2d:null 2e:["$","title",null,{"children":"Computational Essay Project Template | Sandy | Observable"}] 33:T52e,md` ## Step 2: Choose a dataset For next week, please choose and explore a dataset to visualize. Don't worry for now about whether this is the one dataset you want to work on for the rest of the semester. Don't know where to start? Consider choosing: - [Center for Systems Science and Engineering (CSSE) COVID Data](https://github.com/CSSEGISandData/COVID-19) from John Hopkins University - [York County Probate Data](https://repository.upenn.edu/mead/26/) which basically includes ~700 inventories of what people had in their homes (1700-1800) - [Confederate Monuments collected by the Souther Poverty Law Center](https://docs.google.com/spreadsheets/d/17ps4aqRyaIfpu7KdGsy2HRZaaQiXUfLrpUbaR9yS51E/edit#gid=400111512) about which [you can find more information here](https://www.splcenter.org/20190201/whose-heritage-public-symbols-confederacy) - [US protest data from 2017--](https://countlove.org/data/data.csv) compiled by Tommy Leung and Nathan Perkins, about which [you can find more information here](https://countlove.org/faq.html) Or to get a (tiny) glimpse of the data that's out there, [checkout the resources available here.](https://observablehq.com/@bulbil/computational-essay#dataExamples) For our purposes, focus on **clean**, **tabular** datasets with **100 - 200k rows** and include **documentation**. `34:[{"id":"0bc86dc2ee83f0d51b91e12caed150c2454309e2c7c4feed721ab081f5a20d4556ae53aa55e3a2920134927ff0b451010001dffa6899ffe224acc3aed3fe9749","href":"https://static.observableusercontent.com/files/0bc86dc2ee83f0d51b91e12caed150c2454309e2c7c4feed721ab081f5a20d4556ae53aa55e3a2920134927ff0b451010001dffa6899ffe224acc3aed3fe9749","name":"aita_clean.csv.dvc","type":"application/octet-stream","lastModified":1603303633677.026,"size":154},{"id":"0c7b6029154a513787ce0ef8799c47058ca0626d4bef2a64e88fae6bdf4ed6c5d8fc266873040f39b9f0b2ea7cf9d35932be44b20f122b928bca70aed79b689d","href":"https://static.observableusercontent.com/files/0c7b6029154a513787ce0ef8799c47058ca0626d4bef2a64e88fae6bdf4ed6c5d8fc266873040f39b9f0b2ea7cf9d35932be44b20f122b928bca70aed79b689d","name":"candidates_rd2019.csv","type":"text/csv","lastModified":1603305109787.823,"size":10464456},{"id":"13fe105fdd2730a2d0f4bf203e11874ce4781603cf269481c4614beccaf4801d7b317afc6084a5f467fc280f7f5439eac3f0a228248ccc8c7edc5ce650f0f025","href":"https://static.observableusercontent.com/files/13fe105fdd2730a2d0f4bf203e11874ce4781603cf269481c4614beccaf4801d7b317afc6084a5f467fc280f7f5439eac3f0a228248ccc8c7edc5ce650f0f025","name":"aita_clean_appended.csv","type":"text/csv","lastModified":1603461561359.768,"size":846063},{"id":"d96b8820eb86d0ba4cc2d5095fcf6ffe7a7ec4a438bd8edaf490b0440d139541de9ba62cc24e559077e111336d3d5605b0070c9ae0ce228bcf46dd1edabd801a","href":"https://static.observableusercontent.com/files/d96b8820eb86d0ba4cc2d5095fcf6ffe7a7ec4a438bd8edaf490b0440d139541de9ba62cc24e559077e111336d3d5605b0070c9ae0ce228bcf46dd1edabd801a","name":"elected_officials_rd2019.csv","type":"text/csv","lastModified":1603305101475.178,"size":10558712}] 31:{"id":"5f9ea035536b5ccb","slug":"computational-essay-project-template","title":"Computational Essay Project Template","thumbnail":null,"access_level":"public","create_time":"$D2020-10-21T17:23:40.534Z","update_time":"$D2020-11-06T13:55:27.720Z","view_time":null,"first_public_time":"$D2020-10-21T19:14:00.689Z","has_unpublished_edits":null,"fork":{"id":"6a84abf66bf2e0f9","slug":"computational-essay-project-template","title":"Computational Essay Project Template","owner":{"id":"92032f850a4679ab","login":"bulbil","name":"Nabil Kashyap"}},"stars":1,"starred":false,"owner":{"type":"team","id":"2d323f1dc50f72bc","login":"sshen23","name":"Sandy","avatar_url":"https://avatars-next.observableusercontent.com/412df21d381304ce7bbc4296fbbe299d85d2a5624d3eca389a443d8ec894bb30","tier":"starter_2024"},"version":326,"publish_version":326,"role":null,"authored":false,"authors":[{"type":"individual","id":"1a4e72db5dc726a9","login":"sshen23","name":"Sandy","avatar_url":"https://avatars-next.observableusercontent.com/412df21d381304ce7bbc4296fbbe299d85d2a5624d3eca389a443d8ec894bb30","tier":null}],"members":[{"type":"individual","id":"1a4e72db5dc726a9","login":"sshen23","name":"Sandy","avatar_url":"https://avatars-next.observableusercontent.com/412df21d381304ce7bbc4296fbbe299d85d2a5624d3eca389a443d8ec894bb30","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Computational Essay Project Template","cells":[{"id":0,"value":"md`# Computational Essay Project Template`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"md`\n🍴 A forkable 🍴 template for our final computational project notebook. You'll notice very quickly that there is nothing special about this notebook, only a tool to help us along enroute to our final computational essays.\n\nThis is a companion to [our Computational Essay notebook](https://observablehq.com/@bulbil/computational-essay). Please revisit that resource for more detailed description of our basic project steps. To reiterate:\n\n>**A computational essay**:\n\n> - \"performs\" human-readable and machinic text simultaneously\n- is editable/remixable/available\n- does not depend or depends little on any particular platform\n- interprets data through rhetoric and computation\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"md`## ~~Step 1: Get familiar with Observable~~\n✅ Done!\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"md`\n## Step 3: Prototype multiple ways of representing that data\n\nCreate cells for each of the following prompts. The keyword is *preliminary* -- there is no expectation that this exploration be complete or take too much time.\n\n- **Explore your data.** Spend a few minutes scrolling through your data. What do you notice?\n- **Write a paragraph.** In a short paragraph, describe the data, what you know about its source, and what questions you (or anyone) might ask of the data.\n- **Prototypes.** Imagine three different graphical representations of this data thinking about what kinds of questions each prototype might answer or highlight. What is made legible? What is effaced or rendered invisible? What might you need to implement each?\n\n*Wait, prototypes?* Use whatever technology you feel most comfortable using. We actually recommend drawing on paper, taking images with your phone, and uploading those images to your notebooks.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"md ` # 1. AITA Dataset `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"md ` ## The dataset contained in aita_clean.csv has 9 features:\n1. id, a unique string provided by Reddit's API to index every post \n2. timestamp of post creation, in epoch/Unix format\n3. title, a string\n4. body, a string\n5. edited, the timestamp at which a post was edited. If no edits occurred this field is False.\n6. verdict, a string in the set {\"asshole\", \"not the asshole\", \"everyone sucks\", \"no assholes here\")\n7. score, an integer corresponding to the difference between upvotes and downvotes\n8. num_comments, an integer corresponding to the total number of comments (including nested discussion) to the post\n9. is_asshole, a boolean corresponding to whether the verdict is in the set {\"asshole\",\"everyone sucks\"}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"aita = await FileAttachment(\"aita_clean_appended.csv\").text()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":281,"value":"aita_parsed = await d3.csvParse(aita)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"table(aita_parsed)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":308,"value":"aita_parsed.columns","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":318,"value":"viewof aitaBar = vl.markBar() // Make a bar chart\n .data(aita_parsed) // Using the data\n .encode(\n vl.x().fieldN(\"verdict\"), \n vl.y().field(\"Frequency\").axis({ format: \"%\" }) \n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":179,"value":"md ` # 2. Power Demographics Dataset \nSource: [See \"Full Dataset\" under 2018-19 Demographics of Power.](https://wholeads.us/resources/for-researchers/)\n\nPDF: [“The Electability Myth: The Shifting Demographics of Political Power – Report”](https://wholeads.us/the-electability-myth/)\n\nDetails: race and gender data from a dataset of 33,854 federal, state, and local candidates on the November 8, 2018 ballot, and from a Spring, 2019 snapshot of 44,900 elected officials. Reports contain candidates and officeholders by race and gender from the federal to the county level.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":188,"value":"candidates19 = FileAttachment(\"candidates_rd2019.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":214,"value":"md ` # Candidate data: \n1. RDIndex2019\n2. Candidate UUID \n3. State\n4. Office Level \n5. Office Role \n6. OCDID\n7. Electoral District \n8. Office UUID\n8. Office Name (ex. \"solicitor general,\" \"county prosecuting attorney\") \n9. Seat Type (empty?) \n10. Seat ID (empty?) \n11. Number elected (so far only seems to be 1) \n12. Candidate Name \n13. Candidate Party (\"Republican Party\",\"Democratic Party\" ) \n14. Party ID Roll up \n15. White/Non-White (\"White\" vs \"Non-White strings) \n16. Race (\"White\",\"Unknown\",\"American Indian or Alaska Native\",\"Hispanic or Latino\",\"Asian American or Pacific Islander\",\"Multiracial\",\"Black or African American\")\n17. Gender (\"Male\", \"Female\" and) \n18. Winner Y/N (\"True\" or \"False\") \n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":194,"value":"elected_officials19 = FileAttachment(\"elected_officials_rd2019.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"table(candidates19)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"md ` # Elected Officials data: \n1. RDIndex2019 \n2. State \n3. Office Level \n4. Office Role \n5. OCDID \n5. Electoral District \n6. Office Name \n7. Official Name (of elected official) \n8. Official Party (\"Nonpartisan\", \n9. Party Roll Up (\"Independent\",\n10. White/Non-White (\"White\",\"Non-White\",\"Unknown\") \n11. Race (\"White\",\"Unknown\",\"American Indian or Alaska Native\",\"Hispanic or Latino\",\"Asian American or Pacific Islander\",\"Multiracial\",\"Black or African American\")\n12. Gender (\"Male\", \"Female\", and \"Unknown\")\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"table(elected_officials19)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":209,"value":"elected_officials19.columns","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":210,"value":"candidates19.columns","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"viewof canBar = vl.markBar() // Make a bar chart\n .data(candidates19) // Using the data\n .encode(\n vl.x().fieldN(\"Office Role\"), // .sort(vl.fieldQ(\"frequency\").order(\"descending\")), // Letters are ordinal on the x-axis\n vl.y().fieldN(\"Gender\").axis({ format: \"%\" }) // Frequency on the y-axis, formatted as percent\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"md ` ## Appendix \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":101,"value":"import { table } from \"@tmcw/tables/2\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"import { select } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":253,"value":"d3 = require ('d3')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 32:"$31:body"