31:T64a,vega_embed({ "title": `${gender_frac.filter(d => d.party == "All").map(d => d.count).reduce((a,b) => a + b)} Candidates in seats with less than ${(marginality*100).toFixed(0)}% margin between top two parties`, "$schema": "https://vega.github.io/schema/vega-lite/v2.json", "data": { "values": gender_frac }, "width": width*2/3, "resolve": { "scale": { "color": "independent" } }, "layer": [ { "mark": "bar", "encoding": { "x": { "aggregate": "sum", "field": "count", "type": "quantitative", "stack": "normalize" }, "y": { "field": "party", "type": "nominal" }, "color": { "field": "gender", "type": "ordinal", "scale": {"domain": ["Female", "Male"], "range":Object.values(gender_colors).slice(0,2).reverse()} } } }, { "mark": { "type": "text", "baseline": "middle", "align":"right", "dx": -5 }, "encoding": { "x": { "aggregate": "sum", "field": "count", "type": "quantitative", "stack": "normalize" }, "y": { "field": "party", "type": "ordinal" }, "color": { "field": "gender", "type": "nominal", "scale": { "range": [ "white" ] }, "legend": null }, "text": { "field": "count_text", "type": "ordinal" } } } ] }) 32:Te28,vega_embed({ "$schema": "https://vega.github.io/schema/vega-lite/v2.json", "data": {"values":constituency_filtered}, "transform": [ { "lookup": "gender", "as": "gender_color", "from": { "data": { "values": Object.entries(gender_colors).map(d => ({gender: d[0], color: d[1]})) }, "key": "gender", "fields": [ "color" ] } } ], "vconcat": [{"hconcat": [{ "width": width/3, "title": "Top two candidates in each seats, by gender", "mark": "bar", "encoding": { "x": { "title": "Winning margin in 2015", "bin": {"bins":7}, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0,`${Math.min(1, marginality*1.1)}`]} }, "y": { "title":"Number of seats", "aggregate": "count", "type": "quantitative" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } } },{ "width": width/3, "title": "Gender fractions", "mark": "bar", "encoding": { "x": { "title": "Winning margin in 2015", "bin": { "bin": {"bins":7} }, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0,`${Math.min(1, marginality*1.1)}`]} }, "y": { "title":"Gender fraction", "aggregate": "count", "type": "quantitative", "stack": "normalize" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } } }]}, {"hconcat":[{ "width": width/3, "title": "Candidates that won the election", "mark": "bar", "encoding": { "x": { "title": "Winning margin in 2015", "bin": { "bin": {"bins":7} }, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0,`${Math.min(1, marginality*1.1)}`]} }, "y": { "title":"Gender fraction", "aggregate": "count", "type": "quantitative", "stack": "normalize" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } }, "transform": [ { "filter": "datum.won_election" } ] }, { "width": width/3, "mark": "bar", "title": "Candidates that lost the election", "encoding": { "x": { "title": "Winning margin in 2015", "bin": { "bin": {"bins":7} }, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0,`${Math.min(1, marginality*1.1)}`]} }, "y": { "title":"Gender fraction", "aggregate": "count", "type": "quantitative", "stack": "normalize" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } }, "transform": [ { "filter": "!datum.won_election" } ] }]} ] }, {theme: "quartz", renderer: "svg"} )33:T662,vega_embed({ "title": `${veteran_gender_frac.filter(d => d.party == "All").map(d => d.count).reduce((a,b) => a + b)} Candidates in seats with more than ${(veteran_marginality*100).toFixed(0)}% margin between top two parties`, "$schema": "https://vega.github.io/schema/vega-lite/v2.json", "data": { "values": veteran_gender_frac }, "width": width*2/3, "resolve": { "scale": { "color": "independent" } }, "layer": [ { "mark": "bar", "encoding": { "x": { "aggregate": "sum", "field": "count", "type": "quantitative", "stack": "normalize" }, "y": { "field": "party", "type": "nominal" }, "color": { "field": "gender", "type": "ordinal", "scale": {"domain": ["Female", "Male"], "range":Object.values(gender_colors).slice(0,2).reverse()} } } }, { "mark": { "type": "text", "baseline": "middle", "align":"right", "dx": -5 }, "encoding": { "x": { "aggregate": "sum", "field": "count", "type": "quantitative", "stack": "normalize" }, "y": { "field": "party", "type": "ordinal" }, "color": { "field": "gender", "type": "nominal", "scale": { "range": [ "white" ] }, "legend": null }, "text": { "field": "count_text", "type": "ordinal" } } } ] }) 34:Tdba,vega_embed({ "$schema": "https://vega.github.io/schema/vega-lite/v2.json", "data": {"values":constituency_filtered_non_marginals}, "transform": [ { "lookup": "gender", "as": "gender_color", "from": { "data": { "values": Object.entries(gender_colors).map(d => ({gender: d[0], color: d[1]})) }, "key": "gender", "fields": [ "color" ] } } ], "vconcat": [{"hconcat": [{ "width": width/3, "title": "Top two candidates in each seats, by gender", "mark": "bar", "encoding": { "x": { "title": "Winning margin in 2015", "bin": {"bins":7}, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0, 1]} }, "y": { "title":"Number of seats", "aggregate": "count", "type": "quantitative" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } } },{ "width": width/3, "title": "Gender fractions", "mark": "bar", "encoding": { "x": { "title": "Winning margin in 2015", "bin": { "bin": {"bins":7} }, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0, 1]} }, "y": { "title":"Gender fraction", "aggregate": "count", "type": "quantitative", "stack": "normalize" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } } }]}, {"hconcat":[{ "width": width/3, "title": "Candidates that won the election", "mark": "bar", "encoding": { "x": { "title": "Winning margin in 2015", "bin": { "bin": {"bins":7} }, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0, 1]} }, "y": { "title":"Gender fraction", "aggregate": "count", "type": "quantitative", "stack": "normalize" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } }, "transform": [ { "filter": "datum.won_election" } ] }, { "width": width/3, "mark": "bar", "title": "Candidates that lost the election", "encoding": { "x": { "title": "Winning margin in 2015", "bin": { "bin": {"bins":7} }, "field": "con_lab_diff_2015", "type": "quantitative", "scale": {"domain": [0, 1]} }, "y": { "title":"Gender fraction", "aggregate": "count", "type": "quantitative", "stack": "normalize" }, "color": { "field": "gender_color", "type": "nominal", "scale": null }, "tooltip": { "field": "gender" } }, "transform": [ { "filter": "!datum.won_election" } ] }]} ] }, {theme: "quartz", renderer: "svg"} )35:T474,vega_embed("#vis", { "$schema": "https://vega.github.io/schema/vega-lite/v2.json", "data": {"values":constituency_filtered.sort((a, b) => constituency_order.indexOf(a.constituency_name) - constituency_order.indexOf(b.constituency_name))}, "config": {"view":{ "clip": false}}, "transform": [ { "lookup": "party_abbreviation", "as": "party_color", "from": { "data": { "values": party_colors }, "key": "party", "fields": [ "color" ] }, "default": "lightgrey" } ], "mark": "bar", "encoding": { "x": { "title": null, "field": "constituency_name", "type": "nominal", "sort": null }, "y": { "title":"Vote share", "field": "share", "scale": {"domain":[0, 1]} }, "color": { "field": "party_color", "type": "nominal", scale: null }, "tooltip": { "field": "party_abbreviation" }, "legend": true } }, {"renderer":"svg", "theme":"quartz"})36:T941,chart = { const svg = d3.select(DOM.svg(width, height)).style("background-color","#F9F9F9"); // Define the div for the tooltip var div = d3.select("body").append("div") .attr("class", "tooltip") .style("opacity", 0) console.log(div) d3.json("https://gist.githubusercontent.com/dldx/91b616ce10b7a1b48d7f063959a885cd/raw/97c937aa2b088555a08139e73db9d2f640285cb2/constituencies.hexjson").then((hexjson) => { // Create the svg element svg .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); // Render the hexes var hexes = d3.renderHexJSON(hexjson, width, height); // Bind the hexes to g elements of the svg and position them var hexmap = svg .selectAll("g") .data(hexes) .enter() .append("g") .attr("transform", function(hex) { return "translate(" + hex.x + "," + hex.y + ")"; }); // Convert data into hash map //let gender_map = new Map(constituency_gender.map((i) => [i.ons_id, i])) // Draw the polygons around each hex's centre hexmap .append("polygon") .attr("points", function(hex) {return hex.points;}) .attr("stroke", "white") .attr("stroke-width", "2") .attr("fill",hex => constituency_gender[hex.key].gender_color) .on("mouseover", function(hex) { div.transition() .duration(200) .style("opacity", .95); div .html(`${hex.n}: ${constituency_gender[hex.key].mp} (${constituency_gender[hex.key].party_abbreviation})`) .style("left", (d3.event.pageX + 28) + "px") .style("top", (d3.event.pageY - 28) + "px") // d3.select(this).classed("hover", true) }) .on("mouseout", function(d) { div.transition() .duration(500) .style("opacity", 0); }) // Add a legend svg.append("g") .attr("class", "legendOrdinal") .attr("transform", `translate(${width-200},${margin.top})`) .attr("transform-origin", "right"); let ordinal = d3.scaleOrdinal().domain(Object.keys(gender_colors).map(d => d != "unset" ? d + " MP" : "hidden")).range(Object.values(gender_colors)) var legendOrdinal = d3.legendColor() .shape("square") .shapePadding(10) .scale(ordinal); svg.select(".legendOrdinal") .call(legendOrdinal); }) return svg.node() }37:T58f,constituency_data = pipe( data => new df.DataFrame(results_2017_by_candidate) .join(results_2015, left => left.constituency_name, right => right.constituency_name, (left, right) => ({ constituency_name: left.constituency_name, ons_id: left.ons_id, con_lab_diff_2015: right.con_lab_diff_2015, share: +left.share, gender: left.gender, party_abbreviation: left.party_abbreviation, mp: left.firstname + " " + left.surname, sitting_mp: left.sitting_mp == "Yes" })), data => { let winning_candidate = data.groupBy(d => d.constituency_name).select(group => group.orderByDescending(d => d.share).first()) return data.join(winning_candidate, left => left.constituency_name, right => right.constituency_name, (left, right) => ({ constituency_name: left.constituency_name, ons_id: left.ons_id, con_lab_diff_2015: left.con_lab_diff_2015, share: +left.share, gender: left.gender, party_abbreviation: left.party_abbreviation, mp: left.mp, won_election: right.party_abbreviation == left.party_abbreviation, sitting_mp: left.sitting_mp })) } )() .toArray()38:[] 2f:{"title":"Gender distribution of candidates in marginal seats","cells":[{"id":0,"value":"md`# Gender distribution of candidates in marginal seats\n**MAY TAKE A FEW SECONDS TO LOAD ALL THE DATA...**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1775,"value":"marginal_seats = md`## What is the gender fraction of candidates in marginal seats?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":618,"value":"viewof marginality = slider({\n min: 0.,\n max: 1,\n value: 0.05,\n precision: 2,\n description: \"Filter seats by marginality\",\n title: 'Filter candidates',\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1917,"value":"viewof filter_candidates = radio({\n options: [\n { label: 'All', value: 'all' },\n { label: 'Only sitting MPs', value: 'sitting' },\n { label: 'Only challenger candidates', value: 'challenger' },\n ],\n value: 'all'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1051,"value":"gender_frac = \n [[\"All\", d => d], [\"Labour\", d => d.party_abbreviation == \"Lab\"], [\"Conservative\", d => d.party_abbreviation == \"Con\"]].map(e =>pipe(\n \n d => new df.DataFrame(constituency_filtered)\n .orderBy(row => row.gender)\n .where(e[1])\n .where(row => {\n switch(filter_candidates) {\n case \"all\": return true\n case \"sitting\": return row.sitting_mp\n case \"challenger\": return !row.sitting_mp\n \n }\n })\n .groupBy(row => row.gender)\n .select(group => ({\n gender: group.first().gender,\n count: group.count()\n}))\n.inflate(),\n \n d => d.generateSeries({\n \"count_text\": row => (row.count/d.getSeries('count').sum() * 100).toFixed(0) + \"% \" + row.gender,\n \"party\":row => e[0]})\n\n)()\n.toArray())\n.reduce((element, accumulator) => accumulator.concat(element), [])\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":849,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1935,"value":"md`Show seats with less than **${(marginality*100).toFixed(0)}% margin** between the top two candidates in the 2015 election.\nThese are the seats where both candidates have a chance of winning.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":218,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1773,"value":"non_marginal_seats = md`## What is the gender fraction of candidates in safe seats`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1770,"value":"viewof veteran_marginality = slider({\n min: 0.,\n max: 1,\n value: 0.3,\n precision: 2,\n description: \"Filter seats by marginality\",\n title: \"Filter candidates\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1938,"value":"viewof veteran_filter_candidates = radio({\n options: [\n { label: 'All', value: 'all' },\n { label: 'Only sitting MPs', value: 'sitting' },\n { label: 'Only challenger candidates', value: 'challenger' },\n ],\n value: 'all'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1792,"value":"veteran_gender_frac = \n [[\"All\", d => d], [\"Labour\", d => d.party_abbreviation == \"Lab\"], [\"Conservative\", d => d.party_abbreviation == \"Con\"]].map(e =>pipe(\n \n d => new df.DataFrame(constituency_filtered_non_marginals)\n .where(e[1])\n .where(row => {\n switch(veteran_filter_candidates) {\n case \"all\": return true\n case \"sitting\": return row.sitting_mp\n case \"challenger\": return !row.sitting_mp\n \n }\n })\n .groupBy(row => row.gender)\n .select(group => ({\n gender: group.first().gender,\n count: group.count()\n}))\n.inflate(),\n \n d => d.generateSeries({\n \"count_text\": row => (row.count/d.getSeries('count').sum() * 100).toFixed(0) + \"% \" + row.gender,\n \"party\":row => e[0]})\n\n)()\n.toArray())\n.reduce((element, accumulator) => accumulator.concat(element), [])\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1903,"value":"md`Show seats with more than **${(veteran_marginality*100).toFixed(0)}% margin** between the top two candidates in the 2015 election.\nThese are the seats where the top candidate has a much higher chance of winning.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1766,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1778,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":460,"value":"html`

Vote share of parties in 2017 election

\n Show seats with margin < ${(marginality * 100).toFixed(0)}% in 2015\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":709,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":773,"value":"md`## Map of MPs elected in 2017 to seats with less than ${(marginality*100).toFixed(0)}% margin, by gender`","pinned":false,"hidden":false,"mode":"ojs"},{"id":614,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":470,"value":"constituency_gender = new df.DataFrame(constituency_data)\n.groupBy(d => d.constituency_name)\n.select(group => group.first())\n.inflate()\n.generateSeries({gender_color: row => gender_colors[Math.abs(row.con_lab_diff_2015) <= marginality ? row.gender : \"unset\"]})\n.toObject(d => d.ons_id, d => d)","pinned":false,"hidden":false,"mode":"ojs"},{"id":357,"value":"party_colors = [\n {\n \"party\": \"Lab\",\n \"color\": \"#c61148\"\n },\n {\n \"party\": \"Con\",\n \"color\": \"#0096DB\"\n },\n {\n \"party\": \"SNP\",\n \"color\": \"#FCCA46\"\n },\n {\n \"party\": \"LD\",\n \"color\": \"#F37A48\"\n },\n {\n \"party\": \"Green\",\n \"color\": \"#A1C181\"\n },\n {\n \"party\": \"PC\",\n \"color\": \"darkgreen\"\n },\n {\"party\": \"SF\",\n \"color\": \"#008e4b\",\n },\n {\"party\": \"DUP\",\n \"color\": \"red\"\n }\n ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1487,"value":"party_colors_map = new Map(party_colors.map(d => [d.party, d.color]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":702,"value":"gender_colors = ({ \"Male\": \"#7A47C6\",\n \"Female\": \"#9de866\", \"unset\": \"#efefef\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1263,"value":"constituency_order = new df.DataFrame(constituency_filtered)\n .orderByDescending(d => d.con_lab_diff_2015)\n.groupBy(d => d.constituency_name)\n.select(d => d.first().constituency_name)\n .toArray()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1170,"value":"results_2015 = d3.csv(\"https://gist.githubusercontent.com/dldx/91b616ce10b7a1b48d7f063959a885cd/raw/f16285e367a50a79497a53cb1dcd290039067336/hocl-ge2015-results-summary.csv\")\n.then(data => new df.DataFrame(data)\n .generateSeries({con_lab_diff_2015: row => {\n return (row[row.first_party.toLowerCase()]-row[row.second_party.toLowerCase()])/row.valid_votes\n }}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1621,"value":"results_2017_by_candidate = d3.csv(\"https://gist.githubusercontent.com/dldx/91b616ce10b7a1b48d7f063959a885cd/raw/97c937aa2b088555a08139e73db9d2f640285cb2/HoC-GE2017-results-by-candidate.csv\").then(data => {\n // For each constituency, take only the top two candidates by vote share\n return df.DataFrame.concat(new df.DataFrame(data)\n .groupBy(d => d.constituency_name).select(group => group.orderByDescending(d => d.share).take(2))\n .toArray()).toArray()\n \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1214,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":480,"value":"constituency_filtered = constituency_data\n.filter((d) => Math.abs(+d.con_lab_diff_2015) <= `${marginality}` )","pinned":false,"hidden":false,"mode":"ojs"},{"id":1781,"value":"constituency_filtered_non_marginals = constituency_data\n.filter((d) => Math.abs(+d.con_lab_diff_2015) > `${veteran_marginality}` )","pinned":false,"hidden":false,"mode":"ojs"},{"id":548,"value":"// Set the size and margins of the svg\nmargin = ({top: 10, right: 10, bottom: 10, left: 10})","pinned":false,"hidden":false,"mode":"ojs"},{"id":552,"value":"width = document.body.clientWidth - margin.left - margin.right","pinned":false,"hidden":false,"mode":"ojs"},{"id":554,"value":"\theight = 600 - margin.top - margin.bottom","pinned":false,"hidden":false,"mode":"ojs"},{"id":610,"value":"d3 = require(\"https://d3js.org/d3.v5.min.js\", \"d3-hexjson\", \"d3-svg-legend\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":139,"value":"vega_embed = require(\"vega-embed@3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":654,"value":"html`CSS Styles\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":253,"value":"import {slider, radio} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":851,"value":"df = require(\"https://bundle.run/data-forge\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1115,"value":"// Pipe operator\nfunction pipe(...fs) {\n return (...args) => fs.reduce((args,f) => [f.apply(this,args)],args)[0];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1447,"value":"html`Table styles`","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}