25:["$","title",null,{"children":"Group Assignment #2: Your Data on Observable (Milestone) | Frankfurt school of FInance and Management | Observable"}] 49:[{"id":"9b93482ae62b1e9766ae3cbf38d161bcf6898c20647d17916dd0a3859cc6c7dc97add0a60f98e159e783d12d1abb8625b16fec2d110535a077276a67e38aa13f","href":"https://static.observableusercontent.com/files/9b93482ae62b1e9766ae3cbf38d161bcf6898c20647d17916dd0a3859cc6c7dc97add0a60f98e159e783d12d1abb8625b16fec2d110535a077276a67e38aa13f","name":"SuperStoreOrders@1.csv","type":"text/csv","lastModified":1684656217210.003,"size":10806133}] 29:{"id":"1eebbf5bca6be631","slug":null,"title":"Group Assignment #2: Your Data on Observable (Milestone)","thumbnail":"2fc92f973984f011de97d2961d3233edaf5c888248776fd4bb963c951094dfe3","access_level":"public","create_time":"$D2023-05-20T16:27:57.457Z","update_time":"$D2023-05-27T09:30:51.543Z","view_time":null,"first_public_time":"$D2023-05-20T16:27:57.457Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"f2c3ccac33517427","login":"frankfurtschooll","name":"Frankfurt school of FInance and Management ","avatar_url":"https://avatars-next.observableusercontent.com/83c467064e970da19a9be7c492cfcbddf9e3b023a997e36ca546d97c2e995bb5","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":1243,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"63609b8ec83dc4b8","login":"jamsh1d","name":"Jamshid Tukhtasinov","avatar_url":"https://avatars-next.observableusercontent.com/ea8b6baeee6d1d736a06cc9e28dee37bd1697440728f081289208411a9b458de","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"f2c3ccac33517427","login":"frankfurtschooll","name":"Frankfurt school of FInance and Management ","avatar_url":"https://avatars-next.observableusercontent.com/83c467064e970da19a9be7c492cfcbddf9e3b023a997e36ca546d97c2e995bb5","tier":"starter_2024","role":"editor"},{"type":"individual","id":"63609b8ec83dc4b8","login":"jamsh1d","name":"Jamshid Tukhtasinov","avatar_url":"https://avatars-next.observableusercontent.com/ea8b6baeee6d1d736a06cc9e28dee37bd1697440728f081289208411a9b458de","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":{"id":"7d6c3d5137621cb3","slug":"group-assignment-2-group-data-on-observable-milestone","title":"Group Assignment #2: Group Data on Observable (Milestone)","owner":{"id":"f2c3ccac33517427","login":"frankfurtschooll","name":"Frankfurt school of FInance and Management "}},"type":"notebook","body":{"titling":"auto","title":"Group Assignment #2: Your Data on Observable (Milestone)","cells":[{"id":52,"value":"# Group Assignment #2: Your Data on Observable (Milestone)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":862,"value":"## Deadline\n\nMonday, May 22nd, 23:59","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":82,"value":"## Goal\n\nApply the techniques you learned to your own data, so you can prepare and visualize it on Observable.\n\nIf your dataset is not compatible with a technique, please find another dataset for that part so you can demonstrate what you have learned. Although you should try to avoid this as much as you can because you can (partly) use this assignment as an opportunity to work on your final report if you can use your own group's dataset.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":421,"value":"## Grading\n\n- **Performance points: ${yourDataOnObservableAssignmentPoints}**\n- Scale: See below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":356,"value":"## The Task","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":67,"value":"- There should be at least:\n 1. A data import from CSV, JSON, URL, etc. [+${performancePoints.dataImport} performance points]\n 2. Some data wrangling with `Wrangler()` [+${performancePoints.dataWrangling} performance points]\n 3. Reuse of at least one visualization from Observable community with your data [+${performancePoints.chartReuse} performance points]\n 4. Three charts made with Plot [+${performancePoints.chartMaking} performance points]\n 5. Code for the charts or data wrangling should not be overly terse (e.g., not merely 1-2 lines of code; inflating your line number using formatting tricks without putting in a meaningful amount of effort does not count) [-${performancePenalties.tooLittleEffortForCharts} or more performance points deducted, depending of the number of occurrences]","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":393,"value":"- [+${performancePoints.excellence} performance point] is reserved for excellence and wholesomeness. E.g., if you made a technically impressive preprocessing pipeline relevant for visualization, conducted an interesting visual analysis, showed sound reasoning, had a great story in which visuals and explanations came together in a cohesive whole, wrote and coded eloquently, and, importantly, designed visuals adhering to best practices, any of these dimensions can increase the performance points significantly.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":121,"value":"- Although a cohesive story would be desirable and count towards excellence, it is not a requirement for excellence at this point; whether you understood the techniques taught is more important. However, if you spend the effort in putting your analyses in a cohesive whole, you could use your output, or a part of it, directly for your final report more easily.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":140,"value":"- The structure of your notebook is up to you. It can be a single notebook that covers all your analyses, or it could be collection that consists of several notebooks. Messy notebooks or collections could be viewed from the context of (not) clean text/code (see below), and may cost performance point(s). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":25,"value":"- Please try to write clean text and clean code [+${performancePoints.cleanTextAndCode} performance point]:\n - Explain your intention on top of every cell, briefly (e.g., 'import dataset from ...')\n - No cryptic variable names (e.g., `z = x/y` or `dataset5 = wdx[0][0]`; instead, write `aspectRatio = rectangleWidth/rectangleHeight` or `agesDataset[0][0]`)\n - No overly long explanations or cells (not very applicable to Plot, but still, try to aspire for conciseness.)\n - You don't need to be too anxious about this as long as you spend some effort in writing understandable text and code; you are not expected to write code as clear as a professional programmer or prose as concise as of an experienced writer's. These performance points will be (partly) withheld if getting through your text and code turns out to be more challenging than it has been for your peers' text/code.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1024,"value":"superstore_initial = await FileAttachment(\"SuperStoreOrders@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"superstore_initial","database":"$undefined"},{"id":1006,"value":"import { Wrangler, op } from \"@observablehq/data-wrangler\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1017,"value":"Wrangler(superstore_initial)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1029,"value":"superstore_filtered = aq.from(superstore_initial)\n\t.select('ship_date','ship_mode','segment','state','country','market','region','category','sub_category','product_name','sales','quantity','discount','profit','shipping_cost','order_priority','year')\n\t.filter(d => d[\"country\"] === \"United States\")\n\t.sample(20000)\n\t.objects() ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1237,"value":"viewof sort_toggle = Inputs.toggle({label: \"Sort States by Magnitude\", value: false})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1037,"value":"Plot.plot({\n marginBottom: 100,\n x: {label: \"sales by sub-category\", tickRotate: 90},\n y: {grid: true},\n marks: [\n Plot.barY(superstore_filtered,\n Plot.groupX({y: \"sum\"}, {x: \"sub_category\", y: \"sales\", \n fill: \"sub_category\",\n sort: sort_toggle ? {x: \"y\", reverse: true} : \"\"}))\n\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1149,"value":"Plot.plot({\n x: {\n grid: true,\n tickFormat: d3.format(\".0f\")\n },\n y: {\n grid: true\n },\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(superstore_filtered, \n Plot.groupX({y: \"sum\"}, {x: \"year\", y: \"sales\", stroke: \"salmon\"})\n),\n Plot.lineY(superstore_filtered, \n Plot.groupX({y: \"sum\"}, {x: \"year\", y: \"profit\", stroke: \"dodgerblue\"}))\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1173,"value":"Plot.plot({\n x: {\n label: \"shipping cost\"\n },\n y: {\n label: \"profit\",\n grid: true \n },\n marks: [\n Plot.dot(superstore_filtered, {x: \"shipping_cost\", y: \"profit\",fill:\"steelblue\"}),\n\n Plot.linearRegressionY(superstore_filtered, {\n x: \"shipping_cost\", \n y: \"profit\", \n stroke: \"red\",\n ci: 0.95\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"## Getting Help\n\nThe Observable notebooks shared during the class are aimed to serve you as long-term reference. Please consult them (also in the future) in case you experience issues concerning a technique we covered. If you can't find an answer in a notebook, and after searching it online (tip: use 'observablehq' and 'observable plot' when searching the web, instead of 'observable' and 'plot') you can use [Observable forum](https://talk.observablehq.com) or [Stack Overflow](https://stackoverflow.com/questions/tagged/observablehq). The Observable community is very helpful and you usually get an answer to your question within one day on the forum. You can also ask for help from John, of course---also in the future, after the course. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":893,"value":"# Guidelines and Tips","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":897,"value":"Please see [this notebook](https://observablehq.com/d/4de3a434d8cd531b) for guidelines and tips on how to create good content for this assignment.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":868,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":870,"value":"import {yourDataOnObservableAssignmentPoints} from \"2cd741146f68dfd1\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":931,"value":"usedPoints = d3.sum(Object.values(performancePoints).map(d => d))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":934,"value":"availablePoints = yourDataOnObservableAssignmentPoints","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":909,"value":"performancePoints = ({\n dataImport : availablePoints / 5,\n dataWrangling: availablePoints / 5,\n chartReuse: availablePoints / 5,\n chartMaking: availablePoints / 5,\n excellence: availablePoints / 10,\n cleanTextAndCode: availablePoints / 10\n} )","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":919,"value":"performancePenalties = ({\n tooLittleEffortForCharts: availablePoints / 5\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W49","stdlib":"1"}} 24:null