32:Tfc4,# The demographics of House of Rep Members in Nigeria's National Assembly It is always interesting to understand the demographic composition of the ruling class in any country. Can the ruling class be a more representative sample of the population they preside over? This article takes a look at the National Assembly, House of Representatives in Nigeria. We answer questions like: Why should people care about the House of Rep members? What is the gender/age composition of the house of reps and how has it changed over the past few tenures? Are there consistent differences between geopolitical zones? What are the implications of the gender/age representation in the House of Reps? ## Why House of Reps? Why this specific role? It is an elected role. It is a role for which [CSN](https://citizensciencenigeria.org/) has a lot of data, and it is a role that we believe should represent **the people** at the Federal level. ### Some notes on the dataset The dataset held by [CSN](https://citizensciencenigeria.org/) on federal house of representatives in Nigeria can be summarised in the table below. |year| 1999 | 2003 | 2007 | 2011 | 2015 | 2019 | |----|------|------|------|------|------|------| | # of records| ${tenure1999.length}| ${tenure2003.length}|${tenure2007.length}|${tenure2011.length}|${tenure2015.length}|${tenure2019.length}| It is clear from the table that the records do not always cover all 360 seats in the Federal House of Representatives. We however believe the dataset presented in this article is representative of reality. Indeed while curating the dataset, we observed a few positions that were vacant. ### Why you should care about your representatives For any Nigerian going about their daily hustle, the closest elected official they have at the federal level, is not the President. It is a member of the House of Representatives. That is because every road, every local government in the country, is directly linked to one of the 360 seats in the House of Representatives. Since the National Assembly is made up of 469 members, it also means that the House of Representatives accounts for at least 76% of the National Assembly. And if Nigerians, going about their daily hustle, are not satisfied with the performance of the House member representing them, the constitution empowers them to vote out that House member before the next election. Your federal representative is also directly responsible for infrastructure in your community. Members of the Federal House of Representatives exercise significant discretion over **constituency funds** (also referred to as **zonal intervention funds**). These are funds used for infrastructural projects in the respective constituencies House members represent [(cf. Awofeso and Irabor 2020)](https://www.eajournals.org/wp-content/uploads/The-Nigerian-National-Assembly-and-the-Oversight-of-Constituency-Fund.pdf). Although the funds are included in the federal budget, they are not subject to regulatory control under laws of the federation of Nigeria. Instead, House members recommend which constituency projects benefit from the fund in a fiscal year. The chosen projects are then executed by the Executive arm of government. In 2017, [BudgIT](https://yourbudgit.com/wp-content/uploads/2017/08/2017-Constituency-Projects.xlsx) reported that, in the national budget, a total of N100 billion was allocated to constituency projects by federal law makers. In September 2021, [Punch newspaper](https://punchng.com/each-rep-gets-n100m-for-constituency-project-adamawa-lawmaker/) quoted a federal law maker, [Kwamoti Bitrus Laori](https://citizensciencenigeria.org/public-offices/persons/kwamoti-bitrus-laori) as saying that [each member at the Federal House of Representatives receives N100 million for constituency projects](https://punchng.com/each-rep-gets-n100m-for-constituency-project-adamawa-lawmaker/), suggesting that the 360 members of the House of Representatives account for 36% of the constituency funds by the National Assembly in year 2021.33:T814,The overarching question that easily comes out of this is, why? Does having equal or close to equal proportion of females to males at the House of Representatives really matter to a street hawker in Kebbi or Kano? And if it doesn't, how about a street hawker in Lagos or Bayelsa? Would the nature of our laws or the speed at which bills are passed, necessarily change if there were more women in the House of Representatives? Or could the lower levels of gender representation in the Northern zones be pointing to something else? Differences in marginalisation between the Southern and Northern geo-political zones, perhaps? In other words, is the data merely a reflection of society; that is, female marginalisation is abysmal in the South but close to hell in the North? Perhaps, recent events indicate the true place of female representation in Nigeria. Terror attacks leading to the rape and murder of women have been increasingly reported in the media since around July 2011 (e.g., [Independent](https://blog.independent.ng/index.php/2021/06/24/gumi-under-fire-for-rating-herdsmen-better-than-ipob/), [Daily Post](https://dailypost.ng/2017/08/23/fulani-herdsmen-rape-72-year-old-woman-coma-ondo/), [The Sun](https://www.sunnewsonline.com/herdsmen-on-rampage-in-edo-rape-women-kill-2-senator-alleges/), [The Eagle](https://theeagleonline.com.ng/my-husband-child-watched-as-fulani-kidnappers-raped-me-woman-on-holiday-in-nigeria/), [Vanguard](https://www.vanguardngr.com/2021/11/breaking-suspected-fulani-herdsmen-abduct-10-travelers-rape-stab-woman-to-death-in-abia/), [SaharaReporters](http://saharareporters.com/2021/03/19/exclusive-rivers-community-boils-fulani-herdsmen-destroy-farms-rape-women)). Besides few momentary condemnations, the country seems criminally quiet at the growing rampage and most vile violation of womanhood. In contrast, ahead of the 2023 nation-wide elections, political candidates across the country will beg women groups such as, market women unions, traders, nurses, teachers, and political party representatives - for their votes.34:T8ac,### Age distribution in the House of Representatives North Central has consistently produced the highest number of younger public officials between 2007 and 2021. The geo-political zone with the highest number of older public officials is less clear. However, the South-South region appears to be more consistent in producing the highest number of older public officials between 2007 and 2021. The largest age distribution seems to have occurred in 2015 in the South West geo-political zone. Generally, the median age of the House of Representative members is increasing for all zones. This is not surprising if we considered career politicians who spend multiple tenures at the same position. One can expect the median age to grow roughly by the length of the tenure in regions where a significant number of politicians have multiple terms. So does age representation in public office really matter? With close to uniform levels of poverty across the geo-political zones, to what extent should Nigerians focus on the ages of those representing them? There are a number of explanations one could offer to account for age distribution such as, the flawed electoral process and political apathy. Say for instance that in 2007, a person representing a North Central constituency got into office at the age of 20 and has remained in office till 2021. It could imply a number of things. One, the person could simply be doing a great job representing their constituency and has been returned to office at each electoral cycle. This is the easy but less convincing explanation. Second, given the weak nature of the electoral institution, this person may belong to a network of political actors who ensure their stay in office. Third, the constituency may be suffering from an acute condition of political apathy whereby the office holder goes unchallenged for 14 years. There are some limitations to the CSN data. The first is that it is incomplete and as a result, does not capture the complete set of ages for public office holders from 1999 to date. Second, one may well argue that while House of Representative members are effective at the constituency level, the data presented is aggregated at the geo-political level.35:T4be,parseTenureData = year => rawData => { const getAgeFromYear = getAge(year); const getPosition = getRelevantPosition(year); const filterPositionTenure = d => d.startDate && new Date(d.startDate).getFullYear() <= year let result = rawData.data.browseDirectoryPaginated.edges .map(({node}) => node) .map(d => { const {position} = getPosition(d) || {}; const constituency = position ? position.constituency : {name: '', state: { name: ''}}; const state = constituency && constituency.state ? constituency.state.name : "" const zone = stateToZoneMap[state] || "uncategorized"; const tenures = d.positionTenures.filter(filterPositionTenure); return ({ name: d.fullName, gender: d.gender ? d.gender : "NA", url: `https://citizensciencenigeria.org/public-offices/persons/${d.slug}`, age: getAgeFromYear(d), position: position ? position.name : null, location: constituency ? `${constituency.name}, ${constituency.state ? constituency.state.name : ''}`: "", state, zone, numberOfTerms: new Set(tenures.map(d => new Date(d.startDate).getFullYear())).size, positionTenures: tenures })}); return result; }36:[{"id":"20ca71c1021186eacfd2f6b53bdccda15686eb3d1cf0104996a76706cc125a83a07e90baf0e3872f17d5332148760e212e65b3a87bc2acdf562643dca431f848","href":"https://static.observableusercontent.com/files/20ca71c1021186eacfd2f6b53bdccda15686eb3d1cf0104996a76706cc125a83a07e90baf0e3872f17d5332148760e212e65b3a87bc2acdf562643dca431f848","name":"2015-tenure@1.json","type":"application/json","lastModified":1638996738606.647,"size":530942},{"id":"c8d70efbb38e1dde98d6579bf7c7e1d52230792655653e428b591853883846456a4d5da4268cc933408f1a004868a05a971dd34c00f2d7a0e006d0eb7552a13a","href":"https://static.observableusercontent.com/files/c8d70efbb38e1dde98d6579bf7c7e1d52230792655653e428b591853883846456a4d5da4268cc933408f1a004868a05a971dd34c00f2d7a0e006d0eb7552a13a","name":"2011-tenure@1.json","type":"application/json","lastModified":1638996724065.498,"size":624724},{"id":"3d95b1fa35179e8ef1af568222cb23d1c4393f9eac91e839f7ec0ab8c0c76d01448dc854e8406c8099a0915430f93923ddbfbead6ef5968c7cf42932d289e3cd","href":"https://static.observableusercontent.com/files/3d95b1fa35179e8ef1af568222cb23d1c4393f9eac91e839f7ec0ab8c0c76d01448dc854e8406c8099a0915430f93923ddbfbead6ef5968c7cf42932d289e3cd","name":"2007-tenure@1.json","type":"application/json","lastModified":1638996712976.036,"size":582243},{"id":"c56c57125437af31969fbb0f6d7c3cbfef963ec89e82631a76a7d5a614ff0a707014683bcc465b3eb6a1da714a06a555527e6243a3c1cd7534bf06629c44ab22","href":"https://static.observableusercontent.com/files/c56c57125437af31969fbb0f6d7c3cbfef963ec89e82631a76a7d5a614ff0a707014683bcc465b3eb6a1da714a06a555527e6243a3c1cd7534bf06629c44ab22","name":"2003-tenure@1.json","type":"application/json","lastModified":1638996692262.76,"size":538880},{"id":"24b4b04e4d1ea75d138e6b823234bde31a9aecf0530cdc7391661c84b9daa77180e3d081d7e6e7af6265fef2b38de34dccc733fe10ccfa9bd68fcc9d330e77a1","href":"https://static.observableusercontent.com/files/24b4b04e4d1ea75d138e6b823234bde31a9aecf0530cdc7391661c84b9daa77180e3d081d7e6e7af6265fef2b38de34dccc733fe10ccfa9bd68fcc9d330e77a1","name":"2019-tenure@1.json","type":"application/json","lastModified":1638996666671.62,"size":514616},{"id":"3728a0a54d2cefcfd2b2cbf64e0eb028991de7400cfb9b3e74571e8a054a8e77377e4a12b20a927f1f37203dd3d36a3dbfc3afc16ebb91000a6e3e5835f3e9f8","href":"https://static.observableusercontent.com/files/3728a0a54d2cefcfd2b2cbf64e0eb028991de7400cfb9b3e74571e8a054a8e77377e4a12b20a927f1f37203dd3d36a3dbfc3afc16ebb91000a6e3e5835f3e9f8","name":"1999-tenure@1.json","type":"application/json","lastModified":1638996581499.805,"size":455242}] 30:{"title":"The demographics of House of Rep Members in Nigeria's National Assembly","cells":[{"id":0,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":519,"value":"### Gender distribution of the House of Representatives\nBased on CSN data, it is clearly a man's world in the House of Representatives. In addition to very low levels of female representation, there seems to be no marginal increase in the proportion of females at the federal House of Representatives between 2003 and 2019. The data also indicates that the North West and North Central geo-political zones have had the lowest levels of gender representation since 2003. ","pinned":false,"hidden":false,"mode":"md"},{"id":957,"value":"femaleToMaleProportion = Plot.plot({\n color: {\n legend: true,\n },\n height: 300,\n width: 900,\n x: {\n label: \"Age →\",\n grid: true,\n nice: true\n },\n y: {\n grid: true,\n label: \"← Male · Female →\",\n labelAnchor: \"center\",\n tickFormat: Math.abs\n },\n marks: [\n Plot.dot(tenure2019, Plot.stackY2({\n x: d => d.age,\n y: d => d.gender === \"Male\" ? -1 : d.gender === \"Female\" ? 1 : 0,\n fill: \"gender\", \n })),\n Plot.ruleY([0])\n ],\n caption: \"Proportion of Men and Women House of Rep members in the 2019 National Assembly\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":969,"value":"nationalGenderDistributionByTenure = plotStackedGenderChartByTenure(allTenures, d => d, \"Gender distribution for all geopolitical zones between 1999 - 2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1114,"value":"nationalGenderDistributionByTenureAndZone = {\n let tenures = [\"1999\", \"2003\", \"2007\", \"2011\", \"2015\", \"2019\"];\n let data = allTenures\n .map((tenure, index) =>\n tenure.all.map((d) => ({ ...d, tenure: tenures[index] }))\n ).flat();\n\n return Plot.plot({\n width: 900,\n color: {\n legend: true\n },\n height: 280,\n x: {\n percent: true,\n },\n y: {\n inset: 20,\n axis: \"right\"\n },\n facet: {\n data,\n y: (d) => d.tenure,\n x: \"zone\"\n },\n marks: [\n Plot.barX(\n data,\n Plot.stackX(Plot.groupZ({ x: \"proportion-facet\" }, { fill: \"gender\" }))\n ),\n Plot.ruleX([0, 1])\n ],\n caption: \"Gender distribution for the different geopolitical zones between 1999 - 2019 shown separately.\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":901,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":370,"value":"$34","pinned":false,"hidden":false,"mode":"md"},{"id":631,"value":"plotAgeAllTenureBoxPlot()","pinned":false,"hidden":false,"mode":"ojs"},{"id":940,"value":"plotAgeAllTenureBoxPlotByZone()","pinned":false,"hidden":false,"mode":"ojs"},{"id":435,"value":"md`## Number of Terms`","pinned":false,"hidden":false,"mode":"ojs"},{"id":736,"value":"plotTermsAllTenureBoxPlot()","pinned":false,"hidden":false,"mode":"ojs"},{"id":463,"value":"plotTenureHistogram(tenure2019, 'numberOfTerms', '2019', 5)","pinned":false,"hidden":false,"mode":"ojs"},{"id":467,"value":"plotTenureHistogram(tenure2015, 'numberOfTerms', '2015', 4)","pinned":false,"hidden":false,"mode":"ojs"},{"id":469,"value":"plotTenureHistogram(tenure2011, 'numberOfTerms', '2011', 4)","pinned":false,"hidden":false,"mode":"ojs"},{"id":472,"value":"plotTenureHistogram(tenure2007, 'numberOfTerms', '2007', 2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":422,"value":"html`

\n\n\n\n\n
\n

Appendix

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":280,"value":"plotTenureHistogram(tenure2019, \"age\", \"2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":316,"value":"plotTenureHistogram(tenure2015, \"age\", 2015)\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":320,"value":"plotTenureHistogram(tenure2011, \"age\", \"2011\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"plotTenureHistogram = (data, xProperty = \"age\", year = \"2019\", thresholds) => Plot.plot({\n height: 200,\n marginTop: 50,\n y: { grid: true, percent: false},\n facet: {\n data: data,\n x: \"zone\",\n },\n marks: [\n Plot.frame(),\n Plot.rectY(data, Plot.binX({y: \"count\"}, { x: xProperty, fill: \"zone\", thresholds}))\n ],\n caption: html`These histograms show the distribution of ${xProperty} across geo-political zones for the tenure starting in ${year}`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":254,"value":"plotGenderBarChart = (data, year) => Plot.plot({\n marginTop: 50,\n width: 900,\n height: 200,\n inset: 5,\n y: { grid: true, percent: true },\n facet: {\n data: data,\n x: \"zone\",\n },\n marks: [\n //Plot.frame(),\n Plot.barY(data, Plot.groupX({ y: \"proportion-facet\"}, { x: \"gender\", fill: 'gender'})),\n Plot.text(data, Plot.groupX({ \n y: \"proportion-facet\",\n text: {\n reduce: (group, allData) => group.length// d3.format(\".0%\")(group.length/allData.length)\n }\n }, { x: \"gender\", fill: 'gender', dy: -5}))\n ],\n caption: `Proportion of Female and Male members of the House of Reps for the tenure starting ${year}`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":155,"value":"histogram(allTenures[5].withAge, { xProperty: \"age\", xAxisLabel: 'age', xAxisTicks, quantiles: [.5,.8]}).render()","pinned":true,"hidden":false,"mode":"ojs"},{"id":496,"value":"plotTenureHistogramProportion(tenure2019,'numberOfTerms', 2019, 5)","pinned":false,"hidden":false,"mode":"ojs"},{"id":489,"value":"plotTenureHistogramProportion = (data, xProperty = \"age\", year = \"2019\", thresholds) => Plot.plot({\n marginTop: 50,\n height: 200,\n y: { grid: true, percent: true, },\n facet: {\n data: data,\n x: \"zone\",\n marginTop: 40\n },\n marks: [\n Plot.frame(),\n Plot.rectY(data, Plot.binX({y: \"proportion-facet\"}, { x: xProperty, fill: \"zone\", thresholds}))\n ],\n caption: html`These histograms show the distribution of ${xProperty} across geo-political zones for the tenure starting in ${year}`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":79,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":623,"value":"plotAgeAllTenureBoxPlot = () => {\n const data = [\n ...tenure2003.map(d => ({...d, tenure: '2003'})),\n ...tenure2007.map(d => ({...d, tenure: '2007'})),\n ...tenure2011.map(d => ({...d, tenure: '2011'})),\n ...tenure2015.map(d => ({...d, tenure: '2015'})),\n ...tenure2019.map(d => ({...d, tenure: '2019'}))\n ]\n return Plot.plot({\n marginBottom: 100,\n y: { grid: true, inset: 10 },\n x: {grid: true, tickRotate: -90},\n facet: {\n data,\n x: \"tenure\",\n },\n marks: [\n Plot.frame(),\n boxY(data, {y: \"age\", x: \"zone\", fill: \"zone\"})\n ],\n caption: html`This box plot shows the age distribution of HoR Members from different geo-political zones for tenures starting 2003, 2007, 2011, 2015 and 2019. The age plotted is the age of the politician at the beginning of the tenure`\n});\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":932,"value":"plotAgeAllTenureBoxPlotByZone = () => {\n const data = [\n ...tenure2003.map(d => ({...d, tenure: '2003 T'})),\n ...tenure2007.map(d => ({...d, tenure: '2007 T'})),\n ...tenure2011.map(d => ({...d, tenure: '2011 T'})),\n ...tenure2015.map(d => ({...d, tenure: '2015 T'})),\n ...tenure2019.map(d => ({...d, tenure: '2019 T'}))\n ]\n return Plot.plot({\n marginBottom: 100,\n y: { grid: true, inset: 10 },\n x: {grid: true, tickRotate: -90},\n facet: {\n data,\n x: \"zone\",\n },\n marks: [\n Plot.frame(),\n boxY(data, {y: \"age\", x: \"tenure\", fill: \"zone\"})\n ],\n caption: html`This box plot shows the age distribution of HoR Members from different geo-political zones for tenures starting 2007, 2011, 2015 and 2019. The age plotted is the age of the politician at the beginning of the tenure`\n});\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1032,"value":"function plotStackedGenderChartByTenure(tenureData, filter = d => d, caption = \"\") {\n let tenures = [\"1999\", \"2003\", \"2007\", \"2011\", \"2015\", \"2019\"];\n let data = tenureData\n .map((tenure, index) =>\n tenure.all.map((d) => ({ ...d, tenure: tenures[index] }))\n ).flat().filter(filter);\n\n return Plot.plot({\n color: {\n legend: true\n },\n height: 280,\n x: {\n percent: true\n },\n facet: {\n axis: \"right\",\n data,\n y: (d) => d.tenure\n },\n marks: [\n Plot.barX(\n data,\n Plot.stackX(Plot.groupZ({ x: \"proportion-facet\" }, { fill: \"gender\" }))\n ),\n Plot.ruleX([0, 1])\n ],\n caption\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":770,"value":"plotGenderBarChartAllYears = () => {\n const data = [\n ...tenure2003.map((d) => ({ ...d, tenure: \"2003\" })),\n ...tenure2007.map((d) => ({ ...d, tenure: \"2007\" })),\n ...tenure2011.map((d) => ({ ...d, tenure: \"2011\" })),\n ...tenure2015.map((d) => ({ ...d, tenure: \"2015\" })),\n ...tenure2019.map((d) => ({ ...d, tenure: \"2019\" }))\n ];\n return Plot.plot({\n color: {\n legend: true\n },\n inset: 3,\n height: 280,\n width: 900,\n y: { grid: true, percent: true },\n x: { grid: true, percent: true },\n facet: {\n marginRight: 90,\n data: data,\n x: \"zone\",\n y: \"tenure\"\n },\n marks: [\n Plot.frame({ stroke: \"#ddd\" }),\n Plot.barX(\n data,\n Plot.stackX(\n Plot.groupZ({\n x: \"proportion-facet\"},\n { fill: \"gender\" }\n )\n )\n )\n ],\n caption: `Proportion of Female and Male members of the House of Reps for the tenures starting 2007, 2011, 2015 and 2019`\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":734,"value":"plotTermsAllTenureBoxPlot = () => {\n const data = [\n ...tenure2007.map(d => ({...d, tenure: '2007'})),\n ...tenure2011.map(d => ({...d, tenure: '2011'})),\n ...tenure2015.map(d => ({...d, tenure: '2015'})),\n ...tenure2019.map(d => ({...d, tenure: '2019'}))\n ]\n return Plot.plot({\n marginBottom: 100,\n y: { grid: true, inset: 10 },\n x: {grid: true, tickRotate: -90},\n facet: {\n data,\n x: \"tenure\",\n },\n marks: [\n Plot.frame(),\n boxY(data, {y: \"numberOfTerms\", x: \"zone\", fill: \"zone\"})\n ],\n caption: html`This box plot shows the number of terms distribution of HoR Members from different geo-political zones for tenures starting 2007, 2011, 2015 and 2019. The number of terms plotted is that of the politician at the beginning of the specified tenure`\n});\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":83,"value":"getAge = (year) => ({ dateOfBirth }) => \n dateOfBirth === null ? null : (year - (new Date(dateOfBirth).getFullYear()));","pinned":true,"hidden":false,"mode":"ojs"},{"id":105,"value":"getRelevantPosition = year => node => {\n let position = node.positionTenures.find(d => new Date(d.startDate).getFullYear() === year);\n return position;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":151,"value":"allTenures = [tenure1999, tenure2003, tenure2007, tenure2011, tenure2015, tenure2019].map(d => ({\n all: d,\n withAge: d.filter(hasAge),\n withGender: d.filter(hasGender)\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":232,"value":"groupBy = (...f) => data => d3.group(data, ...f); ","pinned":true,"hidden":false,"mode":"ojs"},{"id":239,"value":"groupByState = groupBy(d => d.state)","pinned":true,"hidden":false,"mode":"ojs"},{"id":246,"value":"groupByZone = groupBy(d => stateToZoneMap[d.state], d => d.gender)","pinned":true,"hidden":false,"mode":"ojs"},{"id":241,"value":"groupByState(tenure2019)","pinned":true,"hidden":false,"mode":"ojs"},{"id":248,"value":"groupByZone(tenure2019)","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"Inputs.table(tenure2015.filter((hasGender)), { columns: ['name', 'url', 'location'], sort: \"name\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":191,"value":"not = f => d => !f(d)","pinned":true,"hidden":false,"mode":"ojs"},{"id":81,"value":"tenure1999 = parseTenureData(1999)(tenure1999raw)","pinned":false,"hidden":false,"mode":"ojs"},{"id":125,"value":"tenure2003 = parseTenureData(2003)(tenure2003raw)","pinned":true,"hidden":false,"mode":"ojs"},{"id":127,"value":"tenure2007 = parseTenureData(2007)(tenure2007raw)","pinned":true,"hidden":false,"mode":"ojs"},{"id":137,"value":"tenure2011 = parseTenureData(2011)(tenure2011raw)","pinned":true,"hidden":false,"mode":"ojs"},{"id":139,"value":"tenure2015 = parseTenureData(2015)(tenure2015raw)","pinned":true,"hidden":false,"mode":"ojs"},{"id":141,"value":"tenure2019 = parseTenureData(2019)(tenure2019raw)","pinned":true,"hidden":false,"mode":"ojs"},{"id":129,"value":"hasGender = d => d.gender !== null && d.gender !== \"NA\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":132,"value":"hasAge = d => d.age !== null","pinned":true,"hidden":false,"mode":"ojs"},{"id":77,"value":"tenure1999raw = FileAttachment(\"1999-tenure@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":112,"value":"tenure2003raw = FileAttachment(\"2003-tenure@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":114,"value":"tenure2007raw = FileAttachment(\"2007-tenure@1.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":116,"value":"tenure2011raw = FileAttachment(\"2011-tenure@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":118,"value":"tenure2015raw = FileAttachment(\"2015-tenure@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":120,"value":"tenure2019raw = FileAttachment(\"2019-tenure@1.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":47,"value":"xAxisTicks = new Array(10).fill(1).map((d,i) => i * 10)","pinned":true,"hidden":false,"mode":"ojs"},{"id":333,"value":"boxPlotTenure = (data, year = \"2019\", y = 'age') => Plot.plot({\n y: { grid: true, inset: 10 },\n marks: [\n boxY(data, {y, x: \"zone\"})\n ],\n caption: html`This box plot shows the ${y} distribution of HoR Members from different geo-political zones for tenures starting ${year}. The age plotted is the age of the politician at the beginning of the tenure`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"import {histogram} from \"@thehogfather/vis-utils\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":244,"value":"import { stateToZoneMap } from \"@thehogfather/ng-geo-lib\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":335,"value":"import { boxY } from '@observablehq/plot-box'","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}