2b:null 2c:["$","title",null,{"children":"Consumer Industry Hit Hardest by COVID-19 Layoffs | Harshit Gajjar | Observable"}] 31:T65e,## Made-up Insights (Hypothesis) * Industries having group gathering or coming together should be the worst hit. For example like food, transportation, travel industry should be most impacted in terms of layoff, due to covid-19 pandemic. Since there were lockdowns, social distancing was practiced. [**Selected**] * **Considerations** * I will need enough data of all the companies. * I need to iterate all the records and group all the companies by industry. * In terms of the idiom (visualization), I would need to display the summation of all layoff by each company for any given industry and provide an easy way of comparing which industries had most layoff. Plotting a bar chart of industry and summation of layoff by companies will help me determine the impact on each industry. Using colors in bar chart can help distinguish number of layoff for any given industry across years(2020 - 2023) will be more meaningful * Early stage startup like in the seed stage, series A funding should have been impacted the most, in terms of layoff since investors were not sure with the market and they backed off from making investment forcing startups to shut down or layoff employees * **Considerations** * I will need enough data of all the companies. * I need to iterate all the records and group all the companies by stages. * In terms of the idiom (visualization), I would need to display the summation of all layoff by each company for any given stage of the company and provide an easy way of comparing different stages (seed, Series A, IPO) had most layoff by plotting a bar chart32:T785,## Data Abstraction (What) * **Dataset type:** Temporal * **Attributes used:** * company * Attribute Type - Categorical * industry * Attribute type - Categorical * total_laid_off * Attribute type - Quantitative, * Ordering Directions - Sequential * date (year only) * Attribute type - Ordinal * Ordering Directions - Sequential * **Derived Attributes:** A attributed named "Number of Employees laid off" which is summation of total_laid_off attribute for specified conditions. * Attribute Type - Quantitative * Orientation - Sequential * **Availability:** Static * **Time Frame of Dataset:** Mar 2020 - Jan 2023 ## Tasks Abstraction (Why) To confirm if my selected insights is true we will examine the dataset with the help of the following tasks. - **T1**: **Discover** the total number of layoff across all industry over the years of pandemic (March 2020 onwards) **[Discover, Extreme]** - **T2**: **Identify** the top companies with maximum layoff in an industry, for a given year or combination of years **[Identify, Extreme]** - **T3**: **Identify** the top industry which had maximum layoff for any given year and combination of years **[Identify, Extreme]** ## Marks and Channels (How) #### Mark:- Line #### Channel - **Length:** To denote number of layoff. More the length, more the layoff. This helps user see visually how many employees were laid off in a given industry for a specific year or summation of layoff of all years - Attribute: Number of Employees laid off - Task: T1, T2 - **Horizontal Position:** To determine, which industry had the highest layoff for any given year or summation of layoff for all years - Attribute: Number of Employees laid off - Task: T1, T2 - **Color Saturation:** To identify total number of layoff for an industry in a given year. - Attribute: Number of Employees laid off, Date(year) - Task: T333:T6ce,## Real Insights - As we saw that "Consumer" industry was hit the hardest this pandemic. This industry saw layoffs across the years from 2020 to 2023 (till Jan 23, 2023). This industry suffered the maximum layoff not during the start of pandemic which was in 2020 but lately that is from 2022. During the start of pandemic, in 2020 this industry stood 6th position, followed by 3rd position by 2021, followed by 2nd position in 2022 and they claimed the 1st spot in 2023. This industry was worst hit due to the massive layoff by tech giants such as Google, Meta, Twitter and Bytedance. This is completely different from my example insights since I expected industries like Travel, Transport, Food to be worst hit due to travel restrictions, lockdowns and practising social distancing. - "Retail" industry stood 2nd in terms of layoff. This industry overall didn't have much layoff, but it's 2nd position is due to the massive layoff by one company, that is Amazon that laid of 18000 employees, from the total of around 38000 employees laid off during all years. In this industry many companies did layoff but they were small in number, less than 1000 as compared to Amazon which topped the chart. The company which laid off most after Amazon in this industry is Groupon, with around 3300 employees being let go. Even in "Sales" industry, Salesforce had 10,000 layoff and company with 2nd highest layoff in this industry was docusign with 671 layoff. - Industries like "Transportation", "Travel" were most hit in 2020, which was due to lockdowns across various parts of world. "Transportation" industry remained the worst hit industry till 2022. "Food" industry maintained it spot in top 5 spot every year in terms of number of people lay-off.34:T409,{ const plot1 = vl .markLine() .encode( vl.x().fieldT("date").timeUnit("month").title("Month"), vl.y().sum("total_laid_off").title("Total Employees Laid off"), vl.tooltip().sum("total_laid_off") ) .data( pandemic_month_year.length > 0 ? layoff21.filter((f) => pandemic_month_year.includes(f.date.split("-")[0]) ) : layoff21 ); const plot2 = vl .markBar() .encode( vl.y().fieldN("date").timeUnit("month").title("Company"), vl .x() .sum("total_laid_off") .title( generateXAxisTxt("Number of Employees laid off", pandemic_month_year) ), vl.color().fieldO("date").timeUnit("year").title("Year"), vl.tooltip().sum("total_laid_off") ) .data( pandemic_month_year.length > 0 ? layoff21 .slice() .filter((f) => pandemic_month_year.includes(f.date.split("-")[0])) : layoff21 ); return vl.hconcat(plot1, plot2).render(); }35:T404,{ return mas.length > 0 ? vl .markBar() .encode( vl.y().fieldO("company").title("Company").sort("-x"), vl .x() .sum("total_laid_off") .title( generateXAxisTxt( `Number of Employees laid off`, pandemic_cty_year ) + " in " + mas ), vl.color().fieldO("date").timeUnit("year").title("Year"), vl.tooltip().sum("total_laid_off") ) .data( pandemic_cty_year.length > 0 ? layoff21 .slice() .filter( (f) => mas.includes(f.country) && pandemic_cty_year.includes(f.date.split("-")[0]) ) .splice(0, 21) : layoff21 .slice() .filter((f) => mas.includes(f.country)) .splice(0, 21) ) .render() : null; }36:[{"id":"bddfcbaa73b6e4ec5eb53d4e7099a4f1cc9a988eb1a3791ceac5973878cd33ec6a50c018a12357e7a16a0c942dacf36666d6c55cb9547a53298a7916b336ad17","href":"https://static.observableusercontent.com/files/bddfcbaa73b6e4ec5eb53d4e7099a4f1cc9a988eb1a3791ceac5973878cd33ec6a50c018a12357e7a16a0c942dacf36666d6c55cb9547a53298a7916b336ad17","name":"layoffs 2.csv","type":"text/csv","lastModified":1675020807060.272,"size":145071}] 2f:{"id":"d26ee20b88525ae9","slug":null,"title":"Consumer Industry Hit Hardest by COVID-19 Layoffs","thumbnail":"1aa2450c51c1ae7003f5cc550c74ea179a8c1b367ba000ea33197344cf833fb7","access_level":"public","create_time":"$D2023-01-29T19:27:20.416Z","update_time":"$D2023-02-08T21:06:11.731Z","view_time":null,"first_public_time":"$D2023-01-29T19:27:20.416Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"666e343e9fc70af6","login":"hg24","name":"Harshit Gajjar","avatar_url":"https://avatars-next.observableusercontent.com/6f17a096ab3d83e4a953387b0069b305478a67613578aa9ebebb8eaa88a66abb","tier":"starter_2024"},"version":890,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"5e2ba07857faed1b","login":"hg24","name":"Harshit Gajjar","avatar_url":"https://avatars-next.observableusercontent.com/6f17a096ab3d83e4a953387b0069b305478a67613578aa9ebebb8eaa88a66abb","tier":null}],"members":[{"type":"individual","id":"5e2ba07857faed1b","login":"hg24","name":"Harshit Gajjar","avatar_url":"https://avatars-next.observableusercontent.com/6f17a096ab3d83e4a953387b0069b305478a67613578aa9ebebb8eaa88a66abb","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Consumer Industry Hit Hardest by COVID-19 Layoffs","cells":[{"id":0,"value":"# Consumer Industry Hit Hardest by COVID-19 Layoffs\nSpanning across years 2020 to 2023\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":544,"value":"md`\nCreated by [Harshit Gajjar](https://observablehq.com/@5e2ba07857faed1b?tab=profile)
\nClass: [Data Visualization](https://johnguerra.co/classes/data_visualization_spring_2023/)
\nInstructor: John Alexis Guerra Gomez
\n[Data Source](https://www.kaggle.com/datasets/swaptr/layoffs-2022)
\n[Video](https://youtu.be/rubx5_bLdw4)\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"## Introduction\nCovid-19 pandemic had a profound impact on our lives and global economy. Layoffs have become a bitter truth of the pandemic, as businesses struggle to stay afloat in the face of declining demand and uncertain economic conditions, resulting in widespread job losses and financial hardship for many. Various Industries were impacted by the pandemic and people payed the price of it by loosing their job.\n\nIn this notebook, we will be analyzing the layoff during pandemic from the duration March 2020 to Jan 2023. We will be analyzing which industry was impacted the most and also analyze number of layoff for each industry across years. We will also see the top companies that lay-off maximum number of people in terms of count.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":59,"value":"## Layoff in different industries during pandemic years(March 2020 - Jan 2023)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":853,"value":"### Industry which laid off maximum employees for a given time frame","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":849,"value":"viewof pandemic_year_industry = {\n init;\n return Inputs.checkbox(\n Array.from(yearsSet).sort((a, b) => a - b),\n {\n label: \"Year\"\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":841,"value":"vl\n .markBar()\n .encode(\n vl.y().fieldN(\"industry\").title(\"Industry\").sort(\"-x\"),\n vl\n .x()\n .sum(\"total_laid_off\")\n .title(\n generateXAxisTxt(\"Number of employees laid off\", pandemic_year_industry)\n ),\n vl.color().fieldO(\"date\").timeUnit(\"year\").title(\"Year\"),\n vl.tooltip([\"industry\", vl.x().sum(\"total_laid_off\").title(\"layoff count\")])\n )\n .data(\n pandemic_year_industry.length > 0\n ? layoff21\n .slice()\n .filter((f) => pandemic_year_industry.includes(f.date.split(\"-\")[0]))\n : layoff21\n )\n .render()","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":"$undefined"},{"id":844,"value":"### View top 30 companies that laid off employees for a given industry and given year or combination of years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":346,"value":"viewof pandemic_year = {\n init;\n return Inputs.checkbox(\n Array.from(yearsSet).sort((a, b) => a - b),\n {\n label: \"Year\"\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"viewof color = {\n init;\n return Inputs.select(mutable industrySet, {\n value: \"steelblue\",\n label: \"Industry\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"vl\n .markBar()\n .encode(\n vl\n .y()\n .fieldN(\"company\")\n .title(`Top Companies laid off in ${color} industry`)\n .sort(\"-x\"),\n vl\n .x()\n .sum(\"total_laid_off\")\n .title(\n generateXAxisTxt(\n `Total Employees laid off in ${color} industry`,\n pandemic_year\n )\n ),\n vl.color().fieldO(\"date\").timeUnit(\"year\").title(\"Year\"),\n vl.tooltip().sum(\"total_laid_off\")\n )\n .data(\n pandemic_year.length > 0\n ? industry.records\n .get(color)\n .slice()\n .filter((f) => pandemic_year.includes(f.date.split(\"-\")[0]))\n .splice(0, 30)\n : industry.records.get(color).slice().splice(0, 30)\n )\n .render()\n\n// return vl.hconcat(vl.vconcat(plot1), plot2).render();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":723,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":735,"value":"### Users/Target Audience\n- All the individuals willing to change job.\n- All the individuals deciding to change/transition their career into different field. To analyze how is the job market.\n- Students looking for jobs and internships, for them to have an overview. They can view the companies that conducted layoff for different industries, across different years\n\n### Data\nThe dataset contains all the details about layoff. It includes the company name, the location, number of employees laid off, the country and much more. The dataset includes layoff data from March 2020 to Jan 2023.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":737,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":425,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":658,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":487,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":694,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":530,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":656,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":633,"value":"## Some more visualization to analyze layoff ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":676,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":45,"value":"### Layoff based on stages of company across years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":861,"value":"viewof pandemic_stage_year2 = {\n init;\n return Inputs.checkbox(\n Array.from(yearsSet).sort((a, b) => a - b),\n {\n label: \"Year\"\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":859,"value":"vl\n .markBar()\n .encode(\n vl.y().fieldO(\"stage\").title(\"Stage\").sort(\"-x\"),\n vl\n .x()\n .sum(\"total_laid_off\")\n .title(\n generateXAxisTxt(\"Number of Employees laid off\", pandemic_stage_year2)\n ),\n vl.color().fieldO(\"date\").timeUnit(\"year\").title(\"Year\"),\n vl.tooltip().sum(\"total_laid_off\")\n )\n .data(\n pandemic_stage_year2.length > 0\n ? layoff21\n .slice()\n .filter((f) => pandemic_stage_year2.includes(f.date.split(\"-\")[0]))\n : layoff21\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":870,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":867,"value":"### View top 30 companies that laid off employees for a given stage of company and given year or combination of years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":357,"value":"viewof pandemic_stage_year = {\n init;\n return Inputs.checkbox(\n Array.from(yearsSet).sort((a, b) => a - b),\n {\n label: \"Year\"\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"viewof stage = {\n init;\n return Inputs.select(stagesSet, {\n value: \"steelblue\",\n label: \"Stage\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":61,"value":"{\n return vl\n .markBar()\n .encode(\n vl.y().fieldO(\"company\").title(`${stage} stage Company`).sort(\"-x\"),\n vl\n .x()\n .sum(\"total_laid_off\")\n .title(\n generateXAxisTxt(\n `Employees laid off by ${stage} stage companies`,\n pandemic_stage_year\n )\n ),\n vl.color().fieldO(\"date\").timeUnit(\"year\").title(\"Year\"),\n vl.tooltip().sum(\"total_laid_off\")\n )\n .data(\n pandemic_stage_year.length > 0\n ? stages.records\n .get(stage)\n .slice()\n .filter((f) => pandemic_stage_year.includes(f.date.split(\"-\")[0]))\n .splice(0, 18)\n : stages.records.get(stage).slice().splice(0, 18)\n )\n .render();\n\n // return vl.hconcat(plot1, plot2).render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":674,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":285,"value":"### Top 20 companies with maximum layoff during pandemic","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":366,"value":"viewof pandemic_country_year = {\n init;\n return Inputs.checkbox(\n Array.from(yearsSet).sort((a, b) => a - b),\n {\n label: \"Year\"\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"{\n return vl\n .markBar()\n .encode(\n vl.y().fieldO(\"company\").title(\"Company\").sort(\"-x\"),\n vl\n .x()\n .sum(\"total_laid_off\")\n .title(\n generateXAxisTxt(\n \"Number of Employees laid off\",\n pandemic_country_year\n )\n ),\n vl.color().fieldO(\"date\").timeUnit(\"year\").title(\"Year\"),\n vl.tooltip().sum(\"total_laid_off\")\n )\n .data(\n pandemic_country_year.length > 0\n ? layoff21\n .slice()\n .sort(\n (a, b) => parseInt(b.total_laid_off) - parseInt(a.total_laid_off)\n )\n .filter((f) => pandemic_country_year.includes(f.date.split(\"-\")[0]))\n .splice(0, 20)\n : layoff21\n .slice()\n .sort(\n (a, b) => parseInt(b.total_laid_off) - parseInt(a.total_laid_off)\n )\n .splice(0, 20)\n )\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":672,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":42,"value":"### Layoff across different months","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":374,"value":"viewof pandemic_month_year = {\n init;\n return Inputs.checkbox(\n Array.from(yearsSet).sort((a, b) => a - b),\n {\n label: \"Year\"\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":257,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":670,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":329,"value":"### Number of employees laid off across years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":299,"value":"viewof year = {\n init;\n return Inputs.select(yearsSet, {\n value: \"steelblue\",\n label: \"Year\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":315,"value":"{\n yearData;\n return vl\n .markBar()\n .encode(\n vl.y().fieldN(\"company\").title(\"Company\").sort(\"-x\"),\n vl\n .x()\n .sum(\"total_laid_off\")\n .title(`Number of Employees laid off in ${year}`),\n vl.tooltip().sum(\"total_laid_off\")\n )\n .data(yearData.get(year).slice().splice(0, 20))\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":396,"value":"### Input country to view companies with top layoff in the countries selected","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":385,"value":"viewof mas = {\n init;\n return Inputs.select(Array.from(countrySet).sort(), {\n value: \"steelblue\",\n label: \"Country\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":403,"value":"viewof pandemic_cty_year = {\n init;\n return Inputs.checkbox(\n Array.from(yearsSet).sort((a, b) => a - b),\n {\n label: \"Year\"\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":399,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":681,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":646,"value":"### Number of employees laid off across different years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":636,"value":"vl\n .markBar()\n .encode(\n vl.y().fieldO(\"date\").timeUnit(\"year\").title(\"Year\"),\n vl.x().sum(\"total_laid_off\").title(\"Number of Employees laid off\"),\n vl.tooltip().sum(\"total_laid_off\")\n )\n .data(layoff21)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":653,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"## Data Processing\nWe perform the following steps in order to process the data fit for visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"### Importing the file and display raw data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"layoff2 = FileAttachment(\"layoffs 2.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"### Filtering rows to remove entries which don't have count of number of employees laid off","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":19,"value":"layoff21 = layoff2.filter((l) => l.total_laid_off != \"\" && l.stage !== \"\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"### Displaying raw data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"Inputs.table(layoff21)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"### Group all the layoff records by industry","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":130,"value":"mutable industry = generateVizData(\"industry\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"### Group all layoff records by Stages of Company","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":228,"value":"mutable stages = generateVizData(\"stage\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":607,"value":"### To get all the unique values in Industry in dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":138,"value":"mutable industrySet = new Set()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"### Function call to sort the map","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":132,"value":"function generateVizData(fieldName) {\n let obj = {};\n const industryMap = new Map();\n layoff21.forEach((l) => {\n if (industryMap.has(l[fieldName])) {\n industryMap.set(l[fieldName], [...industryMap.get(l[fieldName]), l]);\n } else {\n industryMap.set(l[fieldName], [l]);\n }\n });\n\n obj.records = sortIndustryData(industryMap);\n\n return obj;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":626,"value":"### Function to get all layoff grouped by industry","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":161,"value":"function sortIndustryData(layoffMap) {\n let newLayoffMap = new Map();\n\n for (let [key, value] of layoffMap) {\n newLayoffMap.set(\n key,\n layoffMap\n .get(key)\n .slice()\n .sort((a, b) => parseInt(b.total_laid_off) - parseInt(a.total_laid_off))\n );\n }\n\n return newLayoffMap;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":624,"value":"### Function call to get all layoff grouped by years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":307,"value":"function generateVizByYear() {\n const yearMap = new Map();\n\n Array.from(yearsSet).forEach((y) => yearMap.set(y, []));\n layoff21.forEach((l) => {\n let month = l.date.split(\"-\")[0];\n yearMap.set(month, [...yearMap.get(month), l]);\n });\n\n return yearMap;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":621,"value":"### Function call to get all layoff grouped by years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":309,"value":"yearData = {\n return generateVizByYear();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":619,"value":"### Function to store all unique values of industry, stages, years and country","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":205,"value":"function uniqueValuesGenerator() {\n let obj = {};\n layoff21.forEach((l) => {\n industrySet.add(l.industry);\n stagesSet.add(l.stage);\n yearsSet.add(l.date.split(\"-\")[0]);\n countrySet.add(l.country);\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":821,"value":"### Function to generate custom label for X-axis on the Visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":777,"value":"function generateXAxisTxt(str, arr) {\n if (arr.length === 0 || arr.length === 4) {\n return str + \" from 2020 to 2023\";\n } else if (arr.length === 1) {\n return str + \" in \" + arr[0];\n } else {\n str += \" in \";\n arr.forEach((y) => {\n str += y + \" + \";\n });\n return str.substring(0, str.length - 2);\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":617,"value":"### Function call to populate all the unique set values","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":794,"value":"init = {\n uniqueValuesGenerator();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":615,"value":"### To get all the unique values of stages of company in dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":189,"value":"mutable stagesSet = new Set()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":613,"value":"### To get all the unique values of years in dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":295,"value":"mutable yearsSet = new Set()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":611,"value":"### To get all the unique values of countries in dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":387,"value":"mutable countrySet = new Set()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"import { vl } from \"@vega/vega-lite-api-v5\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 30:"$2f:body"