30:T5f2,barChart1 = { const svg = d3 .create("svg") .attr("viewBox", [0, 0, width, height]) .call(zoom); // title! svg .append("text") .attr("x", width / 2) .attr("y", 30) .attr("text-anchor", "middle") .style("font-size", "22px") .style("text-decoration", "underline") .text("Number of Ultra Marathon Finishers by Event in " + selectedYear); // x-axis label svg .append("text") .attr("x", width / 2) .attr("y", height - 3) .style("font-size", "16px") .style("text-anchor", "middle") .text("Event"); //Create Y axis label svg .append("text") .attr("x", (height / 2) * -1) .attr("y", margin.left - 27) .style("text-anchor", "middle") .style("font-size", "16px") .attr("transform", "rotate(-90)") .text("Finishers"); svg .append("g") .attr("class", "bars") .attr("fill", "steelblue") .selectAll("rect") .data( ultraRunData.filter(function(d) { return d.year == selectedYear; }) ) .join("rect") .attr("x", d => barX(d.event)) .attr("y", d => barY(d.finishers)) .attr("height", d => barY(0) - barY(d.finishers)) .attr("width", barX.bandwidth()); svg .append("g") .attr("class", "x-axis") .call(barxAxis) .selectAll("text") .attr("transform", "translate(-10,0)rotate(-45)") .style("font-size", "5px") .style("text-anchor", "end"); svg .append("g") .attr("class", "y-axis") .call(baryAxis); return svg.node(); }31:[{"id":"763ab58439d1105e05105f32403469467018c82576fce8c53f094167d297d6a9d5c725ac86ef662c9da9029af5b88c9e896898b1345f95f71decc9ec3af7ea43","href":"https://static.observableusercontent.com/files/763ab58439d1105e05105f32403469467018c82576fce8c53f094167d297d6a9d5c725ac86ef662c9da9029af5b88c9e896898b1345f95f71decc9ec3af7ea43","name":"marathonData.csv","type":"text/csv","lastModified":1591299999579.55,"size":23001},{"id":"ae9f89b005c33f98e9b71e6933681462ba8713b7afcd6dbe25c112ab3a517c05d5c5f2438616843a288f2a6e13698c9aa158766ac2edf7ec4083d684999f2f22","href":"https://static.observableusercontent.com/files/ae9f89b005c33f98e9b71e6933681462ba8713b7afcd6dbe25c112ab3a517c05d5c5f2438616843a288f2a6e13698c9aa158766ac2edf7ec4083d684999f2f22","name":"ultrarunning.csv","type":"text/csv","lastModified":1591299999579.55,"size":907032},{"id":"bd65a51133f08e29fa9fd31336a5dccf5da6e7c9e99a96851154a539974b3adcbac9f50e7d9bc5b011c97dccc366835dd83563cac0da434ed75915e2987bccf2","href":"https://static.observableusercontent.com/files/bd65a51133f08e29fa9fd31336a5dccf5da6e7c9e99a96851154a539974b3adcbac9f50e7d9bc5b011c97dccc366835dd83563cac0da434ed75915e2987bccf2","name":"DUV_Baja_Trail (1).csv","type":"text/csv","lastModified":1591299999579.55,"size":23609},{"id":"d04488674079d6c2d4ac07fc89ec609c7572fe16f9145522d48e2adad215d509601125d4f7491177d4af65e3b4f708e87c74edb062f0104ee8735c745baf4470","href":"https://static.observableusercontent.com/files/d04488674079d6c2d4ac07fc89ec609c7572fe16f9145522d48e2adad215d509601125d4f7491177d4af65e3b4f708e87c74edb062f0104ee8735c745baf4470","name":"runners_by_speed.csv","type":"text/csv","lastModified":1591299999579.55,"size":295},{"id":"a6fbb3c81a7df7e4c908930ca2ed450a417ef08c5f51937b581e3b5e36497a35061ef453aabca93c51049c02544df3c7fd7b1d84dcedfc175b8c56145bcb0ecb","href":"https://static.observableusercontent.com/files/a6fbb3c81a7df7e4c908930ca2ed450a417ef08c5f51937b581e3b5e36497a35061ef453aabca93c51049c02544df3c7fd7b1d84dcedfc175b8c56145bcb0ecb","name":"ultrarunningyearpt@1.csv","type":"text/csv","lastModified":1591302343406.894,"size":65330}] 2e:{"title":"Insane Athleticism: Ultramarathon Edition","cells":[{"id":605,"value":"md`# Insane Athleticism: Ultramarathon Edition`","pinned":false,"hidden":false,"mode":"ojs"},{"id":608,"value":"md` ## Index\n- [Introduction](#introduction)\n- [Gender Performance Correlation](#genderperformancecorrelation)\n * [Analyzing Age Bias](#analyzingagebias)\n * [Performance by Gender](#performancebygender)\n * [Applying Logistic Regression](#applyinglogisticregression) \n- [How Fast do UltraMarathoners Run](#Speed)\n- [Comparing Ultramarathon Performance and Runner's Age](#agevsperformance)\n- [Ultra Marathon Participation](#ultramarathonparticipation)\n- [Conclusion](#conclusion)\n- [Appendix](#appendix)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":743,"value":"introduction = md`## Introduction\n\nWhether you're an avid runner or you've successfully avoided running since your high school gym classes, we can all appreciate impressive feats of athleticism when we see them. It's hard not to watch an ultramarathon documentary and not feel an immense feel of awe as you watch people push themselves to lengths that many of us could scarcely ever dream of.\n\nIt's not uncommon for an ultramarathon to go for 100km, with some of the more famous ones going even longer, stretching days in length. What is common however, is to see people of all ages, from all over the world, competeting. There's major competitions in nearly every country, and regular amateur events are even more common. \n\nThe sheer volume of ultramarathons taking place across the world, every year, with people of all ages competeting led us to conduct this exploration into the world of ultramarathons, seeking to learn about the kinds of people who can complete these insane feats of athleticism.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":601,"value":"genderperformancecorrelation = md`## Gender Performance Correlation`","pinned":false,"hidden":false,"mode":"ojs"},{"id":592,"value":"analyzingagebias = md`### Analyzing Age Bias`","pinned":false,"hidden":false,"mode":"ojs"},{"id":260,"value":"viewof gen = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":238,"value":"chart = {\n const svg = d3.create(\"svg\").attr(\"viewBox\", [0, 0, width, height]);\n\n svg\n .append(\"g\")\n .attr(\"fill\", \"steelblue\")\n .selectAll(\"rect\")\n .data(bins)\n .join(\"rect\")\n .attr(\"x\", d => x(d.x0) + 1)\n .attr(\"width\", d => Math.max(0, x(d.x1) - x(d.x0) - 1))\n .attr(\"y\", d => y(d.length))\n .attr(\"height\", d => y(0) - y(d.length));\n\n svg.append(\"g\").call(xAxis);\n\n svg.append(\"g\").call(yAxis);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":290,"value":"md`The primary question we seek to answer is \"Are men and women significantly different in terms of completion times? The first step to answering this is checking to see if the two gender groups are biased by the metric of age. This is important to see because we want to know if completion time is correlated to gender and not if completion time is correlated to age which might correlate to gender in this particular dataset.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":541,"value":"md`From this chart we can see that the average age of male runners is lower, and that there is less variance in the age of male runners. Additionally we see that there are just far more male runners than female runners. 924 / 1000 of the runners were male, making it difficult to draw adequate conclusions from the graph. However, we can see that the proportion of individuals on either side of is 29 is approximately the same for both genders. 29 is the established peak of the [performance curve](https://content.iospress.com/articles/journal-of-sports-analytics/jsa0021).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":544,"value":"performancebygender = md`### Performance by Gender\nHere we will look at the below data and apply logistic regression to determine if gender is a good indicator of performance. We added an interactive dropdown menu so that the user can see for themselves the affect that age has on performance (mentioned above).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":563,"value":"xAxis2 = g =>\n g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(\n d3\n .axisBottom(x2)\n .ticks(width / 80)\n .tickSizeOuter(0)\n )\n .call(g =>\n g\n .append(\"text\")\n .attr(\"x\", width - margin.right)\n .attr(\"y\", -4)\n .attr(\"fill\", \"currentColor\")\n .attr(\"font-weight\", \"bold\")\n .attr(\"text-anchor\", \"end\")\n .text(marathonDta2.x)\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":566,"value":"yAxis2 = g =>\n g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y2).ticks(height / 40))\n .call(g => g.select(\".domain\").remove())\n .call(g =>\n g\n .select(\".tick:last-of-type text\")\n .clone()\n .attr(\"x\", 4)\n .attr(\"text-anchor\", \"start\")\n .attr(\"font-weight\", \"bold\")\n .text(marathonDta2.y)\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":568,"value":"viewof ageT = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":571,"value":"filterChart2 = {\n const svg = d3.create(\"svg\").attr(\"viewBox\", [0, 0, width, 600]);\n\n svg\n .append(\"g\")\n .attr(\"fill\", \"steelblue\")\n .selectAll(\"rect\")\n .data(bins2)\n .join(\"rect\")\n .attr(\"x\", d => x2(d.x0) + 1)\n .attr(\"width\", d => Math.max(0, x2(d.x1) - x2(d.x0) - 1))\n .attr(\"y\", d => y2(d.length))\n .attr(\"height\", d => y2(0) - y2(d.length));\n\n svg.append(\"g\").call(xAxis2);\n\n svg.append(\"g\").call(yAxis2);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":573,"value":"applyinglogisticregression = md`### Applying Logistic Regression\nHere we applied logistic regression to the data in python using the following steps:\n-Parsed time data to get time in seconds\n-Created new dataset using all female rows and the same number of male rows\n-Split the dataset into testing and validation\n-Parsed the results of the testing into a confusion matrix and useful metrics\n\nYou can view the exact Python script as it is attached with the name: \"MarathonGenderParsing.pdf\"`","pinned":false,"hidden":false,"mode":"ojs"},{"id":575,"value":"md`\n| Metric | Score |\n|-----------|---------|\n| Accuracy | 0.48333 |\n| Recall | 1.0 |\n| Precision | 0.48333 |\n| Fscore | 0.65168 |`","pinned":false,"hidden":false,"mode":"ojs"},{"id":579,"value":"md`As we can see, this model was not particularly effective. This indicates that males and females *do not* seem to have significantly different running times.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":753,"value":"Speed = md`## How Fast do UltraMarathoners Run?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":794,"value":"md`What is the spread in atheticism for ultramarathoners like? Running 100km is a feat in itself, but the speed at which these runners race makes it that much more impressive. A wide range of athletes take part in ultramarathons, making the distribution of runners by speed widespread, but generally skewed left towards times ranging from 5-7.5mph.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":405,"value":"barChart","pinned":false,"hidden":false,"mode":"ojs"},{"id":797,"value":"speed_dist_gender = md`### Speed Distribution by Gender`","pinned":false,"hidden":false,"mode":"ojs"},{"id":800,"value":"md`We also may want to consider, how does the skew of this distribution vary with gender?`","pinned":true,"hidden":false,"mode":"ojs"},{"id":476,"value":"viewof selectedGender2 = select({\n title: \"Gender for Visualization\",\n options: [\"M\", \"F\", \"All\"],\n value: \"All\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":474,"value":"gender_chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":802,"value":"md`As you can see, females tend to be more highly concentrated in the lower end of the spectrum for speed. It is also important to be noted than a higher number of males participate in the race, making their range of speeds have a wider margin.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":758,"value":"Speed_by_place = md`### Speed By Place`","pinned":false,"hidden":false,"mode":"ojs"},{"id":805,"value":"md`Now we may want to consider, how drastically does the speed of the racer change, as their finishing placement changes?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":428,"value":"viewof selectedGender = select({\n title: \"Gender for Visualization\",\n options: [\"M\", \"F\"],\n value: \"M\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":413,"value":"rank_chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":810,"value":"md`The severity of the drop in speed as the race goes on shows how runners earlier in the race tended to be further outliers than any other athletes. The fastest runners in the first through fourtieth place tend to show a fairly drastic drop in speed. The rest of the runners through the end of the race show a fairly steady difference. \n\nThis also visually represents the competition at the time of each finisher. The faster runners were less close in speed to those finishing before and after them, than those who finished in the later portions of the race.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"agevsperformance = md`## Comparing Ultramarathon Performance and Runner's Age`","pinned":false,"hidden":false,"mode":"ojs"},{"id":675,"value":"md`Our next question was whether a runner's age had any significant affect on their performance. To explore this, we took a popular ultramarathon race in Mexico, the Antelope trail, and plotted runner's age versus their performance.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":578,"value":"AntelopeLegend","pinned":false,"hidden":false,"mode":"ojs"},{"id":308,"value":"viewof selectedGenderKyle = select({\n title: \"Gender for Visualization\",\n options: [\"M\", \"F\"],\n value: \"M\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":400,"value":"agePerformanceChartElaborate","pinned":false,"hidden":false,"mode":"ojs"},{"id":381,"value":"md`As we can see above, there is very little correlation between a runner's age and their performance in the ultramarathon. A runner is no more likely to run faster if they're in their 20s than if they were in their 60s.\n\nThis propels our fascination of ultramarathon runners even further, as it shows that people from all stages of life have the ability to perform these incredible feats of athleticism and enduranace.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"ultramarathonparticipation = md`## Ultra Marathon Participation`","pinned":false,"hidden":false,"mode":"ojs"},{"id":327,"value":"viewof selectedYear = select({\n title: \"Year for Visualization\",\n options: allGroup,\n value: 2020\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":158,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":365,"value":"md`From the bar graph we can see a major right skew for each year meaning that while there are a couple large ultra marathons with several hundred finishers, most have less than 100 finishers with the least even being a race with no finishers. Each year appears to have similar amounts of participation with a spike in 2019 for the largest event. 2020, as it is the current year and we have been inside, has the least number of bars. Below is a histogram of all the events by number of finishers. This gives us an even better visual to show the skew in event participation. The majority of all the events have less than 100 finishers, with the largest bar representing the events with 0-20 finishers. We can conclude that ultramarathons are usually not largely participated events with only a couple occcuring with large numbers of finishers.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":641,"value":"hist = {\n const svg = d3.create(\"svg\").attr(\"viewBox\", [0, 0, width, height]);\n\n svg\n .append(\"g\")\n .attr(\"fill\", \"steelblue\")\n .selectAll(\"rect\")\n .data(binsy)\n .join(\"rect\")\n .attr(\"x\", d => xy(d.x0) + 1)\n .attr(\"width\", d => Math.max(0, xy(d.x1) - xy(d.x0) - 1))\n .attr(\"y\", d => yy(d.length))\n .attr(\"height\", d => yy(0) - yy(d.length));\n\n svg.append(\"g\").call(xAxisy);\n\n svg.append(\"g\").call(yAxisy);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":773,"value":"conclusion = md`## Conclusion\n\nIt's easy to look at athletes and assume that we could never reach the same level as them, or our time for athleticism has passed, but we hope that this look into ultramarathon runners, some of the most impressive athletes, shows that one's sex or age has no bearing on their ability to be athletic. Great runner compete every day, across the world, regardless of age.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":28,"value":"appendix = md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs"},{"id":234,"value":"marathonDta = Object.assign(\n d3.csvParse(\n await FileAttachment(\"marathonData.csv\").text(),\n ({ rate, gender }) => {\n if (gender == gen) {\n return rate;\n }\n }\n ),\n { x: \"Age\", y: \"Frequency\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"margin = ({ top: 50, right: 30, bottom: 40, left: 40 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"height = 500","pinned":false,"hidden":false,"mode":"ojs"},{"id":122,"value":"import { legend } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":305,"value":"import { select } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":397,"value":"import {\n agePerformanceChartElaborate,\n AntelopeLegend\n} from \"@info474/interactivity/5\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"d3 = require(\"d3@5\", \"d3-array@2\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":171,"value":"ultraRunData = {\n let rawData = d3.csvParse(await FileAttachment(\"ultrarunning.csv\").text());\n\n let resultData = rawData.map(d => {\n return {\n year: d.year,\n finishers: Number(d.finishers),\n event: d.event\n };\n });\n\n return resultData;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":321,"value":"chart2_y = d3\n .scaleLinear()\n .domain([0, 15])\n .range([height - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs"},{"id":312,"value":"chart2_x = d3\n .scaleLinear()\n .domain([4.6, 10.1])\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs"},{"id":154,"value":"barY = d3\n .scaleLinear()\n .domain(barYDomain)\n .range([height - margin.bottom, margin.top])\n .nice()","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"datay = Object.assign(\n d3.csvParse(\n await FileAttachment(\"ultrarunning.csv\").text(),\n ({ finishers }) => +finishers\n ),\n { x: \"Finishers\", y: \"Events\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":627,"value":"binsy = d3\n .histogram()\n .domain(xy.domain())\n .thresholds(xy.ticks(60))(datay)","pinned":false,"hidden":false,"mode":"ojs"},{"id":631,"value":"xy = d3\n .scaleLinear()\n .domain(d3.extent(datay))\n .nice()\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs"},{"id":629,"value":"yy = d3\n .scaleLinear()\n .domain([0, d3.max(binsy, d => d.length)])\n .nice()\n .range([height - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs"},{"id":634,"value":"xAxisy = g =>\n g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(\n d3\n .axisBottom(xy)\n .ticks(width / 100)\n .tickSizeOuter(0)\n )\n .call(g =>\n g\n .append(\"text\")\n .attr(\"x\", width - margin.right)\n .attr(\"y\", -4)\n .attr(\"fill\", \"currentColor\")\n .attr(\"text-anchor\", \"end\")\n .text(datay.x)\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":638,"value":"yAxisy = g =>\n g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(yy).ticks(height / 40))\n .call(g => g.select(\".domain\").remove())\n .call(g =>\n g\n .select(\".tick:last-of-type text\")\n .clone()\n .attr(\"x\", 4)\n .attr(\"text-anchor\", \"start\")\n .text(datay.y)\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":153,"value":"barX = d3\n .scaleBand()\n .domain(\n ultraRunData\n .filter(function(d) {\n return d.year == selectedYear;\n })\n .map(d => d.event)\n )\n .range([margin.left, width - margin.right])\n .padding(0.1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":167,"value":"baryAxis = g =>\n g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(barY))\n .call(g => g.select(\".domain\").remove())","pinned":false,"hidden":false,"mode":"ojs"},{"id":106,"value":"_ = require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":396,"value":"import { barChart } from \"@mackenziehutchison/interactivity\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":164,"value":"barxAxis = g =>\n g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(d3.axisBottom(barX).tickSizeOuter(0))","pinned":false,"hidden":false,"mode":"ojs"},{"id":207,"value":"function zoom(svg) {\n const extent = [\n [margin.left, margin.top],\n [width - margin.right, height - margin.top]\n ];\n\n svg.call(\n d3\n .zoom()\n .scaleExtent([1, 25])\n .translateExtent(extent)\n .extent(extent)\n .on(\"zoom\", zoomed)\n );\n\n function zoomed() {\n barX.range(\n [margin.left, width - margin.right].map(d => d3.event.transform.applyX(d))\n );\n svg\n .selectAll(\".bars rect\")\n .attr(\"x\", d => barX(d.event))\n .attr(\"width\", barX.bandwidth());\n svg.selectAll(\".x-axis\").call(barxAxis);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":410,"value":"import { rank_chart } with {\n selectedGender\n} from \"@mackenziehutchison/interactivity\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":186,"value":"allGroup = d3\n .map(ultraRunData, function(d) {\n return d.year;\n })\n .keys()","pinned":false,"hidden":false,"mode":"ojs"},{"id":181,"value":"barYDomain = [\n d3.min(ultraRunData, d => d.finishers),\n d3.max(ultraRunData, d => d.finishers)\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":299,"value":"speed_dist = d3.csvParse(await FileAttachment(\"runners_by_speed.csv\").text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":470,"value":"import { gender_chart } with {\n selectedGender2\n} from \"@mackenziehutchison/interactivity\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":332,"value":"chart2_yAxis = g =>\n g.attr(\"transform\", `translate(${margin.left},0)`).call(d3.axisLeft(chart2_y))","pinned":false,"hidden":false,"mode":"ojs"},{"id":348,"value":"d3.max(speed_dist, d => d.avg_sp_rd)","pinned":false,"hidden":false,"mode":"ojs"},{"id":346,"value":"d3.min(speed_dist, d => d.avg_sp_rd)","pinned":false,"hidden":false,"mode":"ojs"},{"id":325,"value":"chart2_xAxis = g =>\n g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(d3.axisBottom(chart2_x))","pinned":false,"hidden":false,"mode":"ojs"},{"id":547,"value":"marathonDta2 = Object.assign(\n d3.csvParse(\n await FileAttachment(\"marathonData.csv\").text(),\n ({ rate, final }) => {\n if (parseInt(rate) > 30 && ageT == \"Over 30\") {\n var hour = final.split(\":\")[0];\n var minute = final.split(\":\")[1];\n var second = final.split(\":\")[2];\n return parseInt(3600 * hour + 60 * minute + second);\n } else if (parseInt(rate) <= 30 && ageT == \"Under 30\") {\n var hour = final.split(\":\")[0];\n var minute = final.split(\":\")[1];\n var second = final.split(\":\")[2];\n return parseInt(3600 * hour + 60 * minute + second);\n }\n }\n ),\n { x: \"Final\", y: \"Frequency\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":244,"value":"bins = d3\n .histogram()\n .domain(x.domain())\n .thresholds(x.ticks(40))(marathonDta)","pinned":false,"hidden":false,"mode":"ojs"},{"id":247,"value":"x = d3\n .scaleLinear()\n .domain(d3.extent(marathonDta))\n .nice()\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs"},{"id":558,"value":"y2 = d3\n .scaleLinear()\n .domain([0, d3.max(bins2, d => d.length)])\n .nice()\n .range([height - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"y = d3\n .scaleLinear()\n .domain([0, d3.max(bins, d => d.length)])\n .nice()\n .range([height - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs"},{"id":240,"value":"yAxis = g =>\n g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y).ticks(height / 40))\n .call(g => g.select(\".domain\").remove())\n .call(g =>\n g\n .select(\".tick:last-of-type text\")\n .clone()\n .attr(\"x\", 4)\n .attr(\"text-anchor\", \"start\")\n .attr(\"font-weight\", \"bold\")\n .text(marathonDta.y)\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":251,"value":"xAxis = g =>\n g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(\n d3\n .axisBottom(x)\n .ticks(width / 80)\n .tickSizeOuter(0)\n )\n .call(g =>\n g\n .append(\"text\")\n .attr(\"x\", width - margin.right)\n .attr(\"y\", -4)\n .attr(\"fill\", \"currentColor\")\n .attr(\"font-weight\", \"bold\")\n .attr(\"text-anchor\", \"end\")\n .text(marathonDta.x)\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":554,"value":"bins2 = d3\n .histogram()\n .domain(x2.domain())\n .thresholds(x2.ticks(100))(marathonDta2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":562,"value":"x2 = d3\n .scaleLinear()\n .domain(d3.extent(marathonDta2))\n .nice()\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs"},{"id":481,"value":"agePerformanceChartElaborate.update(selectedGenderKyle)","pinned":false,"hidden":false,"mode":"ojs"},{"id":437,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}