2b:null 2c:["$","title",null,{"children":"University of Iowa IT Salaries Update | Jose Jimenez | Observable"}] 31:T5c2,{ const bars = vl.markBar() .data(filtered_from_all) .encode(vl.x().fieldQ('Salary1Corrected').aggregate('mean').stack(null).axis({title: 'Average Salary'}), vl.y().fieldN('Class'), vl.color().fieldN('Gender'), vl.size(vl.value(15)), vl.opacity(vl.value(0.2)) ); const circles = vl.markCircle() .data(filtered_from_all) .encode(vl.x().fieldQ('Salary1Corrected').axis({title: 'Individual Salaries'}), vl.y().fieldN('Class'), vl.color().fieldN('Gender'), vl.size(vl.value(75)), vl.opacity(vl.value(0.7)) ); const rules = vl.markRule() .data(filtered_from_all) .encode( vl.x().fieldQ('Salary1Corrected').aggregate('mean').stack(null), vl.color().fieldN('Gender'), vl.size(vl.value(3)) ); const q1 = vl.markRule() .data(filtered_from_all) .encode( vl.x().fieldQ('Salary1Corrected').aggregate('q1').stack(null), vl.color().fieldN('Gender'), vl.size(vl.value(3)), vl.opacity(vl.value(0.5)), vl.tooltip(vl.value('end of first Quartile')) ); const q3 = vl.markRule() .data(filtered_from_all) .encode( vl.x().fieldQ('Salary1Corrected').aggregate('q3').stack(null), vl.color().fieldN('Gender'), vl.size(vl.value(3)), vl.opacity(vl.value(0.4)), vl.tooltip(vl.value('end of third Quartile')) ); const chart = vl.layer(circles,bars, rules) .width(width - 250) .height(800) return chart.render(); }32:T5ff,{ const women = vl.markPoint({filled: true}) .data(totals_by_class.filter(r => r.classification != 'GRAND TOTAL')) .transform(vl.calculate('"F staff earn an average of $" + round(datum.F_as_percent_of_M_average*100)/100 + " for each dollar earned by M staff classified as " + datum.classification + "."').as('Info')) .encode( vl.x().fieldQ('F_as_percent_of_M_average').scale({domain: [0.7,1.3]}).axis({title: 'Dollars per Dollar'}), vl.y().fieldN('classification').axis({title: 'Class'}), vl.color(vl.value('blue')), vl.size(vl.value(200)), vl.tooltip(['Info']) ); const men = vl.markPoint({filled: true}) .data(totals_by_class.filter(r => r.classification != 'GRAND TOTAL')) .transform(vl.calculate('1').as('base') ) .encode( vl.x().fieldQ('base'), vl.y().fieldN('classification'), vl.color(vl.value('orange')), vl.size(vl.value(200)) ); const lines = vl.markBar() .data(totals_by_class.filter(r => r.classification != 'GRAND TOTAL')) .transform(vl.calculate('1').as('base'), vl.calculate('datum.F_as_percent_of_M_average ? datum.F_as_percent_of_M_average : 1').as('line_to') ) .encode( vl.x().fieldQ('base').scale({domain: [0.7,1.3]}).axis({title: 'Dollars per Dollar'}), vl.x2().fieldQ('line_to'), vl.y().fieldN('classification'), vl.color(vl.value('purple')), vl.opacity(vl.value('0.2')), vl.size(vl.value(5)) ); return vl.layer(lines,women,men) .width(width-250) .height(600) .render() }33:T788,{ const classes = vl.markPoint({filled:true}) .data(isograph_data.filter(row => row.Class != 'GRAND TOTAL')) .width(width-250) .transform( vl.calculate('60').as('rows'), vl.calculate('ceil(datum.id/datum.rows)').as('row'), vl.calculate('datum.rows + (datum.id - (datum.row * datum.rows))').as('col') ) .title('Individual Classes') .encode( vl.shape(vl.value("M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 -0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 -0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 -0.6 -0.4 -0.6z")), vl.x().fieldO('col').axis(null).scale({padding: 1}), vl.y().fieldO('row').axis(null).scale({padding: 1}), vl.size(vl.value(25)), vl.color().fieldN('Gender').legend({orient: 'top'}), vl.row().fieldN('Class').header({labelAngle: 0, labelAnchor: 'start',labelPadding: 5, labelFontStyle: 'bold', labelOrient: 'right'}) ); const total = vl.markPoint({filled:true}) .data(isograph_data.filter(row => row.Class === 'GRAND TOTAL')) .width(width-250) .transform( vl.calculate('60').as('rows'), vl.calculate('ceil(datum.id/datum.rows)').as('row'), vl.calculate('datum.rows + (datum.id - (datum.row * datum.rows))').as('col') ) .title('All Classes') .encode( vl.shape(vl.value("M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 -0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 -0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 -0.6 -0.4 -0.6z")), vl.x().fieldO('col').axis(null).scale({padding: 1}), vl.y().fieldO('row').axis(null).scale({padding: 1}), vl.size(vl.value(25)), vl.color().fieldN('Gender'), ); return vl.vconcat(total, classes) .render(); }34:T405,table(totals_by_class, { style: 'compact', columns: { classification: { title: 'Job Classification' }, M_staff_count: { title: 'Male Staff Count', formatter: (val) => numeral(val).format('0,0') }, M_average_salary: { title: 'Male Average Salary', formatter: (val) => numeral(val).format('$0,0') }, F_staff_count: { title: 'Female Staff Count', formatter: (val) => numeral(val).format('0,0') }, F_average_salary: { title: 'Female Average Salary', formatter: (val) => numeral(val).format('$0,0') }, F_as_percent_of_M_average: { title: 'Female as % of Male (average salary)', formatter: (val) => val === null ? '-' : numeral(val).format('0.0%') }, Average_salary: { title: 'Average Salary', formatter: (val) => numeral(val).format('$0,0') }, F_as_percent_of_M_staff: { title: 'Female Staff as Percent of total_staff', formatter: (val) => numeral(val).format('0.0%') } } })35:T50b,{ const bars = vl.markBar() .data(filtered_raises) .transform( vl.filter({field: 'raise', gt: 0.0}) ) .encode(vl.x().fieldQ('percent_raise').aggregate('mean').stack(null).axis({title: 'Average Raise %'}), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.size(vl.value(15)), vl.opacity(vl.value(0.2)) ); const circles = vl.markPoint({filled: true}) .data(filtered_raises) .transform( vl.filter({field: 'raise', gt: 0}) ) .encode(vl.x().fieldQ('percent_raise').axis({title: 'Individual Raises %'}), vl.tooltip(['Gender','salary2017','salary2018', 'raise', 'family2017', 'family2018', 'level2017', 'level2018','Job Change Type', 'Class_2018']), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.shape().fieldN('job_change'), vl.size(vl.value(75)), vl.opacity(vl.value(0.7)) ); const rules = vl.markRule() .data(filtered_raises) .transform( vl.filter({field: 'raise', gt: 0.0}) ) .encode( vl.x().fieldQ('percent_raise').aggregate('mean').stack(null), vl.color().fieldN('Gender'), vl.size(vl.value(3)) ); const chart = vl.layer(bars,rules, circles) .width(width - 250) .height(800) return chart.render(); }36:T5bd,{ const bars = vl.markBar() .data(filtered_raises.filter(row => row['Job Change Type'] != 'Left job family')) .transform( vl.filter({field: 'raise', gt: 0.0}), ) .encode(vl.x().fieldQ('percent_raise').aggregate('mean').stack(null).axis({title: 'Average Raise %'}), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.size(vl.value(15)), vl.opacity(vl.value(0.2)) ); const circles = vl.markPoint({filled: true}) .data(filtered_raises.filter(row => row['Job Change Type'] != 'Left job family')) .transform( vl.filter({field: 'raise', gt: 0}) ) .encode(vl.x().fieldQ('percent_raise').axis({title: 'Individual Raises %'}), vl.tooltip(['Gender','salary2017','salary2018', 'raise', 'family2017', 'family2018', 'level2017', 'level2018','Job Change Type', 'Class_2018']), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.shape().fieldN('job_change'), vl.size(vl.value(75)), vl.opacity(vl.value(0.7)) ); const rules = vl.markRule() .data(filtered_raises.filter(row => row['Job Change Type'] != 'Left job family')) .transform( vl.filter({field: 'raise', gt: 0.0}) ) .encode( vl.x().fieldQ('percent_raise').aggregate('mean').stack(null), vl.color().fieldN('Gender'), vl.size(vl.value(3)) ); const chart = vl.layer(rules, bars,circles) .width(width - 250) .height(800) return chart.render(); }37:T4f2,{ const bars = vl.markBar() .data(filtered_raises) .transform( vl.filter({field: 'raise', gt: 0.0}) ) .encode(vl.x().fieldQ('raise').aggregate('mean').stack(null).axis({title: 'Average Raise $'}), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.size(vl.value(15)), vl.opacity(vl.value(0.2)) ); const circles = vl.markPoint({filled: true}) .data(filtered_raises) .transform( vl.filter({field: 'raise', gt: 0}) ) .encode(vl.x().fieldQ('raise').axis({title: 'Individual Raises $'}), vl.tooltip(['Gender','salary2017','salary2018', 'raise', 'family2017', 'family2018', 'level2017', 'level2018','Job Change Type', 'Class_2018']), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.shape().fieldN('job_change'), vl.size(vl.value(75)), vl.opacity(vl.value(0.7)) ); const rules = vl.markRule() .data(filtered_raises) .transform( vl.filter({field: 'raise', gt: 0.0}) ) .encode( vl.x().fieldQ('raise').aggregate('mean').stack(null), vl.color().fieldN('Gender'), vl.size(vl.value(3)) ); const chart = vl.layer(circles,bars,rules) .width(width - 250) .height(800) return chart.render(); }38:T5a3,{ const bars = vl.markBar() .data(filtered_raises.filter(row => row['Job Change Type'] != 'Left job family')) .transform( vl.filter({field: 'raise', gt: 0.0}) ) .encode(vl.x().fieldQ('raise').aggregate('mean').stack(null).axis({title: 'Average Raise $'}), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.size(vl.value(15)), vl.opacity(vl.value(0.2)) ); const circles = vl.markPoint({filled: true}) .data(filtered_raises.filter(row => row['Job Change Type'] != 'Left job family')) .transform( vl.filter({field: 'raise', gt: 0}) ) .encode(vl.x().fieldQ('raise').axis({title: 'Individual Raises $'}), vl.tooltip(['Gender','salary2017','salary2018', 'raise', 'family2017', 'family2018', 'level2017', 'level2018','Job Change Type', 'Class_2018']), vl.y().fieldN('Class_2017'), vl.color().fieldN('Gender'), vl.shape().fieldN('job_change'), vl.size(vl.value(75)), vl.opacity(vl.value(0.7)) ); const rules = vl.markRule() .data(filtered_raises.filter(row => row['Job Change Type'] != 'Left job family')) .transform( vl.filter({field: 'raise', gt: 0.0}) ) .encode( vl.x().fieldQ('raise').aggregate('mean').stack(null), vl.color().fieldN('Gender'), vl.size(vl.value(3)) ); const chart = vl.layer(circles,bars,rules) .width(width - 250) .height(800) return chart.render(); }39:T50c,{ const women = vl.markPoint({filled: true}) .data(raise_totals_filtered) .transform(vl.calculate('"F received an average of " + round(datum.f_percent_ratio*100)/100 + "% for raise each percent of raise for M staff classified as " + datum.class + "."').as('Info')) .encode( vl.x().fieldQ('f_percent_ratio').axis({title: 'Percent per Percent'}), vl.y().fieldN('class').axis({title: 'Class'}), vl.color(vl.value('blue')), vl.size(vl.value(200)), vl.tooltip(['Info']) ); const men = vl.markPoint({filled: true}) .data(raise_totals_filtered) .transform(vl.calculate('1').as('base') ) .encode( vl.x().fieldQ('base'), vl.y().fieldN('class'), vl.color(vl.value('orange')), vl.size(vl.value(200)), ); const lines = vl.markBar() .data(raise_totals_filtered) .transform(vl.calculate('1').as('base'), vl.calculate('datum.f_percent_ratio ? datum.f_percent_ratio : 1').as('line_to') ) .encode( vl.x().fieldQ('base').axis({title: 'Dollars per Dollar'}), vl.x2().fieldQ('line_to'), vl.y().fieldN('class'), vl.color(vl.value('purple')), vl.opacity(vl.value('0.2')), vl.size(vl.value(5)) ); return vl.layer(lines,women,men) .width(width-250) .height(600) .render() }3a:T504,{ const women = vl.markPoint({filled: true}) .data(raise_totals_filtered) .transform(vl.calculate('"F received an average of $" + round(datum.f_dollars_ratio*100)/100 + " for each dollar of raise for M staff classified as " + datum.class + "."').as('Info')) .encode( vl.x().fieldQ('f_dollars_ratio').axis({title: 'Percent per Percent'}), vl.y().fieldN('class').axis({title: 'Class'}), vl.color(vl.value('blue')), vl.size(vl.value(200)), vl.tooltip(['Info']) ); const men = vl.markPoint({filled: true}) .data(raise_totals_filtered) .transform(vl.calculate('1').as('base') ) .encode( vl.x().fieldQ('base'), vl.y().fieldN('class'), vl.color(vl.value('orange')), vl.size(vl.value(200)) ); const lines = vl.markBar() .data(raise_totals_filtered) .transform(vl.calculate('1').as('base'), vl.calculate('datum.f_dollars_ratio ? datum.f_dollars_ratio : 1').as('line_to') ) .encode( vl.x().fieldQ('base').axis({title: 'Dollars per Dollar'}), vl.x2().fieldQ('line_to'), vl.y().fieldN('class'), vl.color(vl.value('purple')), vl.opacity(vl.value('0.2')), vl.size(vl.value(5)) ); return vl.layer(lines,women,men) .width(width-250) .height(600) .render() }3b:Te98,md`## Source: [Iowa Legislature Salary Book](https://www.legis.iowa.gov/publications/fiscal/salaryBook) for fiscal year 2017 and 2018. This data was filtered for the departments "University of Iowa", "University of Iowa Center for D & D", and "University General Hospital" only. Data was extracted on July 22nd, 2019. The only post-processing is to remove commas from thew two Salary fields. Other filtering is accomplished within this page. The other data source is the [Professional and Scientific Job Classifications page](https://hris.uiowa.edu/CC_Redesign/driver.php?ACTION=HOME) of the University of Iowa Human Resources site. This was also extracted on the same day. Salary data is made public yearly for the prior fiscal year. Fiscal year is described as: "Fiscal Year is July 1 - June 30 of the following year." ### Fields used in these visualizations: * Year -- The fiscal year of the salary. * Class -- Classification of individual job holders * Salary1 -- "Salary 1 represents the employees base pay at the end of the fiscal year. A "BW" represents the employees biweekly base pay." * Agency -- Labeled Department on the salarybook site, indicates the State of Iowa Agency the individual is employed at. One of: * University of Iowa -- interpreted to mean employees of the University of Iowa, not including UIHC or Center for Disabilities and Development. * University General Hospital -- interpreted to mean employees of the University of Iowa Hospitals and Clinics * University of Iowa Center for D & D -- interpreted to mean employees of the University of Iowa Center for Disabilities and Development. * Gender -- three genders appear in the salarybook * M -- interpreted as Male * F -- interpreted as Female * U -- interpreted as Unknown / Unspecified -- not present in IT classifications selected ### Caveats * This set of visualizations is based entirely on public data as listed in the Source above. * It is known that the salary numbers being graphed are not all for full time staff. * This would bring the mean salary down based on individuals that do not work full time. * ${filter_low_salaries ? 'Salaries below the annual minimum full time salary (' + numeral(minimum_wage_salary).format('$0,0') + ') are currently being filtered out.' : 'These are currently included in the visualizations.'} * [Filtering or not filtering these salaries out is controlled below](#filter_low_salaries). * The data does not identify job families, but some can be inferred by the Class field. No attempt has been made to group or sort by job family. * Employees in the dataset work in ${unfiltered_location_counts.length} different locations: * ${unfiltered_location_counts.map(r => r.key).join(', ')} * ${location_counts.length === unfiltered_location_counts ? 'All locations are currently shown in these visualizations.' : 'The following ' + location_counts.length + ' locations are shown in these visualizations: ' + location_counts.map(r => r.key).join(', ')} * [Filtering to Johnson County employees only is controlled elsewhere](#filter_only_johnson). * Job classifications from the full dataset were manually filtered down based on the information on the UIowa HR page referenced. * There are 3 departments in the dataset, but mean salaries may be different for each. * The mean is used as a generalized way to compare salaries, but the dataset does not include information that is relevant to the salary for each employee: * seniority within the classification * Employees who recently became Senior Application Developers may justifiably earn less than employees who have been Senior Application Developers for a longer period of time if there has not been bias in promotions. `3c:Ta16,raises = { const summary = [] at_least_two_years.forEach( person => { const y2017 = person.values.filter(row => row.Year === '2017')[0]; const y2018 = person.values.filter(row => row.Year === '2018')[0]; const y2017Family = job_families.filter(family => family.class === y2017.Class.toUpperCase())[0]; const y2018Family = job_families.filter(family => family.class === y2018.Class.toUpperCase())[0]; const y2017FamilyName = y2017Family ? y2017Family.family : ''; const y2018FamilyName = y2018Family ? y2018Family.family : ''; const level2017 = y2017Family ? parseInt(y2017Family.job_code.slice(-1)) : 0; const level2018 = y2018Family ? parseInt(y2018Family.job_code.slice(-1)) : 0; const found_families = y2017Family && y2018Family; const no_job_change = y2017.Class === y2018.Class; const promotion_in_family = found_families && (y2017FamilyName === y2018FamilyName) && (level2018 > level2017); let job_change_type = 'Unknown'; if (no_job_change) { job_change_type = 'None'; } else if (found_families && promotion_in_family) { job_change_type = 'Promotion in job family'; } else if (found_families && !promotion_in_family) { job_change_type = 'Left job family'; } else if (y2017Family) { job_change_type = 'Left job family'; } if (y2017 && y2018) { const salary2017 = parseFloat((y2017.Salary1.split(',').join(''))); const salary2018 = parseFloat((y2018.Salary1.split(',').join(''))); const raise = salary2018 - salary2017; const job_change = (y2017.Class != y2018.Class) ? 'Changed' : 'None'; let percent_raise = raise / salary2017 * 100; if (raise === 0 || salary2017 === 0) { percent_raise = 0}; const base = { Name: y2017.Name, Gender: y2017.Gender, Agency: y2017.Agency, "City/County": y2017['City/County'], Class_2017: y2017.Class, Class_2018: y2018.Class, family2017: y2017FamilyName, family2018: y2018FamilyName, level2017: level2017, level2018: level2018, salary2017: salary2017, salary2018: salary2018, raise: raise, percent_raise: parseFloat(Math.floor(percent_raise * 100.0)/100.0), 'Job Change Type': job_change_type, job_change: job_change }; summary.push(base); } }) return summary } 3d:T47e,calculate_raise_totals = (from_data) => d3.nest() .key(raise => raise.Class_2017) .rollup((leaves) => ({percent_f: (d3.mean(leaves.filter(leaf => leaf.Gender ==='F'), leaf => leaf.percent_raise)), percent_m: (d3.mean(leaves.filter(leaf => leaf.Gender ==='M'), leaf => leaf.percent_raise)), dollars_f: (d3.mean(leaves.filter(leaf => leaf.Gender ==='F'), leaf => leaf.raise)), dollars_m: (d3.mean(leaves.filter(leaf => leaf.Gender ==='M'), leaf => leaf.raise)), })) .entries(from_data) .map(klass => { klass.value.f_percent_ratio = Math.floor(klass.value.percent_f / klass.value.percent_m * 100)/100; klass.value.f_dollars_ratio = Math.floor(klass.value.dollars_f / klass.value.dollars_m * 100)/100; klass.value.percent_f = Math.floor(klass.value.percent_f * 100)/100; klass.value.percent_m = Math.floor(klass.value.percent_m * 100)/100; klass.value.dollars_f = Math.floor(klass.value.dollars_f * 100)/100; klass.value.dollars_m = Math.floor(klass.value.dollars_m * 100)/100; return Object.assign({class: klass.key},klass.value); })3e:T76c,job_families = d3.tsvParse(`family class job_code pay_level PIA APPLICATION DEVELOPER PIA2 Level 4B PIA APPLICATION PROGRAMMER/ANALYST PIA1 Level 3B PIA LEAD APPLICATION DEVELOPER PIA4 Level 6B PIA SENIOR APPLICATION ARCHITECT PIA5 Level 7B PIA SENIOR APPLICATION DEVELOPER PIA3 Level 5B PIB ASSOCIATE COMMUNICATIONS INFRASTRUCTURE ENGINEER PIB1 Level 2B PIB COMMUNICATIONS INFRASTRUCTURE ARCHITECT PIB4 Level 5B PIB COMMUNICATIONS INFRASTRUCTURE ENGINEER PIB2 Level 3B PIB SENIOR COMMUNICATIONS INFRASTRUCTURE ENGINEER PIB3 Level 4B PIC IT SUPPORT ANALYST PIC1 Level 3A PIC IT SUPPORT CONSULTANT PIC2 Level 4A PIC LEAD IT SUPPORT CONSULTANT PIC4 Level 5B PIC SENIOR IT SUPPORT CONSULTANT PIC3 Level 5A PID DATA ANALYST PID1 Level 3BDual career track with PID2 PID DATABASE ADMINISTRATOR PID2 Level 3BDual career track with PID1 PID DATABASE ARCHITECT PID5 Level 6B PID LEAD DATABASE ADMINISTRATOR PID4 Level 5B PID SENIOR DATABASE ADMINISTRATOR PID3 Level 4B PID SENIOR DATABASE ARCHITECT PID6 Level 7B PIE IT SECURITY ANALYST PIE1 Level 3B PIE IT SECURITY ARCHITECT PIE3 Level 5B PIE SECURITY OPERATIONS DIRECTOR PIE4 Level 6B PIE SENIOR IT SECURITY ANALYST PIE2 Level 4B PIE SENIOR IT SECURITY OFFICER PIE5 Level 7B PIF SENIOR SYSTEMS ADMINISTRATOR PIF3 Level 5B PIF SENIOR SYSTEMS ARCHITECT PIF5 Level 7B PIF SYSTEMS ADMINISTRATOR PIF2 Level 4B PIF SYSTEMS ANALYST PIF1 Level 3B PIF SYSTEMS ARCHITECT PIF4 Level 6B PIM IT DIRECTOR PIM2 Level 6B PIM IT MANAGER PIM1 Level 5B PIM SENIOR IT DIRECTOR PIM3 Level 7B PIN ASSOCIATE NETWORK ENGINEER PIN1 Level 3B PIN NETWORK ARCHITECT PIN4 Level 6B PIN NETWORK ENGINEER PIN2 Level 4B PIN SENIOR NETWORK ENGINEER PIN3 Level 5B PIX BUSINESS INTELLIGENCE ANALYST PIX1 Level 3B PIX BUSINESS INTELLIGENCE ARCHITECT PIX3 Level 5B PIX DATA SCIENTIST PIX4 Level 6B PIX SENIOR BUSINESS INTELLIGENCE ANALYST PIX2 Level 4B PIX SENIOR DATA SCIENTIST PIX5 Level 7B`)3f:Tc2d,totals_by_class = { const totals = d3.nest() .key(r => r.Class) .key(r => r.Gender) .rollup(function(leaves) { return {"individual_count": leaves.length, "total_salary": d3.sum(leaves, function(d) {return parseFloat(d.Salary1Corrected);})} }) .entries(filtered_from_all); const rows = []; const allClass = {classification: 'GRAND TOTAL', M_total_salary: 0.0, M_staff_count: 0, F_total_salary: 0.0, F_staff_count: 0, total_salary: 0.0, staff_count: 0}; totals.forEach(row => { const Class = { classification: row.key }; let total_salary = 0; let total_staff = 0; row.values.forEach( gender => { total_salary += gender.value.total_salary; total_staff += gender.value.individual_count; Class[`${gender.key}_total_salary`] = gender.value.total_salary; Class[`${gender.key}_staff_count`] = gender.value.individual_count; Class[`${gender.key}_average_salary`] = Math.floor(gender.value.total_salary / gender.value.individual_count * 100)/100; allClass[`${gender.key}_total_salary`] += gender.value.total_salary; allClass[`${gender.key}_staff_count`] += gender.value.individual_count; }); if (!Class[`M_total_salary`]){ Class['M_total_salary'] = 0; Class['M_staff_count'] = 0; Class['M_average_salary'] = 0; } if (!Class[`F_total_salary`]){ Class['F_total_salary'] = 0; Class['F_staff_count'] = 0; Class['F_average_salary'] = 0; } Class.total_salary = total_salary; Class.staff_count = total_staff; Class.F_as_percent_of_M_average = null; allClass.total_salary += total_salary; allClass.staff_count += total_staff; if (Class.F_average_salary > 0 && Class.M_average_salary > 0) { Class.F_as_percent_of_M_average = Math.floor(Class.F_average_salary / Class.M_average_salary * 10000) / 10000; } Class.F_as_percent_of_M_staff = 0; if (Class.M_staff_count && Class.F_staff_count) { Class.F_as_percent_of_M_staff = Math.floor(Class.F_staff_count / Class.staff_count * 10000) / 10000; } Class.Average_salary = Math.floor(total_salary / total_staff * 100) / 100; rows.push(Class); }); allClass.Average_salary = Math.floor(allClass.total_salary / allClass.staff_count * 100) / 100; allClass.M_average_salary = Math.floor(allClass.M_total_salary / allClass.M_staff_count * 100) / 100; allClass.F_average_salary = Math.floor(allClass.F_total_salary / allClass.F_staff_count * 100) / 100; allClass.F_as_percent_of_M_average = Math.floor(allClass.F_average_salary / allClass.M_average_salary * 10000) / 10000; allClass.F_as_percent_of_M_staff = Math.floor(allClass.F_staff_count / allClass.staff_count * 10000) / 10000; rows.push(allClass); const render_data = []; rows.forEach(row => { const new_row = Object.assign({}, row); delete(new_row.M_total_salary); delete(new_row.F_total_salary); delete(new_row.total_salary); render_data.push(new_row); }); return render_data; }40:T587,function input(config) { let {form, type = "text", attributes = {}, action, getValue, title, description, submit, options} = config; if (!form) form = html` `; const input = form.input; Object.keys(attributes).forEach(key => { const val = attributes[key]; if (val != null) input.setAttribute(key, val); }); if (submit) form.append(html``); form.append(html``); if (title) form.prepend(html`

${title}

`); if (description) form.append(html`

${description}

`); if (action) { action(form); } else { const verb = submit ? "onsubmit" : type == "button" ? "onclick" : type == "checkbox" || type == "radio" ? "onchange" : "oninput"; form[verb] = (e) => { e && e.preventDefault(); const value = getValue ? getValue(input) : input.value; if (form.output) form.output.value = value; form.value = value; if (verb != "oninput") form.dispatchEvent(new CustomEvent("input")); }; if (!submit) form.onsubmit = (e) => e && e.preventDefault(); form[verb](); } return form; }41:T6aa,function checkbox(config = {}) { let {value: formValue, title, description, submit, options, css_width, collapsible_label} = config; let collapsible_id = collapsible_label ? collapsible_label.replace( /[^a-zA-Z0-9]/ , "_").toLowerCase() : ''; if (Array.isArray(config)) options = config; options = options.map(o => typeof o === "string" ? {value: o, label: o} : o); let collapsible_header = collapsible_label ? `

${collapsible_label}

` : '' let collapsible_footer = collapsible_label ? `

` : '' const form = input({ type: "checkbox", title, description, submit, getValue: input => { if (input.length) return Array.prototype.filter.call(input, i => i.checked).map(i => i.value); return input.checked ? input.value : false; }, form: html` ` }); form.output.remove(); return form; }42:T6b4,collapsible_styles = html``43:[] 2f:{"id":"db33b41a6a9467df","slug":"university-of-iowa-it-salaries-update","title":"University of Iowa IT Salaries Update","thumbnail":"76b3fcd0867c632ead4cf8028e744b798dfc806f4f152eeb45230905c331649b","access_level":"public","create_time":"$D2019-07-21T02:31:46.579Z","update_time":"$D2020-02-27T22:57:59.576Z","view_time":null,"first_public_time":"$D2019-07-25T20:37:59.397Z","fork":{"id":"09b260325d8aa2a7","slug":"university-of-iowa-it-salaries","title":"University of Iowa IT Salaries","owner":{"id":"11eff763c21b0530","login":"jjimenez","name":"Jose Jimenez"}},"stars":0,"starred":false,"owner":{"type":"team","id":"11eff763c21b0530","login":"jjimenez","name":"Jose Jimenez","avatar_url":"https://avatars-next.observableusercontent.com/5d28ac3054c420a200dcd8a0bd3367e2263d2f168c4aa5eaa0c89640622d6088","tier":"starter_2024"},"version":2154,"publish_version":2154,"role":null,"authored":false,"authors":[{"type":"individual","id":"214adfe1a708eb31","login":"jjimenez","name":"Jose Jimenez","avatar_url":"https://avatars-next.observableusercontent.com/5d28ac3054c420a200dcd8a0bd3367e2263d2f168c4aa5eaa0c89640622d6088","tier":null}],"members":[{"type":"individual","id":"214adfe1a708eb31","login":"jjimenez","name":"Jose Jimenez","avatar_url":"https://avatars-next.observableusercontent.com/5d28ac3054c420a200dcd8a0bd3367e2263d2f168c4aa5eaa0c89640622d6088","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"University of Iowa IT Salaries Update","cells":[{"id":0,"value":"md`# University of Iowa IT Salaries Update\n\n(using vega lite api)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2093,"value":"md`## Select a year to view salaries for that year`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1428,"value":"viewof selected_year = select(years)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":154,"value":"md`## Salaries by Classification`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":476,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":11,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":156,"value":"md`This graph shows \n* individual circles for salaries in each classification colored by gender\n* Average (mean) salaries for each classification (bars). \n* vertical lines show the overall Average (mean) salaries across all classifications based on gender.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":628,"value":"md`## Settings\n\nThese settings impact all the visualizations on this page.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":231,"value":"viewof agencies_to_include = checkbox(\n {\n title: 'Departments to include',\n description: 'Select Departments to include',\n options: departments.map(r => ({value: r, label: r})),\n value: departments\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":408,"value":"viewof classes_to_include = checkbox(\n {\n title: 'Job Classifications to include',\n description: 'Selecting classifications limits the data displayed in all visualizations.',\n options: classes.map(r => ({value: r, label: r})),\n value: classes,\n submit: true,\n css_width: `${Math.floor(width / 4)}px`,\n collapsible_label: 'Job Classification Selections'\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":124,"value":"viewof filter_low_salaries = checkbox({\n description: `Check this box to see how filtering out Salaries below the $7.25 minimum full-time pay for a year of ${numeral(minimum_wage_salary).format('$0,0')} impacts the graphs. Individuals making less than this amount are likely to be engaged at the University of Iowa less than full time.`,\n options: [\n { value: 'true', label: 'Fiter Low Salaries Out' }\n ],\n value: 'false'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":185,"value":"viewof filter_only_johnson = checkbox({\n description: `Check this box to see how filtering to only individuals in Johnson and JOHNSON locations impacts the graphs. These individuals are assumed to be working in Johnson County, Iowa.`,\n options: [\n { value: 'true', label: 'Filter to only Johnson / JOHNSON locations' }\n ],\n value: 'false'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":157,"value":"md`## Average (mean) salaries only`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":473,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":83,"value":"vl.markBar()\n.data(filtered_from_all)\n.width((width-200)/4)\n.height(600)\n.encode(vl.x().fieldQ('Salary1Corrected').aggregate('mean').stack(null).axis({title: 'Average Salary'}),\n vl.y().fieldN('Class'),\n vl.color().fieldN('Gender'),\n vl.facet().fieldN('Agency'),\n vl.size(vl.value(15)),\n vl.opacity(vl.value(0.6))\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":160,"value":"md`This graph shows the Average (mean) salaries by gender for each classification`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":291,"value":"md`## Count of staff in given classifications`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":470,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1346,"value":"vl.markBar()\n.data(filtered_from_all)\n.width((width-200)/4)\n.height(600)\n.encode(vl.x().fieldQ('name').aggregate('count').stack(null).axis({title: 'Number of Records'}),\n vl.y().fieldN('Class'),\n vl.color().fieldN('Gender'),\n vl.facet().fieldN('Agency'),\n vl.size(vl.value(15)),\n vl.opacity(vl.value(0.6))\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":300,"value":"md`This graph shows the count of staff in each classification by gender.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":164,"value":"md`## Salary distribution by gender, regardless of classification`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":459,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1351,"value":"vl.markTick()\n.data(filtered_from_all)\n.width(width-300)\n.encode(vl.x().fieldQ('Salary1Corrected').axis({title: 'Salary'}),\n vl.y().fieldN('Gender'),\n vl.color(vl.fieldN('Gender')),\n vl.size(vl.value(100))\n )\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":166,"value":"md`This graph shows the salary distribution by gender.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":413,"value":"md`## Counts of individuals in a particular salary range by Gender for each Agency`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":447,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1361,"value":"vl.markPoint()\n.data(filtered_from_all)\n.width((width-250)/3)\n.encode(vl.x().fieldQ('Salary1Corrected').bin({nice: true, step: 20000 }).axis({title: 'Salary Binned'}),\n vl.y().fieldN('Name').aggregate('count').stack(null),\n vl.color(vl.fieldN('Gender')),\n vl.size().fieldN('Name').aggregate('count'),\n vl.facet().fieldN('Agency')\n )\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":418,"value":"md`These graph shows the distribution of salaries in $20,000 segments with circles representing the counts of individuals in that salary range.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":415,"value":"md`## Counts of individuals in a particular salary range by Gender.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":444,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1375,"value":"vl.markPoint()\n.data(filtered_from_all)\n.width((width-250))\n.encode(vl.x().fieldQ('Salary1Corrected').bin({nice: true, step: 20000 }).axis({title: 'Salary (binned)'}),\n vl.y().fieldN('Name').aggregate('count').stack(null),\n vl.color(vl.fieldN('Gender')),\n vl.size().fieldN('Name').aggregate('count').scale({range: [50,400]})\n )\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":644,"value":"md`## Counts of individuals in a particular salary range by Gender for highly compensated (over ${numeral(100000).format('$0,0')} in yearly salary).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1385,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1380,"value":"vl.markPoint()\n.data(filtered_from_all)\n.width((width-250))\n.transform(vl.filter({field: 'Salary1Corrected', gt: 100000})\n )\n.encode(vl.x().fieldQ('Salary1Corrected').bin({nice: true, step: 20000 }).axis({title: 'Salary (binned)'}),\n vl.y().fieldN('Name').aggregate('count').stack(null),\n vl.color(vl.fieldN('Gender')),\n vl.size().fieldN('Name').aggregate('count').scale({range: [50,400]})\n )\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1619,"value":"md`## How much do employees marked with gender 'F' on average make copared to employees marked with gender 'M' on average.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1666,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1555,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1964,"value":"md`## Count by Gender in each Class.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1677,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":747,"value":"table_view = md`## Table-based view`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":682,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1031,"value":"md`## Individual raises from 2017 to 2018 for a selected Class.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1060,"value":"viewof single_class = select(classes)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1037,"value":"{\n const f_detail = vl.markLine()\n .width((width - 200)/2)\n .height(600)\n .data(three_years_filtered)\n .transform(\n vl.filter({field: 'Year', oneOf: years}),\n vl.filter({field: 'Gender', equal: 'F'}))\n .encode(\n vl.x().fieldO('Year'),\n vl.y().fieldQ('Salary1Corrected').axis({title: 'Salary (F)'}),\n vl.detail().fieldN('Name'),\n vl.size(vl.value(4)),\n vl.opacity(vl.value(0.5)),\n vl.color(vl.value('blue'))\n );\n \n const m_detail = vl.markLine()\n .width((width - 200)/2)\n .height(600)\n .data(three_years_filtered)\n .transform(vl.filter({field: 'Year', oneOf: years}),\n vl.filter({field: 'Gender', equal: 'M'}))\n .encode(\n vl.x().fieldO('Year'),\n vl.y().fieldQ('Salary1Corrected').axis({title: 'Salary (M)'}),\n vl.detail().fieldN('Name'),\n vl.size(vl.value(4)),\n vl.opacity(vl.value(0.5)),\n vl.color(vl.value('orange'))\n );\n \n const chart = vl.hconcat(f_detail, m_detail)\n return chart.render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1968,"value":"md`## Raise Distribution in percents for Selected Classes\nNote that individuals whose salary decreased are not included.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1977,"value":"md`${class_limits}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1142,"value":"vl.markTick()\n.data(filtered_raises)\n.width(width-300)\n.transform(\n vl.filter({field: 'raise', gt: 0}),\n )\n.encode(vl.x().fieldQ('percent_raise'),\n vl.y().fieldN('Gender'),\n vl.color(vl.fieldN('Gender')),\n vl.size(vl.value(100))\n )\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1980,"value":"md`## Raise Distribution in percents for individuals and averages for each classification selected and over all\n\n${class_limits}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1170,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1997,"value":"md`## Raise Distribution in percents for individuals and averages for each classification selected and over all for individuals who did not leave their job family\n\n${class_limits}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1984,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2004,"value":"md`## Raise Distribution in dollars for individuals and averages for each classification selected and over all\n\n${class_limits}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1198,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2011,"value":"md`## Raise Distribution in dollars for individuals and averages for each classification selected and over all for those that did not leave their job family.\n\n${class_limits}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2014,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2019,"value":"md`## Raise Distribution based on type of job change\n\n${class_limits}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1256,"value":"{\n const rects = vl.markRect()\n .data(filtered_raises)\n .transform(\n vl.filter({field: 'raise', gt: 0.0}),\n vl.calculate(\"datum['Job Change Type'] + ' (' + datum.Gender + ')'\").as('Job Change Label')\n )\n .encode(vl.y().fieldN('Job Change Label').axis({title: 'Number of Raises by Job Change Type'}),\n vl.x().fieldN('family2017').axis({title: 'Starting Job Family'}),\n vl.color().fieldN('Name').aggregate('count')\n .legend({orient: 'bottom', titleOrient: 'left'}),\n vl.tooltip().fieldN('Name').aggregate('count'),\n vl.opacity(vl.value(0.6)),\n );\n \n return rects\n .width((width - 250))\n .height(150)\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1326,"value":"{\n const rects = vl.markRect()\n .data(filtered_raises)\n .transform(\n vl.filter({field: 'raise', gt: 0.0}),\n vl.calculate(\"datum['Job Change Type'] + ' (' + datum.Gender + ')'\").as('Job Change Label')\n )\n .encode(vl.y().fieldN('Job Change Label').axis({title: 'Average $ Raises by Job Change Type'}),\n vl.x().fieldN('family2017').axis({title: 'Starting Job Family'}),\n vl.color().fieldQ('raise').aggregate('average')\n .legend({orient: 'bottom', titleOrient: 'left'}),\n vl.tooltip().fieldN('raise').aggregate('average'),\n vl.opacity(vl.value(0.6)),\n );\n \n return rects\n .width((width - 250))\n .height(150)\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1336,"value":"{\n const rects = vl.markRect()\n .data(filtered_raises)\n .transform(\n vl.filter({field: 'raise', gt: 0.0}),\n vl.calculate(\"datum['Job Change Type'] + ' (' + datum.Gender + ')'\").as('Job Change Label')\n )\n .encode(vl.y().fieldN('Job Change Label').axis({title: 'Raises % by Job Change Type'}),\n vl.x().fieldN('family2017').axis({title: 'Starting Job Family'}),\n vl.color().fieldQ('percent_raise').aggregate('average')\n .legend({orient: 'bottom', titleOrient: 'left'}),\n vl.tooltip().fieldN('percent_raise').aggregate('average'),\n vl.opacity(vl.value(0.6)),\n );\n \n return rects\n .width((width - 250))\n .height(150)\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2114,"value":"viewof job_changes_to_include = checkbox(\n {\n title: 'Job Changes to include',\n description: 'Selecting job change types limits the data displayed in the raise total visualizations.',\n options: job_change_types.map(r => ({value: r, label: r})),\n value: job_change_types,\n submit: false,\n css_width: `${Math.floor(width / 4)}px`,\n collapsible_label: 'Job Change Selections'\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2146,"value":"viewof raise_types_to_include = checkbox(\n {\n title: 'raise types to include',\n description: 'Selecting raise types limits the data displayed in the raise total visualizations.',\n options: ['positive','negative'].map(r => ({value: r, label: r})),\n value: ['positive', 'negative'],\n submit: false,\n css_width: `${Math.floor(width / 4)}px`,\n collapsible_label: 'Raise Type Selections'\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2032,"value":"md`## How much of a raise did F individuals get for each 1% of raises for M individuals\n\n${class_limits}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2022,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2076,"value":"md`## How much of a raise did F individuals get for each $1 of raises for M individuals\n\n${class_limits}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2067,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":101,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":168,"value":"md`## Appendix -- Data and imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2086,"value":"md`### Data elements (imported and calculated)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":135,"value":"minimum_wage_salary = 7.25 * 52 * 40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1418,"value":"years = ['2017', '2018']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":193,"value":"unfiltered_location_counts = d3.nest()\n.key(r => r['City/County'])\n.rollup(r => r.length)\n.entries(three_years_all_parsed)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":173,"value":"location_counts = d3.nest()\n.key(r => r['City/County'])\n.rollup(r => r.length)\n.entries(filtered_from_all)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":224,"value":"departments = d3.nest()\n.key(r => r.Agency)\n.entries(three_years_all_parsed).map(r => r.key)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":355,"value":"classes = job_families.map(r => r.class).sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":430,"value":"class_limits = {\n if (classes_to_include.length === classes.length) {\n return 'All job classifications shown.';\n }\n return `Limited to: ${classes_to_include.join(', ')} classifications only.`\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1016,"value":"three_years_all = d3.tsv('https://gist.githubusercontent.com/jjimenez/4b234fbf3d81695c12dcf3b136bb96a0/raw/4c402689cb066c941a1b5c3ce95eb241f45631f1/uiowa%2520salaries%2520from%2520Iowa%2520Salary%2520Book.tsv')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1098,"value":"three_years_nested = d3.nest().key(r => r.Name).entries(three_years_all)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1102,"value":"at_least_two_years = three_years_nested.filter(person => (person.values.some(r => r.Year === '2017') && person.values.some(r => r.Year === '2018'))).filter(person => person.values.length <=3)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1118,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2120,"value":"job_change_types = Array.from(new Set(filtered_raises.map(row => row['Job Change Type'])))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1903,"value":"raise_totals = calculate_raise_totals(filtered_raises\n .filter(row => row.raise > 0))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2045,"value":"raise_totals_without_change = calculate_raise_totals(filtered_raises.filter(r => r.raise > 0 && r['Job Change Type'] != 'Left job family'))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2127,"value":"raise_totals_filtered = calculate_raise_totals(filtered_raises.filter(row => {\n const actual_raise = row.raise > 0 && raise_types_to_include.some(type => type === 'positive');\n const actual_negative = row.raise < 0 && raise_types_to_include.some(type => type === 'negative');\n const job_change_ok = job_changes_to_include.some(job_change => job_change === row['Job Change Type']);\n return (actual_raise || actual_negative) && job_change_ok;\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2041,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1389,"value":"filtered_raises = {\n //things that can be filtered\n // low salaries filter_low_salaries minimum_wage_salary\n // agencies agencies_to_include\n // location filter_only_johnson\n return raises.filter( row => {\n const salary_ok = !filter_low_salaries || (row.salary2018 > minimum_wage_salary && row.salary2017 > minimum_wage_salary);\n const agency_ok = agencies_to_include.some(agency => agency === row.Agency)\n const location_ok = !filter_only_johnson ||(row['City/County'].toUpperCase() === 'JOHNSON')\n const class_ok = classes_to_include.some(klass => row.Class_2017.toUpperCase() === klass.toUpperCase())\n return salary_ok && agency_ok && location_ok && class_ok\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1022,"value":"three_years_classes = Array.from(new Set(three_years_all.map(row => row.Class)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1023,"value":"three_years_filtered = three_years_all.filter(r => {\n r.Salary1Corrected = parseFloat(r.Salary1.split(',').join(''))\n r.Salary2Corrected = parseFloat(r.Salary2.split(',').join(''))\n const salary_ok = !filter_low_salaries || (r.Salary1Corrected > minimum_wage_salary);\n const agency_ok = agencies_to_include.some(agency => agency === r.Agency)\n const location_ok = !filter_only_johnson ||(r['City/County'].toUpperCase() === 'JOHNSON')\n const class_ok = r.Class.toUpperCase() === single_class.toUpperCase()\n return class_ok && agency_ok && location_ok && salary_ok\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1435,"value":"three_years_all_parsed = three_years_all.filter(r => years.some(year => year === r.Year)).map( r => (Object.assign({Salary1Corrected: parseFloat((r.Salary1.split(',').join(''))), Salary2Corrected: parseFloat(r.Salary2.split(',').join(''))},r)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1440,"value":"filtered_from_all = {\n return three_years_all_parsed.filter( row => {\n const salary_ok = !filter_low_salaries || (row.Salary1Corrected > minimum_wage_salary);\n const agency_ok = agencies_to_include.some(agency => agency === row.Agency)\n const location_ok = !filter_only_johnson ||(row['City/County'].toUpperCase() === 'JOHNSON')\n const class_ok = classes_to_include.some(klass => row.Class.toUpperCase() === klass.toUpperCase())\n const year_ok = row.Year === selected_year\n return salary_ok && agency_ok && location_ok && class_ok && year_ok\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1224,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":665,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1823,"value":"isograph_data = {\n let result = [];\n totals_by_class.forEach( row => {\n //if (row.classification != 'GRAND TOTAL'){\n Array.prototype.push.apply(result,(Array.from({length: row.M_staff_count}, (x,i) => ({id: i+1, Gender:'M', Class: row.classification}))).concat((Array.from({length: row.F_staff_count}, (x,i) => ({id: row.M_staff_count+i+1, Gender: 'F', Class: row.classification})))))\n // }\n })\n return result\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2083,"value":"md`### Functions`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2,"value":"d3 = require('d3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":122,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":132,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":680,"value":"import {table} from \"@tmcw/tables@444\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1933,"value":"vl = {\n const [vega, vegalite, api, tooltip] = await Promise.all([\n 'vega@5',\n 'vega-lite@3.4.0',\n 'vega-lite-api@0.0.15',\n 'vega-tooltip@0.18.1'\n ].map(module => require(module)));\n\n const options = {\n config: {\n // vega-lite default configuration\n config: {\n view: {width: 400, height: 300},\n mark: {tooltip: null}\n }\n },\n init: view => {\n // initialize tooltip handler\n view.tooltip(new tooltip.Handler().call);\n // enable horizontal scrolling for large plots\n if (view.container()) view.container().style['overflow-x'] = 'auto';\n },\n view: {\n // view constructor options\n loader: vega.loader({baseURL: 'https://vega.github.io/vega-datasets/'}),\n renderer: 'canvas'\n }\n };\n \n return api.register(vega, vegalite, options);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1015,"value":"\nimport {select} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":148,"value":"numeral = require('numeral')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":649,"value":"md`Collapsible css and code (spelling theirs) are from [Implementing a Pure CSS Collapsible](https://alligator.io/css/collapsible/)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":485,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":407,"value":"md`## Sandbox`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":411,"value":"md`These items are not in use.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W43","stdlib":"1","titling":"auto"}} 30:"$2f:body"