employment Level by Age Groups'}, }, annotations: [ { x: dates[13], y: yval_5[13]+4500000, xref: 'x', yref: 'y', text: "Jobs lost", showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_5[13]+3000000, xref: 'x', yref: 'y', text: "since Jan. '20:", showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_1[13]+500000, xref: 'x', yref: 'y', text: yval_1[13] - yval_1[0], showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_2[13]+500000, xref: 'x', yref: 'y', text: yval_2[13] - yval_2[0], showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_3[13]+500000, xref: 'x', yref: 'y', text: yval_3[13] - yval_3[0], showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_4[13]+500000, xref: 'x', yref: 'y', text: yval_4[13] - yval_4[0], showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, { x: dates[13], y: yval_5[13]+500000, xref: 'x', yref: 'y', text: yval_5[13] - yval_5[0], showarrow: false, arrowhead: 7, ax: 0, ay: -40 }, ] }; Plotly.newPlot(myDiv, data, layout); return myDiv; }35:T6ca,md`### Line Plot 1: Overall Employment Level Millions of jobs were cut between January and April of 2020 due to the pandemic. From April 2020 to February 2021, jobs started increasing again but were still around 8 million less from the original point. Chart numbers vary slightly from CNN's posted chart, but the overall trend remains the same. ### Line Plot 2: Unemployment Loss by Race/Ethnicity This chart had the closest results to CNN's chart. Black and Latino workers suffered higher unemployment rates throughout the pandemic than Asian and white workers. This shows inequality in the workforce which exists even before the pandemic started. ### Line Plot 3: Employment Level by Gender This chart showed the most significant differences in employment levels from CNN's posted chart. In this case, women had way less jobs to begin with and only lost a small amount more than men. This differs from CNN's claim that women started out with more jobs and lost a greater amount of jobs than they did. It is hard to tell where the discrepancy lies, but it can be related to CNN grabbing the wrong data, or using data for a specific age group of men vs. women without specifying that. ### Line Plot 4: Employment Loss by Age Group This was an additional chart added to evaluate the effects of age on employment loss. According to the charts, each age group showed similar trends in employment levels, although the age group of 50&up had the greatest decrese in employment level. This could be due to other factors such as retirement however. Below that was the age group 25-34 which was surprising. 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Black.csv","type":"text/csv","lastModified":1619470644071.389,"size":518}] 2f:{"title":"Final Project: Job Market trend during Covid-19","cells":[{"id":0,"value":"md`# Final Project: Job Market trend during Covid-19`","pinned":false,"hidden":false,"mode":"ojs"},{"id":288,"value":"md`### The main goal of this project is to identify trends in the job market, related to the timeline of the COVID-19 pandemic, based on charts produced by CNN Business.\nhttps://www.cnn.com/2021/03/11/economy/pandemic-job-market/index.html`","pinned":false,"hidden":false,"mode":"ojs"},{"id":549,"value":"md`## Progress III (Julia Arroyo):\nAfter checking the data source for the line plot: Employment Level of Men and Women, it was verified that the data pulled for women and men was correct based on the specifications. The conclusion is that the original author may have selected employment levels for specific age groups of women and men that were not specified, but for our analysis, I decided to keep the data I had to give a better overall picture of workers. I also created a new line plot to evaluate trends in employment loss according to age groups. The conclusion from that plot is that each group seemed to be affected almost exactly the same, as each line follows the same pattern. I also changed the scale of each plot to be in millions to make the plots easier to follow for readers. Under the line plots, I created an analyis section where I spoke about how each plot relates to the CNN plots and also other trends and possible reasons for those trends noticed.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":553,"value":"md`## Progress III (Jessica Schielka):\nFor this Progress report, I double checked to make sure the bar charts are accurate. The first bar chart is verfied, but the EPI data for the second chart still can't be found or sorted correctly. At this rate, for the final demo it might need to be subbed in for other insights we can gather from all of the data we pulled. On top of that, I think more details can be added to the bar charts such as labeling, numbering, colors, or trends. I also added a bar chart analysis section with descriptions of each chart (existing and future idea). `","pinned":false,"hidden":false,"mode":"ojs"},{"id":476,"value":"md`## Progress II (Julia Arroyo):\nUsing the data imported last time, I created the line plots which correspond to the ones produced by CNN Business. These plots show trends in employment for all employees, women vs. men, and between different races. I used Plotly to create the line plots and added annotations when needed. At first glance, the last line plot shows different numbers for employees which will be investigated this upcoming week while checking accuracy. Scales are slightly different which may or may not need to be adjusted as well. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":504,"value":"md`## Progress II (Jessica Schielka):\nFor this Progress report, my job was to recreate the bar charts in the CNN Business article. The first bar chart is simply showing the declining employment of women and men in each business sector...leisure and hospitality being the hardest hit during Covid. The second bar chart is proving hard to replicate as the data source isn't very exact. This chart will show the declining employment of certain income levels from 2019 to 2020. According to the EPI website below, the data for wage groups isn't given in 4 income sectors. Either the author of the article split up the data himself or we collected the wrong data set from EPI. We will need to see if we can find the data set, or we can come up with other insights with the given data. The next step after that will be to make sure our charts are accurate.\nhttps://www.epi.org/data/#?subject=wagegroup\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":254,"value":"md`## Progress I:\nUsing the Bureau of Labor Statistics' Data Finder and the Economic Policy Institute Data Library, we were able to pull all of the data needed for the analysis from the databases. The data needed ranged from an overall monthly employment level to unemployment rates by race, employment levels by gender, employment levels by gender and industry, and annual wages. Using each site's search tool, we had to specify which exact data we needed in order for it to produce a CSV file of that data. After downloading all of the CSV's provided, we uploaded them as d3 objects to the Observable Notebook.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":243,"value":"md`## Imports:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"d3= require (\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":296,"value":"Plotly = require(\"plotly.js-dist\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":56,"value":"md`### Overall Employment Level (Seasonally Adjusted):`","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"Overall_Employment_Level = d3.csvParse(await FileAttachment(\"OverallEmploymentLevel.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":71,"value":"md`### Unemployment Rates by Race (Seasonally Adjusted):`","pinned":false,"hidden":false,"mode":"ojs"},{"id":75,"value":"Unemployment_Rate_Black = d3.csvParse(await FileAttachment(\"Unemployment Rate - Black.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":79,"value":"Unemployment_Rate_Hispanic = d3.csvParse(await FileAttachment(\"Unemployment Rate - Hispanic.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":83,"value":"Unemployment_Rate_Asian = d3.csvParse(await FileAttachment(\"Unemployment Rate - Asian.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":92,"value":"Unemployment_Rate_White = d3.csvParse(await FileAttachment(\"Unemployment Rate - White.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"md`### Employment Levels by Gender (Seasonally Adjusted):`","pinned":false,"hidden":false,"mode":"ojs"},{"id":103,"value":"Employment_Level_Men = d3.csvParse(await FileAttachment(\"Employment Levels - Men.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":105,"value":"Employment_Level_Women = d3.csvParse(await FileAttachment(\"Employment Level - Women.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":113,"value":"md`### Employment Levels by Industry and Gender (Seasonally Adjusted):`","pinned":false,"hidden":false,"mode":"ojs"},{"id":118,"value":"Employment_Women_LeisureandHospitality = d3.csvParse(await FileAttachment(\"Women Employees- Leisure and Hospitality.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":120,"value":"Employment_Men_LeisureandHospitality = d3.csvParse(await FileAttachment(\"Men Employees- Leisure and Hospitality.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":133,"value":"Employment_Women_EducationandHealthServices = d3.csvParse(await FileAttachment(\"Women Employees - Education and Health Services.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"Employment_Men_EducationandHealthServices = d3.csvParse(await FileAttachment(\"Men Employees - Education and Health Services.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":141,"value":"Employment_Women_Retail = d3.csvParse(await FileAttachment(\"Women Employment Level - Retail.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":146,"value":"Employment_Men_Retail = d3.csvParse(await FileAttachment(\"Men Employment Level- Retail.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":152,"value":"Employment_Women_Business = d3.csvParse(await FileAttachment(\"Women Employed - Business.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":156,"value":"Employment_Men_Business = d3.csvParse(await FileAttachment(\"Men Employed - Business.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":162,"value":"Employment_Men_Manufacturing = d3.csvParse(await FileAttachment(\"Men Employees - Manufacturing.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":166,"value":"Employment_Women_Manufacturing = d3.csvParse(await FileAttachment(\"Women Employees - Manufacturing.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":170,"value":"Employment_Men_TransportationAndWarehousing = d3.csvParse(await FileAttachment(\"Men Employed - Trasnportation and Warehousing.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":175,"value":"Employment_Women_TransportationAndWarehousing = d3.csvParse(await FileAttachment(\"Women Employed - Transportation and Warehousing.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":181,"value":"Employment_Men_Information = d3.csvParse(await FileAttachment(\"Men Employed - Information.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":186,"value":"Employment_Women_Information = d3.csvParse(await FileAttachment(\"Women Employed - Information.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"Employment_Men_WholesaleTrade = d3.csvParse(await FileAttachment(\"Men Employed - Wholesale Trade.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":194,"value":"Employment_Women_WholesaleTrade = d3.csvParse(await FileAttachment(\"Women Employed - Wholesale Trade.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"Employment_Men_Financial = d3.csvParse(await FileAttachment(\"Men Employeed - Financial.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":205,"value":"Employment_Women_Financial = d3.csvParse(await FileAttachment(\"Women Employed - Financial.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":211,"value":"Employment_Women_Construction = d3.csvParse(await FileAttachment(\"Women Employeed - Construction.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":215,"value":"Employment_Men_Construction = d3.csvParse(await FileAttachment(\"Men Employeed - Construction.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":221,"value":"Employment_Men_Mining = d3.csvParse(await FileAttachment(\"Men Employed - Mining.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":223,"value":"Employment_Women_Mining = d3.csvParse(await FileAttachment(\"Women Employed - Mining.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":229,"value":"Employment_Women_Utilities = d3.csvParse(await FileAttachment(\"Women Employed - Utilities.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"Employment_Men_Utilities = d3.csvParse(await FileAttachment(\"Men Employed - Utilities.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":384,"value":"Sectors = ['Leisure and Hospitality', 'Education and Health Services', 'Retail', 'Business', 'Manufacturing', 'Transportation and Warehousing', 'Information', 'Wholesale Trade', 'Financial', 'Construction', 'Mining', 'Utilities']","pinned":false,"hidden":false,"mode":"ojs"},{"id":560,"value":"md`### Employment Levels by Age Groups`","pinned":false,"hidden":false,"mode":"ojs"},{"id":565,"value":"Employment_16to24 = d3.csvParse(await FileAttachment(\"Employment Level 16-24.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":568,"value":"Employment_25to34 = d3.csvParse(await FileAttachment(\"Employment Level 25-34.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":571,"value":"Employment_35to44 = d3.csvParse(await FileAttachment(\"Employment Level 35-44.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":574,"value":"Employment_45to54 = d3.csvParse(await FileAttachment(\"Employment Level 45-54.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":577,"value":"Employment_55Up = d3.csvParse(await FileAttachment(\"Employment Level 55 Over.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":264,"value":"md`### EPI Current Population Survey - Wages`","pinned":false,"hidden":false,"mode":"ojs"},{"id":270,"value":"Wage_Percentile_Race = d3.csvParse(await FileAttachment(\"EPI Wage - Percentile - Race.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":279,"value":"Wage_Percentile_Gender = d3.csvParse(await FileAttachment(\"EPI Wage - Percentile - Gender.csv\").text(), d3.autoType)","pinned":false,"hidden":false,"mode":"ojs"},{"id":285,"value":"md`## Line Plots`","pinned":false,"hidden":false,"mode":"ojs"},{"id":336,"value":"md`### The job market is still in a hole\nUS employers cut 22 million jobs in March and April of 2020. Since then, just over half have been recovered.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":318,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":325,"value":"md`### Black and Latino workers still have higher unemployment rates\nBoth before and during the pandemic, Black and Latino workers have generally seen higher unemployment rates than White and Asian workers.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":294,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":373,"value":"md`### Women have lost more jobs than men during the pandemic\nAs of February 2021, women were still down 5.1 million jobs compared to the same time last year.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":347,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":609,"value":"md`### Age Groups Suffer Similar Trends in Employment Loss\nEmployees belonging to different age group suffer similar trends in employment loss since January 2020.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":580,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":623,"value":"md`## Line Plot Analysis`","pinned":false,"hidden":false,"mode":"ojs"},{"id":626,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":453,"value":"md`## Bar Charts`","pinned":false,"hidden":false,"mode":"ojs"},{"id":350,"value":"md`### Women-led sectors have lost more jobs\nOne reason women lost more jobs is because there were steep cuts in leisure and hospitality, as well as in education and health services — sectors where women are the majority of workers.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":355,"value":"{\n const myDiv = html`

`;\n\n const trace1 = {\n x: Employment_Men_Utilities.map((d) => d.Value * 1000),\n y: Sectors,\n name: \"Women\",\n type: \"bar\",\n orientation: \"h\",\n marker: {\n color: \"red\"\n }\n };\n\n const trace2 = {\n x: Employment_Women_Utilities.map((d) => d.Value * 1000),\n y: Sectors,\n name: \"Men\",\n type: \"bar\",\n orientation: \"h\",\n marker: {\n color: \"navy\"\n }\n };\n\n const data = [trace1, trace2];\n\n const layout = { barmode: \"group\" };\n\n Plotly.newPlot(\n myDiv,\n data,\n {\n margin: { t: 50, b: 50, l: 200, r: 50 }\n },\n layout\n );\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":801,"value":"{\n const myDiv = html`

`;\n\n const trace1 = {\n x: Employment_Men_LeisureandHospitality.map((d) => d.Value),\n y: Employment_Men_LeisureandHospitality.map((d) => d.Label),\n name: \"Men\",\n type: \"bar\",\n orientation: \"h\",\n marker: {\n color: \"navy\"\n }\n };\n\n const trace2 = {\n x: Employment_Women_LeisureandHospitality.map((d) => d.Value),\n y: Employment_Women_LeisureandHospitality.map((d) => d.Label),\n name: \"Women\",\n type: \"bar\",\n orientation: \"h\",\n marker: {\n color: \"red\"\n }\n };\n\n const data = [trace1, trace2];\n\n const layout = { barmode: \"group\" };\n\n Plotly.newPlot(\n myDiv,\n data,\n {\n margin: { t: 50, b: 50, l: 200, r: 50 }\n },\n layout\n );\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":525,"value":"md`### Lower-paid workers lost the most jobs during the pandemic\nFrom 2019 to 2020, workers paid in the bottom fourth of the income spectrum lost nearly 7.9 million jobs, while highest-paid workers gained nearly a million jobs.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":662,"value":"md`### EPI Wage Percentile\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":699,"value":"{\n const myDiv = html`

`;\n const years = Wage_Percentile_Race.map((d) => d.Date);\n\n const data = [\n {\n x: years,\n y: Wage_Percentile_Race.map((d) => d.Average),\n type: \"bar\",\n transforms: [\n {\n type: \"sort\",\n target: \"y\"\n }\n ],\n marker: {\n color: \"red\"\n }\n }\n ];\n const layout = {\n title: \"Average Wage Percentile\",\n yaxis: {\n title: \"Dollars ($)\"\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":667,"value":"md`## Bar Chart Analysis`","pinned":false,"hidden":false,"mode":"ojs"},{"id":670,"value":"md`### Bar Chart 1: Unemployment Level by Sector and Gender\nOver the past 12 months, the female labor force participation rate fell from 57.8% to 55.8%, according to the Bureau of Labor Statistics. This chart shows the closest resemblance to the actual chart on the CNN website.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":764,"value":"md`### Bar Chart 2: Lower-paid workers lost the most jobs during the pandemic\nThis chart was never able to be replicated due to the author's source not giving enough information. I went to the EPI website and pulled what I thought was the closest data source to the one used in the chart. But this data with the same title of \"Economic Policy Institute analysis of Current Population Survey Outgoing Rotation Group microdata\" didn't have a value for number of jobs, only the wages each year up to 2019...which is showed in the next chart. These were also sorted by race and gender.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":771,"value":"md`### Bar Chart 3: EPI Wage Percentage\nThe EPI website only gives you so much data. Out of the few data sets, the wage data made the most sense in order to replicate each fourth of the income spectrum from 2019 to 2020. But after further review, this data only went up to 2019. Therefore, I am unsure the CNN reporters source is correct.\n`","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}