2b:null 2c:["$","title",null,{"children":"UI Salary Data (using data.iowa.gov api) | Jose Jimenez | Observable"}] 31:T421,it_classifications = [ "APPLICATION ADMINISTRATOR", "APPLICATION ANALYST", "APPLICATION DEVELOPER", "APPLICATION PROGRAMMER/ANALYST", "ASSOCIATE NETWORK ENGINEER", "DATA ANALYST", "DATABASE ADMINISTRATOR", "DATABASE ARCHITECT", "EXECUTIVE IT DIRECTOR", "IT DIRECTOR", "IT MANAGER", "IT SECURITY ANALYST", "IT SECURITY ARCHITECT", "IT SUPPORT ANALYST", "IT SUPPORT CONSULTANT", "LEAD APPLICATION DEVELOPER", "LEAD DATABASE ADMINISTRATOR", "LEAD IT SUPPORT CONSULTANT", "NETWORK ARCHITECT", "NETWORK ENGINEER", "SENIOR APPLICATION ANALYST", "SENIOR APPLICATION ARCHITECT", "SENIOR APPLICATION DEVELOPER", "SENIOR BUSINESS ANALYST", "SENIOR DATA SCIENTIST", "SENIOR DATABASE ADMINISTRATOR", "SENIOR DATABASE ARCHITECT", "SENIOR IT DIRECTOR", "SENIOR IT SECURITY ANALYST", "SENIOR IT SECURITY OFFICER", "SENIOR IT SUPPORT CONSULTANT", "SENIOR NETWORK ENGINEER", "SENIOR SYSTEMS ADMINISTRATOR", "SENIOR SYSTEMS ARCHITECT", "SYSTEMS ADMINISTRATOR", "SYSTEMS ANALYST", "SYSTEMS ARCHITECT" ]32:Tfcc,positions = { let position_a = []; let years = new Set(); const unique_positions = [ ...new Set(data.map((row) => row.position.toUpperCase())) ].sort(); unique_positions.forEach((row) => { position_a[row] = new Object(); }); data.forEach((row) => { const year = row.fiscal_year; const position = row.position.toUpperCase(); let element = position_a[position]; years.add(year); if (row.total_salary_paid > min_salary) { if (!element.hasOwnProperty(year)) { element[year] = { minimum: 99999999999, maximum: -1, midpoint: -1, male: 0, female: 0, undisclosed: 0, male_total: 0.0, female_total: 0.0, undisclosed_total: 0.0, salaries: [], salaries_female: [], salaries_male: [] }; } element[year].salaries.push(parseFloat(row.total_salary_paid)); if (element[year].minimum > parseFloat(row.total_salary_paid)) element[year].minimum = parseFloat(row.total_salary_paid); if (element[year].maximum < parseFloat(row.total_salary_paid)) element[year].maximum = parseFloat(row.total_salary_paid); if (row.gender === "M") { element[year].male += 1; element[year].male_total += parseFloat(row.total_salary_paid); element[year].salaries_male.push(parseFloat(row.total_salary_paid)); } if (row.gender === "F") { element[year].female += 1; element[year].female_total += parseFloat(row.total_salary_paid); element[year].salaries_female.push(parseFloat(row.total_salary_paid)); } if (row.gender === "U") { element[year].undisclosed += 1; element[year].undisclosed_total += parseFloat(row.total_salary_paid); } } }); Object.keys(position_a).forEach((position) => { Object.keys(position_a[position]).forEach((year) => { let element = position_a[position][year]; element.midpoint = (element.maximum - element.minimum) / 2.0 + element.minimum; element.percent_male = Math.floor( (element.male / (element.male + element.female + element.undisclosed)) * 100 ); element.percent_female = Math.floor( (element.female / (element.male + element.female + element.undisclosed)) * 100 ); element.percent_undisclosed = Math.floor( (element.undisclosed / (element.male + element.female + element.undisclosed)) * 100 ); element.male_average = element.male > 0 ? element.male_total / element.male : 0; element.female_average = element.female > 0 ? element.female_total / element.female : 0; element.undisclosed_average = element.undisclosed > 0 ? element.undisclosed_total / element.undisclosed : 0; element.male_female_pay_gap = element.male > 2 && element.female > 2 ? element.male_average - element.female_average : 0; element.average_salary = d3.sum(element.salaries) / element.salaries.length; element.differences = element.salaries.map( (d) => d - element.average_salary ); element.squared_diffs = element.differences.map((d) => d * d); element.sum_of_squares = d3.sum(element.squared_diffs); element.variance = element.sum_of_squares / element.salaries.length - 1; element.standard_deviation = Math.sqrt(element.variance); element.male_female_gap_sds = Math.abs(element.male_female_pay_gap) > 0 ? element.male_female_pay_gap / element.standard_deviation : 0; element.female_outside_of_one_sd = element.salaries_female.filter( (d) => Math.abs(d - element.average_salary) > element.standard_deviation ); element.male_outside_of_one_sd = element.salaries_male.filter( (d) => Math.abs(d - element.average_salary) > element.standard_deviation ); }); }); return position_a; }33:Td47,position_summary = { const attrition_summary = [] last_years_only.forEach(row => { if(attrition_summary[row.position]) { if (attrition_summary[row.position][row.fiscal_year]) { attrition_summary[row.position][row.fiscal_year] = {female: attrition_summary[row.position][row.fiscal_year].female + (row.gender === 'F' ? 1 : 0), male: attrition_summary[row.position][row.fiscal_year].male + (row.gender === 'M' ? 1 : 0), undisclosed: attrition_summary[row.position][row.fiscal_year].undisclosed + (row.gender === 'U' ? 1 : 0) } } else { attrition_summary[row.position][row.fiscal_year] = {female: row.gender === 'F' ? 1 : 0, male: row.gender === 'M' ? 1 : 0, undisclosed: row.gender === 'U' ? 1 : 0 } } } else { attrition_summary[row.position] = [] attrition_summary[row.position][row.fiscal_year] = {female: row.gender === 'F' ? 1 : 0, male: row.gender === 'M' ? 1 : 0, undisclosed: row.gender === 'U' ? 1 : 0 } } }) const summary = [] Object.keys(positions).forEach( position => { const one_position = positions[position]; Object.keys(one_position).forEach( year => { const one_year = one_position[year] summary.push({ position: position, year: year, gender: 'M', count: one_year.male, percent: one_year.percent_male, attrition: attrition_summary[position] && attrition_summary[position][year] ? attrition_summary[position][year].male : 0, percent_attrition: one_year.male > 0 ? (Math.floor((attrition_summary[position] && attrition_summary[position][year] ? attrition_summary[position][year].male : 0) / one_year.male * 100)) : 0 }) summary.push({ position: position, year: year, gender: 'F', count: one_year.female, percent: one_year.percent_female, attrition: attrition_summary[position] && attrition_summary[position][year] ? attrition_summary[position][year].female : 0, percent_attrition: one_year.female > 0 ? (Math.floor((attrition_summary[position] && attrition_summary[position][year] ? attrition_summary[position][year].female : 0) / one_year.female * 100)) : 0 }) summary.push({ position: position, year: year, gender: 'U', count: one_year.undisclosed, percent: one_year.percent_undisclosed, attrition: attrition_summary[position] && attrition_summary[position][year] ? attrition_summary[position][year].undisclosed : 0, percent_attrition: one_year.undisclosed > 0 ? (Math.floor((attrition_summary[position] && attrition_summary[position][year] ? attrition_summary[position][year].undisclosed : 0) / one_year.undisclosed * 100)) : 0 }) }) } ) return summary }34:T544,current_year_PAI_employees = { let final_year = unique_years[unique_years.length -1] let lower_positions = app_dev_positions.map(d => d.toLowerCase()) let matching_rows = matched_data.filter(row => row.fiscal_year === final_year && lower_positions.includes(row.position.toLowerCase()) && parseFloat(row.base_salary) > 0) return matching_rows.map(d => ({...d, position_level: (lower_positions.indexOf(d.position.toLowerCase())+1)/lower_positions.length, gender_level: d.gender === "M" ? 1 : 0, parsed_base_salary: parseFloat(d.base_salary), log_base_salary: parseFloat(d.base_salary) == 0.00 ? 0 : Math.log(parseFloat(d.base_salary)), log_total_salary: Math.log(parseFloat(d.total_salary_paid)), is_hygienic: (!(typeof(d.agency_institution) === "undefined") && d.agency_institution === "Univ. Of Iowa - Hygienic Lab") ? 1 : 0, is_psych: (!(typeof(d.agency_institution) === "undefined") && d.agency_institution === "Univ. Of Iowa - Psych Hospital") ? 1 : 0, unknown_agency: typeof(d.agency_institution) === 'undefined' ? 1 : 0 })) }35:T4b1,generate_regression_plot = (source_data, salary_field, salary_field_label, against_field, against_label, chart_title) => { let base_chart = vl.markPoint() .title(chart_title) .data(source_data) .width(width /3) .height(250) let point_chart = base_chart .encode(vl.x().fieldQ(against_field).axis({title: against_label}), vl.y().fieldQ(salary_field).axis({title: salary_field_label}) ) let text_chart = base_chart.markText({align: 'right'}) .transform( [ { "regression": salary_field, "on": against_field, "params": true }, {"calculate": "'R²: '+format(datum.rSquared, '.2f')", "as": "R2"} ] ) .encode( vl.color(vl.value('firebrick')), vl.x(vl.value(50)), vl.y(vl.value(-5)), vl.text().fieldN('R2') ) let regression_line = base_chart.markLine({color: 'firebrick'}) .transform( [ { "regression": salary_field, "on": against_field } ] ) .encode( vl.x().fieldQ(against_field), vl.y().fieldQ(salary_field) ) return vl.layer(point_chart, regression_line, text_chart) }36:[] 2f:{"id":"989abe7802bcf0d6","slug":"ui-salary-data-using-data-iowa-gov-api","title":"UI Salary Data (using data.iowa.gov api)","thumbnail":"986cca849dc97d4b2dd7c98555f8fb4f8bcee7977dffddf57dfe81bf66a1b0fc","access_level":"public","create_time":"$D2020-01-29T20:09:45.028Z","update_time":"$D2026-05-26T17:26:19.321Z","view_time":null,"first_public_time":"$D2020-02-17T17:17:27.973Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"11eff763c21b0530","login":"jjimenez","name":"Jose Jimenez","avatar_url":"https://avatars-next.observableusercontent.com/5d28ac3054c420a200dcd8a0bd3367e2263d2f168c4aa5eaa0c89640622d6088","tier":"starter_2024"},"version":2792,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"214adfe1a708eb31","login":"jjimenez","name":"Jose Jimenez","avatar_url":"https://avatars-next.observableusercontent.com/5d28ac3054c420a200dcd8a0bd3367e2263d2f168c4aa5eaa0c89640622d6088","tier":null}],"members":[{"type":"individual","id":"214adfe1a708eb31","login":"jjimenez","name":"Jose Jimenez","avatar_url":"https://avatars-next.observableusercontent.com/5d28ac3054c420a200dcd8a0bd3367e2263d2f168c4aa5eaa0c89640622d6088","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":4,"collection":null,"type":"notebook","body":{"title":"UI Salary Data (using data.iowa.gov api)","cells":[{"id":0,"value":"md`# UI Salary Data (using data.iowa.gov api)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":614,"value":"md`## Compensation Ratios for a single classification as selected`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":413,"value":"viewof single_classification= select({\n title: \"Job Classification\",\n description: \"Please select a job classification to display.\",\n options: position_list,\n value: \"RESEARCH ASSISTANT\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":402,"value":"generate_boxplot([single_classification], 'compa', 'Compensation Ratio').render()\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":706,"value":"md`**reading this chart:**\nThis chart (for Research Assistants) compares the compensation ratios of individuals based on gender for a given year. Hovering over the colored rectangle for each year will show how women or men were paid in comparison to each other. In 2017 There are some women who were outliers in terms of their compensation (the circles above the blue rectangle). The rectangle encompases the second and third quartiles of compensation for women in the classification and the horizontal line indicates the midpoint of compensation ratios for the year. For men and women the compensation ratio median are both close to 70%. *How is that possible?* Remember this is based on the midpoint of the minimum and maximum salaries for all individuals in this job classification for the year, regardless of gender. The outliers (blue circles) drive the 100% mark for compensation ratio up above this midpoint. \n\nKeep reading to learn more about compensation ratios.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":715,"value":"md`## For reference, for the same job classification, where is what the salary looks like.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":712,"value":"generate_boxplot([single_classification], 'total_salary_paid', 'Salary').render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":731,"value":"md`## How this relates to previous notebooks about UI salaries`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":699,"value":"md`In the past, I've done screen scrapes for data from the State of Iowa Salary book. Now, the state of Iowa provides a data api to get similar data (it isn't exactly the ame in format). The [API](https://data.iowa.gov/Government-Employees/State-of-Iowa-Salary-Book/s3p7-wy6w) makes is easier to get the data we want to use.\n\nIn this workbook, I'm using different plots from vega-lite and I'm working with compensation ratios.\nTo learn more, read the article from [CUPAHR](https://www.cupahr.org/blog/simple-metrics-how-to-use-compa-ratios-to-guide-compensation-decisions/).\n\nFor the purposes of this notebook, the compensation ratio is set by comparing the salary of an individual to the midpoint between the minimum and the maximum salary for individuals within the same job classification during the year in question. \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":617,"value":"md`## Here is a similar chart for filtered postitions (by dollars)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":719,"value":"md`Here is the salary data for several job classifications. Note how the Senior Application Architect salaries are fairly high compared to the others. This is the highest job classification for this job family.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":407,"value":"viewof filtered_positions = select({\n title: \"Job Descriptions\",\n description:\n \"Select multiple positions (for example, all the jobs in a job classification).\",\n options: position_list,\n multiple: true,\n size: 10,\n value: use_library_positions ? library_position_sample : app_dev_positions\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":659,"value":"filtered_positions.join(',\\n')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2705,"value":"viewof use_library_positions = Inputs.button(\"Use Library Positions\", {\n value: false,\n reduce: (value) => !value\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":35,"value":"generate_boxplot(filtered_positions, 'total_salary_paid', 'Salary','position', filtered_positions.length).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":845,"value":" viewof single_person = autoSelect({\n title: 'Select a single individual for comparison',\n options: unique_matched_names,\n placeholder: \"Search for a specific prerson . . .\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":860,"value":"md`\"${single_person}\" selected`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":935,"value":"generate_person_plot(filtered_positions, 'total_salary_paid', 'Salary', 'position', filtered_positions.length).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":621,"value":"md`## Here is a compensation chart for the filtered positions (by compensation ratio based on the year for the job classification)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":723,"value":"md`The same data is presented here using compensation ratios instead. This normalizes the data into similar bands across all of the job classifications presented.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":307,"value":"generate_boxplot(filtered_positions, 'compa', 'Compensation Ratio','position', filtered_positions.length).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":948,"value":"generate_person_plot(filtered_positions, 'compa', 'Compensation Ratio', 'position', filtered_positions.length).render()\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1063,"value":"md`Here is a chart of percent of staff by gender in order to show how head-count differs for the job classifications listed.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1038,"value":"generate_bar_plot(filtered_positions, 'percent', 'Percent of Staff', 'position',filtered_positions.length ).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":626,"value":"md`## Attrition by year by gender for the filtered classifications`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1050,"value":"generate_bar_plot(filtered_positions, 'attrition', 'Number of Staff', 'position',filtered_positions.length ).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1056,"value":"generate_bar_plot(filtered_positions, 'percent_attrition', 'Percent of Gender', 'position',filtered_positions.length ).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1726,"value":"md`## Exploring Regression analysis in different ways\n\n\nHere is a table of regressions calculated using the [simple-statistics](https://www.npmjs.com/package/simple-statistics) npm package. \nThe first value for each salary column is the slope of the line found that best matches the data. The r-squared value is also calculated by the library. \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1494,"value":"table(regression_table_data,regression_table_options)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1763,"value":"md`\n\nHere is a table of regressions calculated using the [regression](https://www.npmjs.com/package/regression) npm package. \nThe first value for each salary column is the slope of the line found that best matches the data. the r-squared value is calculated by the library. \n\nThe values match closely for the log values, but not as closely for the bare salary data.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1741,"value":"table(regression_table_data_regression, regression_table_options)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1791,"value":"md`\n\nHere are some charts for Log of Base Salary including regression lines and r-squared values. Again the r-squared values are a close match to the above data.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1784,"value":"vl.vconcat(test_fields_and_labels.map(d => generate_regression_plot(current_year_PAI_employees,'log_base_salary','Log of Base Salary', d[0], d[1],`Regression for Log of Base Salary and ${d[1]}`))).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1799,"value":"md`\nAs we look at this data, it might be interesting to look at r-squared values for some other combinations of values: \n\nHere is a table of r-squared values across all of the non-salary variables. The r-squared values for a variable with itself should be 1.\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1888,"value":"table(other_regressions, other_regression_options)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":790,"value":"md`## Data and imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":833,"value":"md`### Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2671,"value":"library_this_year = Inputs.table(\n matched_data.filter(\n (d) => d.position == \"LIBRARIAN\" && d.fiscal_year == data_last_year\n )\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2681,"value":"library_positions = position_list.filter((d) => d.match(/LIBR/))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2695,"value":"viewof library_position_tbl = Inputs.table(\n library_positions.map((d) => ({ position: d}))\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2700,"value":"library_position_sample = [\n \"LIBRARIAN\",\n \"EXPERT LIBRARIAN\",\n \"SPECIALIST LIBRARIAN\",\n \"SR LIBRARIAN OR LIBRARY DIRECTOR\",\n \"SENIOR LIBRARIAN\"\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":781,"value":"minimum_year = {\n const today = new Date();\n const year = today.getFullYear();\n return year - 5\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":835,"value":"md`Raw data is brought into the notebook from the API in this call. Its alot of data, so it can take a while. The minimum year for the data is controlled by the \"minimum_year\" value above.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":683,"value":"raw_data = fetch(\n `https://data.iowa.gov/resource/s3p7-wy6w.json?$where=fiscal_year >= ${minimum_year}&department='UNIVERSITY OF IOWA'&$limit=500000`\n).then((response) => response.json())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1139,"value":"unique_years = [...(new Set(matched_data.map(row => row.fiscal_year)))].sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1948,"value":"data_first_year = unique_years[0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1950,"value":"data_last_year = unique_years[unique_years.length -1]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":391,"value":"max_salary = 5000000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":393,"value":"min_salary = 36336","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2,"value":"data = raw_data\n .filter(\n (row) =>\n parseFloat(row.total_salary_paid) > min_salary &&\n parseFloat(row.total_salary_paid) <= max_salary\n )\n .map((d) =>\n Object.assign(d, { total_salary_paid: parseFloat(d.total_salary_paid) })\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":157,"value":"matched_data = data.map(row => {\n let new_row = Object.assign({}, row)\n new_row.position = row.position.toUpperCase()\n const name_key = name_matches[fix_comma(new_row.name)]\n new_row.matched_name = name_key\n if (new_row.total_salary_paid > min_salary) {\n new_row.compa = compa(row.total_salary_paid, new_row.position, row.fiscal_year)\n const percents = percent_of_staff(new_row.position, row.fiscal_year)\n new_row.percent_male = percents.percent_male;\n new_row.percent_female = percents.percent_female;\n new_row.percent_undisclosed = percents.percent_undisclosed;\n new_row.last_year = max_years[name_key];\n new_row.first_year = min_years[name_key];\n new_row.years_in_dataset = new_row.last_year - new_row.first_year;\n }\n return new_row\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2212,"value":"all_women_last_year = matched_data.filter(\n (d) =>\n d.gender === \"F\" &&\n d.fiscal_year == data_last_year &&\n d.total_salary_paid > min_salary\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2566,"value":"pay_gap_classifications = filtered_positions; //it_classifications","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2493,"value":"filtered_women_for_year = (year) => matched_data.filter(\n (d) =>\n pay_gap_classifications.includes(d.position) &&\n d.gender === \"F\" &&\n d.fiscal_year == year &&\n d.total_salary_paid > min_salary\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2216,"value":"all_men_last_year = matched_data.filter(\n (d) =>\n d.gender == \"M\" &&\n d.fiscal_year == data_last_year &&\n d.total_salary_paid > min_salary\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2495,"value":"filtered_men_for_year = (year) =>\n matched_data.filter(\n (d) =>\n pay_gap_classifications.includes(d.position) &&\n d.gender == \"M\" &&\n d.fiscal_year == year &&\n d.total_salary_paid > min_salary\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2229,"value":"last_year_averages = {\n let men_average =\n all_men_last_year.reduce(\n (pv, cv) => (pv += parseFloat(cv.total_salary_paid)),\n 0\n ) / all_men_last_year.length;\n let women_average =\n all_women_last_year.reduce(\n (pv, cv) => (pv += parseFloat(cv.total_salary_paid)),\n 0\n ) / all_women_last_year.length;\n return [\n { label: \"men\", average: men_average },\n { label: \"women\", average: women_average },\n { label: \"difference\", average: men_average - women_average }\n ];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2502,"value":"last_year_averages_filtered_for_year = (year) => {\n let men_average =\n filtered_men_for_year(year).reduce(\n (pv, cv) => (pv += parseFloat(cv.total_salary_paid)),\n 0\n ) / filtered_men_for_year(year).length;\n let women_average =\n filtered_women_for_year(year).reduce(\n (pv, cv) => (pv += parseFloat(cv.total_salary_paid)),\n 0\n ) / filtered_women_for_year(year).length;\n return [\n {year: year, label: \"men\", average: men_average },\n {year: year, label: \"women\", average: women_average },\n {year: year, label: \"difference\", average: men_average - women_average }\n ];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2247,"value":"Plot.plot({\n width: 500,\n\n marks: [\n Plot.barY(last_year_averages, {\n x: \"label\",\n y: \"average\",\n fill: (d) => d.label,\n sort: { x: \"y\", reverse: true }\n }),\n Plot.ruleY([0])\n ],\n y: {\n tickFormat: d3.format(\"$,.2s\"),\n label: \"Unadjusted Pay Gap (averages across all job classifications)\"\n },\n x: { label: \"\" },\n color: {\n type: \"categorical\",\n scheme: \"tableau10\",\n columns: \"50px\"\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2533,"value":"pay_gap_data = [\n ...last_year_averages_filtered_for_year(data_last_year),\n ...last_year_averages_filtered_for_year(data_last_year - 1),\n ...last_year_averages_filtered_for_year(data_last_year - 2),\n ...last_year_averages_filtered_for_year(data_last_year - 3),\n ...last_year_averages_filtered_for_year(data_last_year - 4)\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2500,"value":"filtered_pay_gap = Plot.plot({\n width: 500,\n height: 900,\n\n marks: [\n Plot.barY(pay_gap_data, {\n x: \"label\",\n y: \"average\",\n fy: \"year\",\n fill: (d) => d.label,\n sort: { x: \"y\", reverse: true }\n }),\n Plot.ruleY([0]),\n Plot.text(\n pay_gap_data.filter((d) => d.label == \"difference\"),\n {\n x: \"label\",\n lineAnchor: \"top\",\n lineHeight: -4,\n y: \"average\",\n fy: \"year\",\n text: \"average\"\n }\n )\n ],\n y: {\n tickFormat: d3.format(\"$,.2s\"),\n label: \"Unadjusted Pay Gap (averages across all IT classifications)\"\n },\n x: { label: \"\" },\n color: {\n type: \"categorical\",\n scheme: \"tableau10\",\n columns: \"50px\"\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1273,"value":"matched_data.filter(row => row.name.match(/etsey, edud/i))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1935,"value":"women_app_dev = matched_data.filter(\n (row) =>\n row.fiscal_year == data_last_year &&\n row.gender == \"F\" &&\n [\n \"APPLICATION_DEVELOPER\",\n \"SENIOR APPLICATION DEVELOPER\",\n \"LEAD APPLICATION DEVELOPER\"\n ].includes(row.position)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2189,"value":"ris_developers = matched_data.filter(\n (row) =>\n row.fiscal_year == data_last_year && ris_people.includes(row.matched_name)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2751,"value":"LIT_Rows = matched_data.filter(\n (row) =>\n row.fiscal_year == data_last_year &&\n lit_people.includes(row.matched_name) &&\n row.position != \"IT DIRECTOR\"\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2753,"value":"Inputs.table(LIT_Rows)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2198,"value":"senior_application_developers = matched_data\n .filter(\n (row) =>\n row.fiscal_year == data_last_year &&\n [\"SENIOR APPLICATION DEVELOPER\"].includes(row.position)\n )\n .sort((a, b) => a.total_salary_paid - b.total_salary_paid)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2205,"value":"lead_application_developers = matched_data.filter(\n (row) =>\n row.fiscal_year == data_last_year &&\n [\"LEAD APPLICATION DEVELOPER\"].includes(row.position)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2202,"value":"Inputs.table(senior_application_developers)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2712,"value":"Inputs.table(lead_application_developers)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2668,"value":"Men not in HCIS","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":2651,"value":"Inputs.table(\n senior_application_developers\n .filter(\n (d) => d.gender == \"M\" && typeof d.agency_institution == \"undefined\"\n )\n .sort((a, b) => parseInt(b.base_salary) - parseInt(a.base_salary))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2193,"value":"Inputs.table(ris_developers)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1938,"value":"Inputs.table(women_app_dev, { select: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2021,"value":"format_dollar = {\n const format = d3.format(\"$\");\n return x => format(x / 1000);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2487,"value":"Inputs.table(it_directors)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2481,"value":"it_directors = matched_data.filter(\n (row) =>\n row.fiscal_year == data_last_year &&\n [\"IT DIRECTOR\", \"SENIOR IT DIRECTOR\"].includes(row.position) &&\n row.agency_institution != \"IOWA HEALTH CARE\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2768,"value":"senior_IT_director_salaries = it_directors\n .filter((r) => r.position === \"SENIOR IT DIRECTOR\")\n .map((r) => parseFloat(r.base_salary.replace(\",\", \"\")))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2780,"value":"avg_senior_dir_salary = Math.sumPrecise(senior_IT_director_salaries) /\n senior_IT_director_salaries.length","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2787,"value":"{\n return [143_000 * 1.025, 143_000 * 1.025 * 1.1];\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2579,"value":"library = matched_data.filter(\n (row) =>\n row.fiscal_year == data_last_year &&\n //[\"ASSOCIATE UNIVERSITY LIBRARIAN\"].includes(row.position)\n row.position.includes(\"LIBRAR\")\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2595,"value":"Inputs.table(library, { format: { base_salary: (x) => parseFloat(x) } })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2612,"value":"baurs = matched_data.filter(row => {return row.name.includes('BAUR')})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2762,"value":"matched_data.filter((row) => row.name.includes(\"KHALASTCHI, DANIEL ELY\"))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2277,"value":"position_list.filter((d) => d.match(/app/i))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2297,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2037,"value":"seniors_and_leads = matched_data.filter(row => row.fiscal_year == data_last_year && ['SENIOR APPLICATION DEVELOPER','LEAD APPLICATION DEVELOPER'].includes(row.position))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2049,"value":"make_dollar = (value) =>\n typeof value == \"number\" ? value : parseFloat(value.replace(\",\", \"\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2039,"value":"count_of_seniors =({ below_25: seniors_and_leads.filter(row => make_dollar(row.base_salary) < 88200.00).map(row => make_dollar(row.base_salary)), \n below_50: seniors_and_leads.filter(row => make_dollar(row.base_salary) >= 88200.00 && make_dollar(row.base_salary) < 104860.00).map(row => make_dollar(row.base_salary)), \n below_75: seniors_and_leads.filter(row => make_dollar(row.base_salary) >= 104860.00 && make_dollar(row.base_salary) < 126175.00).map(row => make_dollar(row.base_salary)), \n above_75: seniors_and_leads.filter(row => make_dollar(row.base_salary) >= 126175.00 ).map(row => make_dollar(row.base_salary)), \n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2077,"value":"total_salary_seniors =({ below_25: seniors_and_leads.filter(row => make_dollar(row.total_salary_paid) < 88200.00).map(row => make_dollar(row.total_salary_paid)), \n below_50: seniors_and_leads.filter(row => make_dollar(row.total_salary_paid) >= 88200.00 && make_dollar(row.total_salary_paid) < 104860.00).map(row => make_dollar(row.total_salary_paid)), \n below_75: seniors_and_leads.filter(row => make_dollar(row.total_salary_paid) >= 104860.00 && make_dollar(row.total_salary_paid) < 126175.00).map(row => make_dollar(row.total_salary_paid)), \n above_75: seniors_and_leads.filter(row => make_dollar(row.total_salary_paid) >= 126175.00 ).map(row => make_dollar(row.total_salary_paid)), \n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2058,"value":"just_counts = [114, 59,19,1]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2070,"value":"just_counts.map(row => Math.round(row / seniors_and_leads.length * 10000.00) / 100)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":505,"value":"last_years_only = matched_data.filter(row => row.fiscal_year === row.last_year && row.fiscal_year != data_last_year && row.fiscal_year != data_first_year)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":493,"value":"max_years = {\n let new_data = [];\n data.forEach(row => {\n const name_key = name_matches[fix_comma(row.name)]\n if (!new_data.hasOwnProperty(name_key)) {\n new_data[name_key] = row.fiscal_year;\n } else {\n if (new_data[name_key] < row.fiscal_year) {\n new_data[name_key] = row.fiscal_year;\n }\n }\n })\n return new_data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1859,"value":"min_years = {\n let new_data = [];\n data.forEach(row => {\n const name_key = name_matches[fix_comma(row.name)]\n if (!new_data.hasOwnProperty(name_key)) {\n new_data[name_key] = row.fiscal_year;\n } else {\n if (new_data[name_key] > row.fiscal_year) {\n new_data[name_key] = row.fiscal_year;\n }\n }\n })\n return new_data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":235,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2304,"value":"it_pay_gaps = {\n return it_classifications.map((d) => ({\n position: d,\n pay_gap: positions[d][data_last_year].male_female_pay_gap,\n sds: positions[d][data_last_year].male_female_gap_sds\n }));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2407,"value":"it_out_of_sd = {\n let all_rows = [];\n it_classifications.forEach((d) => {\n positions[d][data_last_year].male_outside_of_one_sd.forEach((m) =>\n all_rows.push({\n position: d,\n gender: \"m\",\n salary: m,\n average: positions[d][data_last_year].average_salary\n })\n );\n positions[d][data_last_year].female_outside_of_one_sd.forEach((f) =>\n all_rows.push({\n position: d,\n gender: \"f\",\n salary: f,\n average: positions[d][data_last_year].average_salary\n })\n );\n });\n\n return all_rows;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2369,"value":"positions[\"SENIOR SYSTEMS ARCHITECT\"]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":847,"value":"unique_matched_names = [...(new Set(matched_data.map(row => row.matched_name)))].sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":741,"value":"position_list = Object.keys(positions)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":602,"value":"app_dev_positions = ['Application Programmer/Analyst','Application Developer','Senior Application Developer', 'Lead Application Developer','Senior Application Architect'].map(d => d.toUpperCase())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2714,"value":"position_list.filter((d) => d.match(/BUSI/i))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2718,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1438,"value":"unique_agency = [...new Set(current_year_PAI_employees.map(d => d.agency_institution))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":995,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":62,"value":"unique_names = [...(new Set(data.map(row => row.name).map(name => name = fix_comma(name))))].sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":120,"value":"name_matches = {\n let matches = []\n unique_names.forEach((name, index) => {\n matches[name] = find_match(name, index)\n })\n return matches\n}\n ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1072,"value":"recent_high_salaries = matched_data.filter(row => (parseInt(row.fiscal_year) === (new Date()).getFullYear() - 1) && (parseFloat(row.total_salary_paid) > high_salary_minimum))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1083,"value":"high_salary_count = recent_high_salaries.length","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1086,"value":"high_salary_total = {\n let total = 0\n recent_high_salaries.forEach(row => total += parseFloat(row.total_salary_paid))\n return Math.round(((total + Number.EPSILON) * 100), 2) / 100\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1095,"value":"high_base_salary = high_salary_count * high_salary_minimum","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1110,"value":"excess_high_salary = high_salary_total - high_base_salary","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1133,"value":"average_loss = Math.round(excess_high_salary / high_salary_count * 100,2) / 100","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1102,"value":"desired_minimum_salary = 70000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1098,"value":"recent_low_salaries = matched_data.filter(row => (parseInt(row.fiscal_year) === (new Date()).getFullYear() - 1) && (parseFloat(row.total_salary_paid) < desired_minimum_salary))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1166,"value":"low_salaries_count = recent_low_salaries.length","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1169,"value":"low_salary_total = {\n let total = 0\n recent_low_salaries.forEach(row => total += parseFloat(row.total_salary_paid))\n return Math.round(((total + Number.EPSILON) * 100), 2) / 100\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1212,"value":"average_low_salary = low_salary_total / low_salaries_count","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1172,"value":"low_salary_goal = low_salaries_count * desired_minimum_salary","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1183,"value":"adjusted_low_salary = (excess_high_salary + low_salary_total)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1113,"value":"salary_adjustments_required = {\n let required = 0;\n recent_low_salaries.forEach(row => required = required + (desired_minimum_salary -parseFloat(row.total_salary_paid)))\n return Math.round((required + Number.EPSILON) * 100, 2) / 100 \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1116,"value":"pay_difference = Math.round((excess_high_salary - salary_adjustments_required + Number.EPSILON) * 100, 2) / 100","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1162,"value":"balance_data = [\n {\n group: 'High',\n label: 'Current', \n amount: high_salary_total,\n count: high_salary_count\n },\n {\n group: 'High',\n label: 'Required',\n amount: high_base_salary,\n count: high_salary_count\n },\n {\n group: 'Low',\n label: 'Current',\n amount: low_salary_total,\n count: low_salaries_count\n },\n {\n group: 'Low',\n label: 'Required',\n amount: low_salary_goal,\n count: low_salaries_count,\n },\n {\n group: 'Low',\n label: 'Adjusted',\n amount: adjusted_low_salary,\n count: low_salaries_count\n }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1293,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1485,"value":"salary_fields_and_labels = [\n ['total_salary_paid','Total Salary Paid'],\n ['parsed_base_salary','Base Salary'],\n ['log_total_salary','Log of Total Salary'],\n ['log_base_salary','Log of Base Salary']]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1487,"value":"test_fields_and_labels=[\n ['position_level','Progression in PAI rankings'],\n ['gender_level','Gender (male = 1)'],\n ['is_psych','Psych Hospital Developer (yes = 1)'],\n ['is_hygienic','Hygenics Lab Developer (yes = 1)'],\n ['unknown_agency','Agency Not Specified (yes = 1)'],\n ['years_in_dataset', 'Number of Years in Dataset']\n ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1490,"value":"regression_table_data = {\n // we want a column for each of the salary_fields_and labels and a row for each test_fields_and_labels\n return test_fields_and_labels.map( tf => {\n let row = {\"Test Field\": tf[1]}\n salary_fields_and_labels.forEach(sf => {\n let results = regress(current_year_PAI_employees,sf[0],tf[0])\n let r_value = results.m\n let regressionLine = ss.linearRegressionLine(results)\n let r2 = ss.rSquared(make_regression_data(current_year_PAI_employees,sf[0],tf[0]),regressionLine)\n row[sf[0]] = r_value\n row[sf[0]+'_R²'] = r2\n })\n return row\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1737,"value":"regression_table_data_regression = {\n // we want a column for each of the salary_fields_and labels and a row for each test_fields_and_labels\n return test_fields_and_labels.map( tf => {\n let row = {\"Test Field\": tf[1]}\n salary_fields_and_labels.forEach(sf => {\n let result = regression_regress(current_year_PAI_employees,sf[0],tf[0])\n row[sf[0]] = result.equation[0]\n row[sf[0]+'_R²'] = result.r2\n })\n return row\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1882,"value":"other_regressions = {\n let new_data = []\n test_fields_and_labels.forEach(row => {\n let new_row = {'Test Field': row[1]}\n test_fields_and_labels.forEach(column => {\n new_row[column[0]] = d3.format('.2f')(regression_regress(current_year_PAI_employees,row[0],column[0]).r2)\n })\n new_data.push(new_row)\n })\n return new_data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1898,"value":"other_formatters = {\n let base = {}\n test_fields_and_labels.forEach(sf => base[sf[0]] = {title: sf[1], formatter: heat_formatter })\n return base\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1903,"value":"other_regression_options = ({columns: {...other_formatters}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1894,"value":"heat_formatter = (val, i) => {\n let opacity = d3.scaleLinear()\n .domain([0, 1])\n .range([0,0.5]);\n return html`${val}`;\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1833,"value":"multiple_regression_data = make_multiple_regression_data('log_base_salary')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1829,"value":"make_multiple_regression_data = (salary_field) => {\n let rows = current_year_PAI_employees.map(d => {\n let row = []\n test_fields_and_labels.forEach(field => row.push(d[field[0]]))\n row.push(d[salary_field])\n return row\n })\n return rows\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1854,"value":"simple_multiple_regression_data = current_year_PAI_employees.map(d => [d.position_level, d.is_psych, d.log_base_salary])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1856,"value":"simple_model = multiple_regression.fit(simple_multiple_regression_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1843,"value":"multiple_regression = new jsregression.LinearRegression({\n alpha: 0.001, // \n iterations: 300,\n lambda: 0.0\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1851,"value":"model = multiple_regression.fit(multiple_regression_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1923,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1827,"value":"md`## sandbox`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1079,"value":"viewof high_salary_minimum = slider({\n min: desired_minimum_salary,\n max: desired_minimum_salary * 15,\n step: 5000,\n value: 200000,\n format: \",\",\n description:\n `${desired_minimum_salary} to ${desired_minimum_salary * 15}, in steps of five thousand, formatted as a (US) number`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1191,"value":"{\n const salary = generate_balance_plot('amount', 'Salary');\n const count = generate_balance_plot('count', 'Staff Count');\n return vl.hconcat(salary,count).render();\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1148,"value":"generate_balance_plot = (field, title) => {\n const chart = vl.markBar({filled: true})\n .width((width - 250)/2)\n .height(450)\n .data(balance_data)\n .encode(\n vl.x().fieldO('group').axis({title: 'Salary Group'}),\n vl.y().fieldQ(field).stack(null).axis({title: title}),\n vl.color().fieldN('label'),\n vl.opacity().value(.8),\n vl.tooltip([field])\n );\n \n return chart;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1130,"value":"met_need = pay_difference > 0","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":799,"value":"md`### Support functions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":70,"value":"fix_comma = (name => {\n if (name.match(/[A-z],[A-z]/)){\n return name.split(',').join(', ') \n } else {\n return name\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":132,"value":"find_match = (name_to_find, name_to_find_index) => {\n const name_parts = name_to_find.split(' ')\n const name_initial = name_parts[name_parts.length-1]\n const is_initial = name_initial.length === 1\n const next_name = is_initial ? (unique_names.length > name_to_find_index ? unique_names[name_to_find_index +1] : name_to_find) : name;\n const next_match = match_name(name_to_find, next_name)\n// return [name_initial, is_initial, next_name, next_match]\n if (!is_initial) {\n return name_to_find;\n } else {\n // is the next name the same?\n if (next_match) {\n return next_name;\n } else {\n return name_to_find;\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":97,"value":"match_name = (name,name_to_check) => {\n const match_first_part =name_to_check.substring(0,name.length) === name;\n const name_parts = name.split(' ')\n const name_to_check_parts = name_to_check.split(' ')\n const name_initial = name_parts[name_parts.length-1]\n const check_last = name_to_check_parts[name_to_check_parts.length-1]\n const is_initial = name_initial.length === 1\n const match_initial = check_last.substring(0,1) === name_initial\n return match_first_part && is_initial && match_initial\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":273,"value":"compa = (salary, position, year) => \n{\n return Math.floor((parseFloat(salary) / positions[position][year].midpoint) * 10000)/100\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":457,"value":"percent_of_staff = (position, year) => \n{\n return ({percent_male: positions[position][year].percent_male, \n percent_female: positions[position][year].percent_female,\n percent_undisclosed: positions[position][year].percent_undisclosed})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":958,"value":"generate_boxplot = (classifications, field, title, columns, split_by) => {\n const boxplot = vl\n .markBoxplot()\n .width((width - 220) / (split_by ? split_by : 1))\n .height(450)\n .data(matched_data)\n .transform(\n vl.filter({ field: \"position\", oneOf: classifications }),\n vl\n .calculate(\n `datum.gender === 'F' ? datum.fiscal_year + '-F' : (datum.gender === 'U' ? datum.fiscal_year + '-U' : datum.fiscal_year + '-M')`\n )\n .as(\"xoffset\")\n )\n .encode(\n vl.column().fieldN(columns),\n vl.x().fieldO(\"xoffset\").axis({ title: \"Year and Gender\" }),\n vl.y().fieldQ(field).axis({ title: title }),\n vl.color().fieldN(\"gender\")\n );\n return boxplot;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2121,"value":"generate_diff_plot = (classifications, field, title, columns, split_by) => {\n const diffplot = vl.markArea()\n .width((width - 220)/(split_by ? split_by : 1))\n .height(450)\n .data(matched_data)\n .transform(\n vl.filter({field: 'position', oneOf: classifications}),\n vl.calculate(`datum.compa - 100`).as('diff')\n )\n .encode(\n vl.column().fieldN(columns),\n vl.x().fieldO('fiscal_year').axis({title: 'Year'}),\n vl.y().median('diff').axis({title: title}).stack(false),\n vl.opacity().value(.8),\n vl.color().fieldN('gender')\n );\n return diffplot;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2124,"value":"generate_diff_plot(filtered_positions, 'compa', 'Compensation Ratio','position', filtered_positions.length).render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":983,"value":"generate_person_plot = (classifications, field, title, columns, split_by) => {\n const person_plot = vl.markPoint()\n .width((width - 220)/(split_by ? split_by : 1))\n .height(450)\n .data(matched_data)\n .transform(\n vl.filter({field: 'position', oneOf: classifications}),\n vl.calculate(`datum.gender === 'F' ? datum.fiscal_year + '-F' : (datum.gender === 'U' ? datum.fiscal_year + '-U' : datum.fiscal_year + '-M')`).as('xoffset'),\n )\n .encode(\n vl.column().fieldN(columns),\n vl.x().fieldO('xoffset').axis({title: 'Year and Gender'}),\n vl.y().fieldQ(field).axis({title: title}),\n vl.opacity().if({test: `datum.matched_name === '${single_person}'`}, vl.value(1)).value(0.3),\n vl.size().if({test: `datum.matched_name === '${single_person}'`}, vl.value(180)).value(10),\n vl.color().fieldN('gender'),\n vl.tooltip([ { field: 'matched_name', type: 'nominal', title: 'Name'},\n { field: field, type: 'quantitative', title: title }\n ]\n )\n );\n \n return person_plot\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1025,"value":"generate_bar_plot = (classifications, field, title, columns, split_by) => { \n const chart = vl.markBar({filled: true})\n .width((width - 200)/(split_by ? split_by : 1))\n .height(450)\n .data(position_summary)\n .transform(\n vl.filter({field: 'position', oneOf: classifications}),\n vl.filter({field: 'count', gt: 0}),\n vl.calculate(`datum.gender === 'F' ? datum.fiscal_year + '-F' : datum.fiscal_year + '-M'`).as('xoffset'),\n vl.calculate(`datum.gender === 'F' ? datum.year + '-F' : (datum.gender === 'U' ? datum.year + '-U' : datum.year + '-M')`).as('xoffset')\n )\n .encode(\n vl.column().fieldN(columns),\n vl.x().fieldO('xoffset').axis({title: 'Year and Gender'}),\n vl.y().fieldQ(field).axis({title: title}),\n vl.color().fieldN('gender'),\n vl.tooltip().fieldQ(field).title(vl.field(title))\n );\n \n return chart;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1306,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1477,"value":"make_regression_data = (data, first_field, second_field) => data.map(d => [parseFloat(d[first_field]),parseFloat(d[second_field])])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1482,"value":"regress = (data, first_field, second_field) => ss.linearRegression(make_regression_data(data, first_field, second_field))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1735,"value":"regression_regress = (data, first_field, second_field) => regression.linear(make_regression_data(data, first_field, second_field))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1514,"value":"regression_table_options = ({columns: {...regression_formatters}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1499,"value":"regression_formatters = {\n let base = {}\n salary_fields_and_labels.forEach(sf => base[sf[0]] = {title: sf[1], formatter(val,i) { return d3.format('.2f')(val)}})\n salary_fields_and_labels.forEach(sf => base[sf[0]+ '_R²'] = {title: sf[1] + ' R²', formatter(val,i) { return d3.format('.2f')(val)}})\n return base\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2164,"value":"find_person = (name) => {\n return unique_matched_names.filter((p) => p.match(name));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2174,"value":"ris_people = [\n \"DOMINY, PATRICK ALAN\",\n \"GEERTS, RYAN\",\n \"KATEBA, INNOCENT MBULANG'HULU\",\n \"GOLD, RACHEL M\",\n \"YAWALKAR, SACHIN\",\n \"KANE, MICHAEL EDWARDS\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2721,"value":"lit_people = [\n \"ADAMS JONES, RUTH ELLEN\",\n \"HOLLAND, ANDREW S\",\n \"CLARK, CHRISTOPHER JOHN\",\n \"THOMPSON, BRIAN JAMES\",\n \"DOERSHUK, NOAH J\",\n \"CLINKENBEARD, KENNETH MICHAEL\",\n \"HIGGINS-FREESE, ERIC JON\",\n \"BROWN, WILL T\"\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2573,"value":"find_person(\"KHALASTCHI\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2168,"value":"find_person(\"KANE\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":830,"value":"md`### Imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":31,"value":"vl = {\n const [vega, vegalite, api, tooltip] = await Promise.all([\n 'vega@5',\n 'vega-lite@4.14',\n 'vega-lite-api@0.0.15',\n 'vega-tooltip@0.18.1'\n ].map(module => require(module)));\n\n const options = {\n config: {\n // vega-lite default configuration\n config: {\n view: {width: 400, height: 300},\n mark: {tooltip: null}\n }\n },\n init: view => {\n // initialize tooltip handler\n view.tooltip(new tooltip.Handler().call);\n // enable horizontal scrolling for large plots\n if (view.container()) view.container().style['overflow-x'] = 'auto';\n },\n view: {\n // view constructor options\n loader: vega.loader({baseURL: 'https://vega.github.io/vega-datasets/'}),\n renderer: 'canvas'\n }\n };\n \n return api.register(vega, vegalite, options);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":735,"value":"import {select} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":841,"value":"import {autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1123,"value":"import {slider} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1496,"value":"import {table} from \"@tmcw/tables@513\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1467,"value":"ss = require('simple-statistics')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1507,"value":"d3 = require('d3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1867,"value":"d3Array = require('d3-array')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1730,"value":"regression = require('regression')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1840,"value":"jsregression = require('https://bundle.run/js-regression@1.0.10')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 30:"$2f:body"