3f:null 40:["$","title",null,{"children":"The Morpheus Project: Shining a Light on Diversity in Tech | A.J. Angus | Observable"}] 46:T59e,## Overview LinkedIn post with more context on [The Morpheus Project](https://www.linkedin.com/pulse/morpheus-project-shining-light-diversity-tech-a-j-angus/). The Morpheus Project aims to shine light on the progress over time in the tech industry in creating a diverse employee base. This is a dataset of technology companies that have disclosed their [EEO-1](https://www.eeoc.gov/employers/eeo-1-data-collection) data around US based employees. EEO-1 reports detail demographic workforce data including data by race/ethnicity, sex and job categories. You see many companies talking a big game on diversity, but with very few actual results or accountability because they are not required to publicly disclose this information. Oracle famously sued to prevent releasing their EEO-1 data, but [later relented](https://www.marketwatch.com/story/oracle-finally-releases-detailed-diversity-numbers-and-they-arent-great-11604966287) under shareholder pressure. Even among companies in this dataset, there is no standardization around visualizations and data schema to create easy comparisons across companies. This is where The Morpheus Project kicks in. This data was hand-collected from each company's website, so it may contain (human) errors. Contact [me](mailto:aj@ajangus.com) and I'll fix any that come up. Don't like this visualization, but like this data? Feel free to download the data and contribute your own visualizations. 47:T613,## Diversity Data Table The below table shows the output based on the above selected filters. - Data source: Each data point comes from the yearly company diversity report. Where available, the data shows global gender and US ethnicity data. - Bar width: Will show a larger bar scaled based on the *maximum* value observed in that diversity factor across the whole dataset - Bar color: This color will show different colors based on the median value across the dataset - **Over-represented groups** (White, Asian): The bar will show **green** when it is *below* the median value and **red** when it's *above*. - **Under-represented groups** (everyone else): The bar will show **green** when it is *above* the median value and **red** when it's *below*. - URM (Under-represented Minority): All but the URM number comes from the company's diversity report. The Morpheus Project calculates the URM value in order to show this comparison across companies. This project takes the position that URM in the tech industry is one of the following: *Black, Hispanic, Native American, Native Hawaiian and Multi-racial*. - "Other": This is employees that (1) did not disclose their ethnicity information or (2) accounts for inconsistent data disclosure by each company. #### Filters - *Overall vs. Tech filter*: `Overall` is the composition across the entire company. `Tech` means the technical workers within teams at each company. - *Company*: Focus on specific companies by selecting them. Fewer companies have reported their `Tech` workers consistently.48:T5b4,viewof table = Inputs.table((companyFilter.length > 0) ? companyFilter.flat() : filtered_data, { format: { Company: x => x.split('"').join(''), Year: x=> new Date(x).getFullYear(), Women: sparkbar_minority_group(d3.median(filtered_data, d => d.Women), d3.max(filtered_data, d => d.Women)), White: sparkbar_majority_group(d3.median(filtered_data, d => d.White), d3.max(filtered_data, d => d.White)), Asian: sparkbar_majority_group(d3.median(filtered_data, d => d.Asian), d3.max(filtered_data, d => d.Asian)), Black: sparkbar_minority_group(d3.median(filtered_data, d => d.Black), d3.max(filtered_data, d => d.Black)), Hispanic: sparkbar_minority_group(d3.median(filtered_data, d => d.Hispanic), d3.max(filtered_data, d => d.Hispanic)), Multi: sparkbar_minority_group(d3.median(filtered_data, d => d.Multi), d3.max(filtered_data, d => d.Multi)), Native: sparkbar_minority_group(d3.median(filtered_data, d => d.Native), d3.max(filtered_data, d => d.Native)), URM: sparkbar_minority_group(d3.median(filtered_data, d => d.URM), d3.max(filtered_data, d => d.URM)), Other: sparkbar_minority_group(d3.median(filtered_data, d => d.Other), d3.max(filtered_data, d => d.Other)), "Hawaiian/PI": sparkbar_minority_group(d3.median(filtered_data, d => d["Hawaiian/PI"]), d3.max(filtered_data, d => d["Hawaiian/PI"])), //Black: x=> x + "%", }, sort: "Year", reverse: true, rows: 12, multiple: false });49:[{"id":"ec249c876a00d8260108435da11be71321a4c3655ec75b52f4394c34c3caf24a56d98af42408907706910c6201f45e3349cbcc3ff4eb6c4648acdc9ce6a4fbcc","href":"https://static.observableusercontent.com/files/ec249c876a00d8260108435da11be71321a4c3655ec75b52f4394c34c3caf24a56d98af42408907706910c6201f45e3349cbcc3ff4eb6c4648acdc9ce6a4fbcc","name":"Morpheus - Tech Diversity - raw@7.csv","type":"text/csv","lastModified":1735793258992.273,"size":38856},{"id":"9449f4a05aff484e5157a74daff85968f0d492c04dfbffd6c91b403f4a1e87dfe88a84245b7c31950c3979529c0099380f5eaa10f2e360ccced2ecb24a150912","href":"https://static.observableusercontent.com/files/9449f4a05aff484e5157a74daff85968f0d492c04dfbffd6c91b403f4a1e87dfe88a84245b7c31950c3979529c0099380f5eaa10f2e360ccced2ecb24a150912","name":"Morpheus - Tech Diversity - summary@7.csv","type":"text/csv","lastModified":1735794410142.013,"size":752},{"id":"0885862447ddd3790b38cc2a81dd6c7c281049da1ab9fb29d38bc434c024037995ff3680d05dbaef6e6645c0598d90ece86d4adc986727e32163faf56ab7b93b","href":"https://static.observableusercontent.com/files/0885862447ddd3790b38cc2a81dd6c7c281049da1ab9fb29d38bc434c024037995ff3680d05dbaef6e6645c0598d90ece86d4adc986727e32163faf56ab7b93b","name":"logo-final.png","type":"image/png","lastModified":1659232117833.558,"size":135527}] 44:{"id":"af33f69c8a66a310","slug":"the-morpheus-project","title":"The Morpheus Project: Shining a Light on Diversity in Tech","thumbnail":"9c786794643b8f9ed968bad6399898d05741fa2922ea95a8e196e26a635f6966","access_level":"public","create_time":"$D2022-03-14T01:47:12.082Z","update_time":"$D2025-01-02T05:06:49.726Z","view_time":null,"first_public_time":"$D2022-08-29T13:06:30.198Z","has_unpublished_edits":null,"fork":true,"stars":8,"starred":false,"owner":{"type":"team","id":"efc13da6f327985d","login":"a38c239118314682","name":"A.J. 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\n\n# The Morpheus Project: Shining a Light on Diversity in Tech","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2097,"value":"**${d3.group(raw_data, d => d.Company).size}** companies represented as of August 10th, 2023","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1,"value":"$46","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1238,"value":"$47","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":740,"value":"viewof overallAgg = Inputs.select([\"Overall\", \"Tech\"], {label: \"Overall vs. Tech\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":659,"value":"viewof companyFilter = Inputs.checkbox(d3.group(filtered_data, d => d.Company), {label: \"Company\", format: d => html`${d[0].split('\"').join('')}`})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1002,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2996,"value":"## [NEW!] Tech industry diversity over time\n\nThe below charts shows the changes in **median** representation in the tech industry over time. The latest year (2023) reflects incomplete results. \n\n#### Median representation of women over time\n- Across all roles (overall) in tech companies, representation has stayed flat (**34% in 2014 vs. 34.8% in 2022**).\n- Within tech roles, there is a **50% increase** in women in the industry.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2941,"value":"Plot.plot({\n y: {\n domain: [0, 45]\n },\n width: 1200,\n height: 250,\n marginRight: 75,\n marks: [\n Plot.frame({ strokeOpacity: 0.1 }),\n Plot.lineY(\n gender_groups_tidy,\n Plot.binX(\n { y: \"median\" },\n {\n fx: \"Type\",\n fy: \"Group\",\n x: \"Year\",\n y: \"Percent\",\n stroke: \"#4775de\",\n tip: true\n }\n )\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3056,"value":"#### Median representation of under-represented minorities (URMs) over time\n- Across all roles (overall) in tech companies, there is a **65% increase** since 2014.\n- Within tech roles, there is an even larger **increase of 80%** in these groups since 2014.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2990,"value":"Plot.plot({\n y: {\n domain: [0, 20]\n },\n width: 1200,\n height: 250,\n marginRight: 75,\n marks: [\n Plot.frame({ strokeOpacity: 0.1 }),\n Plot.lineY(\n urm_groups_tidy,\n Plot.binX(\n { y: \"median\" },\n {\n fx: \"Type\",\n fy: \"Group\",\n x: \"Year\",\n y: \"Percent\",\n stroke: \"#4775de\",\n tip: true\n }\n )\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3097,"value":"Drivers of increased representation:\n- [Black representation](https://observablehq.com/@a38c239118314682/the-morpheus-project#cell-3070) in the industry has increased **overall by 147**% and in **tech roles by 230%** since 2014\n- [Hispanic representation](https://observablehq.com/@a38c239118314682/the-morpheus-project#cell-3070) in the industry has increased **overall by 58%** and in **tech roles by 71%** since 2015","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1249,"value":"## Company Diversity Over Time\nThis chart allows you to see the trends across companies over time. Use the filters below to focus on the slice that you care about. \n\n#### Filters\n- *Overall vs. Tech filter*: `Overall` is the composition across the entire company. `Tech` means the technical workers within teams at each company.\n- *Gender / Ethnicity*: See the breakdown in this part of the employee base by demographic","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2032,"value":"viewof overallAgg2 = Inputs.select([\"Overall\", \"Tech\"], {label: \"Overall vs. Tech\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":737,"value":"viewof groupAgg = Inputs.select([\"Women\", \"White\", \"Asian\", \"Black\", \"Hispanic\", \"Multi\", \"Native\", \"Hawaiian/PI\", \"URM\"], {label: \"Gender / Ethnicity\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1885,"value":"diversity_chart.legend(\"color\", {\n label: groupAgg + \" %\",\n tickSize: 4,\n ticks: 6,\n tickFormat: (d) => `${d}%`,\n width: 300,\n height: 50\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1725,"value":"diversity_chart = Plot.plot({\n grid: true,\n padding: 0.15,\n marginLeft: 150,\n x: { label: \"Year\", axis: \"top\" , transform: d => String(d) },\n y: { label: \"Company\" },\n color: { type: \"linear\", scheme: \"blues\" },\n width: 550,\n marks: [\n Plot.cell(filtered_data2, {\n x: d => d.Year.getUTCFullYear(),\n y: \"Company\",\n fill: d=> d[groupAgg]\n }),\n Plot.text(\n filtered_data2,\n colorContrast({\n x: d => d.Year.getUTCFullYear(),\n y: \"Company\",\n text: d=> d[groupAgg] + \"%\",\n fill: d=> d[groupAgg]\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1520,"value":"## Diversity in tech today\n\nWith the diversity programs each company has put in place and the changes in the industry over time, below charts show the current composition today. These charts show the latest information for each company. \n\n### Latest Overall Company Data - Median Composition ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3148,"value":"Plot.plot({\n x: {\n axis: \"top\",\n grid: true,\n tickFormat: \"%\",\n label: \"Median group %\"\n },\n y: { label: null, domain: Plot.valueof(summary_data, \"Category\") },\n width: 720,\n marginLeft: 100,\n height: 300,\n color: \"royalblue\",\n marks: [\n Plot.barX(summary_data, {\n x: \"Value_percent\",\n y: \"Category\",\n fill: \"royalblue\"\n }),\n Plot.ruleX([0]),\n Array.from(\n d3.group(summary_data, (d) => d.Value_percent < 0.01),\n ([small, values]) =>\n Plot.text(values, {\n text: (d) => `${(100 * d.Value_percent).toFixed(1)}%`,\n y: \"Category\",\n x: \"Value_percent\",\n textAnchor: small ? \"start\" : \"end\",\n dx: small ? 4 : -4,\n fill: small ? \"black\" : \"white\"\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1528,"value":"### Latest Tech Worker Data - Median Composition \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3139,"value":"Plot.plot({\n x: {\n axis: \"top\",\n grid: true,\n tickFormat: \"%\",\n label: \"Median group %\"\n },\n y: { label: null, domain: Plot.valueof(summary_data_tech, \"Category\") },\n width: 720,\n marginLeft: 100,\n height: 300,\n color: \"steelblue\",\n marks: [\n Plot.barX(summary_data_tech, {\n x: \"Value_percent\",\n y: \"Category\",\n fill: \"steelblue\"\n }),\n Plot.ruleX([0]),\n Array.from(\n d3.group(summary_data_tech, (d) => d.Value_percent < 0.01),\n ([small, values]) =>\n Plot.text(values, {\n text: (d) => `${(100 * d.Value_percent).toFixed(1)}%`,\n y: \"Category\",\n x: \"Value_percent\",\n textAnchor: small ? \"start\" : \"end\",\n dx: small ? 4 : -4,\n fill: small ? \"black\" : \"white\"\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3007,"value":"## Tech industry diversity over time - full data\n### Over-represented groups\nThe below charts show the representation over time between Asian and White groups in the tech industry.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2813,"value":"Plot.plot({\n y: {\n domain: [0, 65]\n },\n width: 1200,\n height: 400,\n marginRight: 75,\n marks: [\n Plot.frame({ strokeOpacity: 0.1 }),\n Plot.lineY(\n larger_groups_tidy,\n Plot.binX(\n { y: \"median\" },\n {\n fx: \"Type\",\n fy: \"Group\",\n x: \"Year\",\n y: \"Percent\",\n stroke: \"#4775de\",\n tip: true\n }\n )\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3024,"value":"### Under-represented groups \nThe below charts show the representation over time between under-represented groups in the tech industry.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3070,"value":"Plot.plot({\n y: {\n domain: [0, 8]\n },\n width: 1200,\n marginRight: 75,\n marks: [\n Plot.frame({ strokeOpacity: 0.1 }),\n Plot.lineY(\n smaller_groups_tidy,\n Plot.binX(\n { y: \"median\" },\n {\n fx: \"Type\",\n fy: \"Group\",\n x: \"Year\",\n y: \"Percent\",\n stroke: \"#4775de\",\n tip: true\n }\n )\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1702,"value":"### Additional reading\n- [Diversity in Tech 2014-2017 Data](https://www.informationisbeautiful.net/visualizations/diversity-in-tech/)\n- [Silicon Valley Diversity Data Github repo](https://github.com/cirlabs/Silicon-Valley-Diversity-Data)\n- [WSJ 2015 Diversity Data](http://graphics.wsj.com/diversity-in-tech-companies/)\n- [Proposal to SEC for EEO-1 data mandate](https://www.prnewswire.com/news-releases/investors-urge-sec-to-mandate-disclosure-of-eeo-1-workforce-composition-data-301428435.html)\n- [Tech Diversity visualized in their logos](https://www.fastcompany.com/90428465/see-big-techs-terrible-diversity-record-visualized-using-its-logos)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":721,"value":"raw_data = FileAttachment(\"Morpheus - Tech Diversity - raw@7.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1376,"value":"raw_summary_data = FileAttachment(\"Morpheus - Tech Diversity - summary@7.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1485,"value":"summary_data = raw_summary_data.filter(d => d.Year >= new Date(\"2021-01-02\") && d.Type === \"Overall\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1505,"value":"summary_data_tech = raw_summary_data.filter(d => d.Year >= new Date(\"2021-01-02\") && d.Type === \"Tech\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":723,"value":"filtered_data = raw_data.filter(d => d.Type === overallAgg)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2037,"value":"filtered_data2 = raw_data.filter(d => d.Type === overallAgg2)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1727,"value":"colorContrast = ({ luminance = 60, ...options }) =>\n Plot.initializer(options, (data, facets, channels, scales) => {\n const contrast = (color) =>\n d3.hcl(color).l > luminance ? \"black\" : \"white\";\n return {\n channels: {\n fill: {\n ...channels.fill,\n scale: undefined,\n value:\n channels.fill == null // when options.fill is a constant color such as \"red\"\n || scales.color == null // when the scale isn't yet available (e.g. hexbin below)\n ? Array.from(data).fill(contrast(options.fill))\n : Array.from(channels.fill.value, (value) => contrast(scales.color(value)))\n }\n }\n };\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1131,"value":"function sparkbar_majority_group(median, max) { \n return x => htl.html`

= median ? '#FFCCCB' : '#C2FFC2' };\n width: ${100 * x / max}%;\n float: right;\n padding-right: 3px;\n box-sizing: border-box;\n overflow: visible;\n display: flex;\n justify-content: end;\">${x.toLocaleString(\"en\")+\"%\"}`\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1135,"value":"function sparkbar_minority_group(median, max) { \n return x => htl.html`

= median ? '#C2FFC2' : '#FFCCCB' };\n width: ${100 * x / max}%;\n float: right;\n padding-right: 3px;\n box-sizing: border-box;\n overflow: visible;\n display: flex;\n justify-content: end;\">${x.toLocaleString(\"en\")+\"%\"}`\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1491,"value":"import {BarChart} from \"@d3/horizontal-bar-chart\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2439,"value":"import {aq, op} from '@uwdata/arquero'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1669,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2410,"value":"raw_data_larger_groups = raw_data","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"raw_data_larger_groups","database":"$undefined"},{"id":2521,"value":"raw_data_smaller_groups = raw_data","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"raw_data_smaller_groups","database":"$undefined"},{"id":2922,"value":"raw_data_gender_groups = raw_data","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"raw_data_gender_groups","database":"$undefined"},{"id":2982,"value":"raw_data_URM_groups = raw_data","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"raw_data_URM_groups","database":"$undefined"},{"id":2448,"value":"smaller_groups_tidy = aq\n .from(raw_data_smaller_groups)\n .fold([\"Black\", \"Hispanic\", \"Multi\", \"Native\", \"Hawaiian/PI\"], {\n as: [\"Group\", \"Percent\"]\n })\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2516,"value":"larger_groups_tidy = aq\n .from(raw_data_larger_groups)\n .fold([\"Asian\", \"White\"], { as: [\"Group\", \"Percent\"] })\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2930,"value":"gender_groups_tidy = aq\n .from(raw_data_gender_groups)\n .fold([\"Women\"], { as: [\"Group\", \"Percent\"] })\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2986,"value":"urm_groups_tidy = aq\n .from(raw_data_URM_groups)\n .fold([\"URM\"], { as: [\"Group\", \"Percent\"] })\n .objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3108,"value":"Plot.plot({\n caption: \"test\",\n marks: [\n Plot.auto(urm_groups_tidy, {x: \"Year\", y: {value: \"Percent\", reduce: \"median\"}, fx: \"Group\", fy: \"Type\", color: \"#4775de\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W49","stdlib":"1"}} 45:"$44:body"