2b:null 2c:["$","title",null,{"children":"Females and privately employed individuals beware! | Fengrui Gan (Simon) | Observable"}] 31:T6b0,{ const brush = vl.selectInterval().encodings('x'); const click = vl.selectPoint().encodings('color'); const scale = { domain: ['Male', 'Female'], range: ['#08A04B','#E9CD00'] }; const workTypeScale = { domain: ['Private', 'Self-employed', 'Govt_job'], range: ['#08A04B','#E9CD00', '#427BF5'] }; const plot1 = vl.markPoint({filled: true}) .data(perWorkTypeData) .encode( vl.color().value('lightgray') .if(brush, vl.color().fieldN('gender').scale(scale).title('Gender')), vl.size().fieldQ('avg_glucose_level').scale({domain: [getLowGluc(glucoseLevel), getHighGluc(glucoseLevel)]}).title('Average Glucose Level'), vl.order().fieldQ('avg_glucose_level').sort('descending'), vl.x().fieldQ('age').axis({title: 'Age'}), vl.y().fieldQ('bmi').scale({domain: [10, 50]}).axis({title: 'Body Mass Index'}) ) .width(width) .height(500) .params(brush) .transform(vl.filter(click)); const plot2 = vl.markBar() .data(perWorkTypeData) .encode( vl.color().if(click, vl.color().fieldN('gender')).value('lightgray') .scale(scale).title('Gender'), vl.x().count().title('Number of Male & Female'), vl.y().fieldN('gender').scale({domain: scale.domain}).title('Gender') ) .width(width) .params(click) .transform(vl.filter(brush)); const plot3 = vl.markBar() .data(workTypeMap) .encode( vl.x().fieldQ('count'), vl.y().fieldN('workType').scale({domain: workTypeScale.domain}).title('Work Type') ) .width(width); const plot12 = vl.vconcat(plot1, plot2) .autosize({type: 'fit-x', contains: 'padding'}); return vl.vconcat(plot12, plot3).render(); }32:T111b,### Example Insights * Self Employed workers have higher chances of brain stroke as their jobs include not just getting the work done but also taking care of other aspects required to run company. *[SELECTED]* * Female tend to be more susceptible to having brain strokes as they tend to overthink. * People with higher blood glucose level should have higher body mass index as the extra glucose in blood is directly converted to fat. ### Considerations : * I will need enough data for different age groups of patients to determine the co-relation between blood glucose level and their body mass index. * I will also need the aggregate of male and female to determine if the female was actually at a higher risk of getting a brain stroke. * I will then need to group this data based on different work-type in order to count the number of male and female working in a particular sector. * In regards with idiom, I will use a scatter plot graph to plot blood glucose level in male and female of different ages across their body mass indices which will be grouped by their profession. I will use another idiom, a bar plot to count the number of male and female of different age groups. The scatter plot will be intractable in the sense that we will be able to filter out a selected age range. ### Tasks (Why?) : * **T1 :** **[Identify]** which profession and which gender is at a higher risk of brain stroke. * **T2 :** **[Compare]** the number of male and female working in different professions and their susceptibility to having brain stroke. ### Data Abstraction (What?) : * **Dataset type:** Tabular * **Attributes used:** * Age * Attribute Type - Ordinal * Ordering Directions - Sequential * Body Mass index * Attribute Type - Quantitative * Ordering Directions - Sequential * Average Glucose Level * Attribute type - Quantitative * Ordering Directions - Sequential * Gender * Attribute type - Categorical, * Work Type * Attribute type - Categorical * **Derived Attributes:** A attributed named "Number of Male & female" which is an aggregation of number of males and females grouped by age and work type. * Attribute Type - Quantitative * Orientation - Sequential * **Availability:** Static * **Source:** [Brain Stroke Data](https://www.kaggle.com/datasets/zzettrkalpakbal/full-filled-brain-stroke-dataset) ### Marks & Channels (How?) : * The data representation is in a 2-D format. * **Marks:** Points and Lines * **Channels:** - The points are colored, based on the gender, and sized, based on the average glucose level. - **Colors** : To identify the gender of the patient. * Attribute : Gender - Categorical * Task : T1 - **Size** : To identify the average level of glucose in blood. * Attribute : Average Glucose Level - Quantitative * Task : T1 - **Length** : To compare the number of male and female patients. * Attribute : Number of Mal and Female - Quantitative * Task : T2 - The x coordinate of the scatter plot represents the body mass index where as the y coordinate represents the ages. - The bar chart is vertically represented with y axis representing the gender. - The x axis of the bar graph represents the total number of male and female in a particular selection. ### Target Audience (Users) : * All Individuals trying to figure out their chance of brain stroke. * Fitness industry to target the right audience for their business. * Pharmaceutical and wellness companies to create audience centered medicines. ### Real Insights : * Private sectors employee the most number of people and hence by ratio they are more susceptible to having a brain stroke. * In general female are at a higher risk of brain stroke, irrespective of their work type or blood glucose level. * In case of normal glucose levels, privately employed individuals in age groups 54-61 and 69-78 (approx) male are at a higher risk of brain stroke. * In case of self employed and government workers male with different glucose level are more susceptible to brain stroke at a younger age. * The range of body mass index is higher amongst people with normal blood glucose level ruling out the fact that high blood glucose level leads to higher BMI. 33:[{"id":"262acb643709616c735f002f50e515a166def1a5a1026efbeb2b956e076178616d5d75e4bf63b393a12aeffd8232ec15cea16358efbc5229886371e379d9fd45","href":"https://static.observableusercontent.com/files/262acb643709616c735f002f50e515a166def1a5a1026efbeb2b956e076178616d5d75e4bf63b393a12aeffd8232ec15cea16358efbc5229886371e379d9fd45","name":"brain-stroke.csv","type":"text/csv","lastModified":1675557892176.685,"size":290771}] 2f:{"id":"0e8eae0a133e3ff5","slug":null,"title":"Females and privately employed individuals beware!","thumbnail":"0ec53921b7bc7804a9433e891d9b7bbbd1d3ea239284a4d949ca51c86cb6c7bc","access_level":"public","create_time":"$D2023-02-05T00:44:52.176Z","update_time":"$D2023-02-05T00:44:52.176Z","view_time":null,"first_public_time":"$D2023-02-05T00:44:52.176Z","has_unpublished_edits":null,"fork":{"id":"048f9d3c5dfa86f8","slug":null,"title":"Females and privately employed individuals beware!","owner":{"id":"29c4858a5f0f02f2","login":"parthivi-varshney","name":""}},"stars":0,"starred":false,"owner":{"type":"team","id":"a48bdab08888437c","login":"fengruigan","name":"Fengrui Gan (Simon)","avatar_url":"https://avatars-next.observableusercontent.com/73027a1b3f09dc8313d125b3d1f54464475e75f162e6d1db0259249352073c1d","tier":"starter_2024"},"version":467,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"6d58559bc5b22c08","login":"fengruigan","name":"Fengrui Gan (Simon)","avatar_url":"https://avatars-next.observableusercontent.com/73027a1b3f09dc8313d125b3d1f54464475e75f162e6d1db0259249352073c1d","tier":null}],"members":[{"type":"individual","id":"6d58559bc5b22c08","login":"fengruigan","name":"Fengrui Gan (Simon)","avatar_url":"https://avatars-next.observableusercontent.com/73027a1b3f09dc8313d125b3d1f54464475e75f162e6d1db0259249352073c1d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Females and privately employed individuals beware!","cells":[{"id":177,"value":"# Females and privately employed individuals beware! \n*You are at a higher risk of brain stroke.*\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":287,"value":"Author : [Parthivi Varshney](https://observablehq.com/@pv1995)
\nCourse : [Data Visualization / Information Visualization](https://johnguerra.co/classes/data_visualization_spring_2023/)
\nInstructor : [John Alexis Guerra Gomez](https://observablehq.com/@john-guerra)
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":368,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":190,"value":"### Introduction\nBrain strokes are most common cause of adult disability. \nEvery year, more than 795,000 people in the United States have a stroke with about 160,000 dying from stroke-related causes.\nSome of the common causes of stroke include : stress, diabetes, high blood pressure, smoking and so on.
\nThe graphs below use the [Brain Stroke Data](https://www.kaggle.com/datasets/zzettrkalpakbal/full-filled-brain-stroke-dataset) to visualize the patients statistics and analyze the possibility of brain stroke in patients based on their profession and blood glucose level. The visualization also provides an insight on the ratio of men and women being impacted by the risk of brain stroke.
\n[Video Explanation](https://www.youtube.com/watch?v=SWe9zj0A7Lk)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":372,"value":"

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Brain Stroke Visualization

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":165,"value":"viewof workType = Inputs.radio([\"Private\", \"Self-employed\", \"Govt_job\"], {\n label: \"Work-Type\",\n value: \"Private\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"viewof glucoseLevel = Inputs.radio([\"Normal\", \"Prediabetic\", \"Diabetic\", \"All\"], {\n label: \"Glucose\",\n value: \"All\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":374,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":200,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":376,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":317,"value":"function getHighGluc(input) {\n switch(input){\n case \"Normal\":\n return 99;\n case \"Prediabetic\":\n return 125;\n case \"Diabetic\":\n return 230;\n case \"All\":\n return 300;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":315,"value":"function getLowGluc(input) {\n switch(input){\n case \"Normal\":\n return 52;\n case \"Prediabetic\":\n return 100;\n case \"Diabetic\":\n return 126;\n case \"All\":\n return 50;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":439,"value":"perWorkTypeData = brain_data\n .slice()\n .filter(\n (f) =>\n f.work_type == workType &&\n f.avg_glucose_level >= getLowGluc(glucoseLevel) &&\n f.avg_glucose_level <= getHighGluc(glucoseLevel)\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":415,"value":"workTypeMap = [...new Set(brain_data.map(el => el.work_type).filter(el => el !== \"children\"))].map(type => {\n return {\n workType: type,\n count: brain_data.filter(nestEl => nestEl.work_type === type).length\n }\n})\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"brain_data = FileAttachment(\"brain-stroke.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":0,"value":"import { vl } from \"@vega/vega-lite-api-v5\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 30:"$2f:body"