25:["$","title",null,{"children":"Mini-Project 2 (Temporal Data) | Richard Fu | Observable"}] 49:Ta8b,md`## Questions Your goal is to create 3 independent visualizations of the same data set, each one with the intent of answering the questions stated below. For each numbered visualization, you should be able to create a data visualization that answers **all** of the questions specified. These are the 3 visualizations you should create: - **Visualization 1:** a) How does the amount donated vs. amount received change over time for each country?; b) Are there countries that mostly send or mostly receive and countries that have a similar amount of donations they receive and send?; c) Are there countries that change their role over time? That is, they used to mostly send donations and turn into mostly receiving donations and vice-versa?; d) Are there countries in which you can find a sudden increase ("peak") or a sudden decrease ("valley")? - **Visualization 2:** Focus on the top 10 “Coalesced Purposes” of donations (in terms of amount of disbursement across all countries and all time). What are the top 10 purposes of disbursements (in terms of total amount of disbursement) and how does their relative amount compare over time? E.g., are there purposes that tend to be prominent for a period of time and others that become more prominent during other periods? Hint: looking at the graph one should be able to observe: “Ah! During these years donations were mostly about X but then there were way more donations about Y”. Note: if the purpose is “UNSPECIFIED” it should be removed. - **[OPTIONAL] Visualization 3:** Focusing exclusively on countries that receive donations, how do donations shift geographically over time? Do donations tend to be always in the same regions of the world over the years or they have been shifting over time? Can you build a visualization that shows the “history of donations” so that one can get a sense of which regions of the world have had more need for donations over the years? Note 1: for this visualization you can, if you wish, use interaction (but you don’t have to). Note 2: For this exercise you may want to review the lecture on geographical visualization in which we explain how you can visualize geographical data over time. Note 3: if you want you can aggregate data over a few years (say, group together data at 5 year intervals). For each visualization add a description of your thought process and why you think your solution is *appropriate* and *effective*. Note it is okay to submit more than one solution for a given visualization problem if you think there are some competing solutions that work well. When you do that, make sure to compare the solutions by commenting on their advantages and disadvantages. `4a:T6e3,{ const margin = {top: 10, right: 30, bottom: 20, left: 40}; const visWidth = width - margin.left - margin.right; const visHeight = 500 - margin.top - margin.bottom; const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const g = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); const x = d3.scaleTime() .domain([minYear, maxYear]) .range([0, visWidth]); const y = d3.scaleLinear() .domain([minPrice, maxPrice]).nice() .range([visHeight, 0]); const xAxis = d3.axisBottom(x); const yAxis = d3.axisLeft(y); const color = d3.scaleOrdinal(highlightCountries, d3.schemeCategory10) g.append('g') .attr('transform', `translate(0,${visHeight})`) .call(xAxis); g.append('g') .call(yAxis) .call(g => g.selectAll('.domain').remove()) .append('text') .attr('text-anchor', 'start') .attr('dominant-baseline', 'middle') .attr('fill', 'black') .attr('x', 5) .text('Amount Donated ($MM)'); const line = d3.line() .x(d => x(d.year)) .y(d => y(d.amount)); const series = g.selectAll('.series') .data(aidPerYear) .join('g') .attr('stroke', ([country, info]) => {if(highlightCountries.includes(country)) {return color(country)} else {return 'black'}}) .attr('stroke-opacity', ([country, info]) => {if(highlightCountries.includes(country)) {return 1} else {return 0.1}}) .attr('class', 'series') .append('path') .datum(([countries, donations]) => donations) .attr('fill', 'none') .attr('stroke-width', 1.5) .attr('d', line); return svg.node(); }4b:T490,Notable Areas: Poland typically receives very little, but had a large spike around 1992 of received donations. The USA donated heavily in the period between 1990 and 2002, as did Japan, but donations have tapered off since then. India receives a consistent amount of donations throughout the years We look at the top 5 highest donors based on total net donations and top 5 highest recipients based on total net received. We see from this graph that out of the highest receivers and donors, typically they do not change their status as a donor or receiver, with the exception of the United Kingdom, who briefly received more during the 1980s. We also see that around 1992 there was likely a significant event that occurred in Eastern Europe that led to an influx of donations to Poland and Romania. Countries in East Asia like Thailand and Korea received the most during the late 1990s and early 2000s, and most countries have stopped donating quite as much after 2005. There also seemed to be a world event that led the United States to donated heavily in the early 1980s, though this is an isolated event and is probably indicative of some movement in the US.4c:T62d,{ const margin = {top: 10, right: 30, bottom: 20, left: 40}; const visWidth = width - margin.left - margin.right; const visHeight = 500 - margin.top - margin.bottom; const svg = d3.create('svg') .attr('width', visWidth + margin.left + margin.right) .attr('height', visHeight + margin.top + margin.bottom); const g = svg.append('g') .attr('transform', `translate(${margin.left}, ${margin.top})`); const x = d3.scaleTime() .domain([minYear, maxYear]) .range([0, visWidth]); const y = d3.scaleLinear() .domain([minPrice_purpose, maxPrice_purpose]).nice() .range([visHeight, 0]); const xAxis = d3.axisBottom(x); const yAxis = d3.axisLeft(y); const color = d3.scaleOrdinal(top10Purpose, d3.schemeTableau10) g.append('g') .attr('transform', `translate(0,${visHeight})`) .call(xAxis); g.append('g') .call(yAxis) .call(g => g.selectAll('.domain').remove()) .append('text') .attr('text-anchor', 'start') .attr('dominant-baseline', 'middle') .attr('fill', 'black') .attr('x', 5) .text('Amount Donated ($MM)'); const line = d3.line() .x(d => x(d.year)) .y(d => y(d.amount)); const series = g.selectAll('.series') .data(aidByPurpose) .join('g') .attr('stroke', ([purpose, donations]) => color(purpose)) .attr('class', 'series') .append('path') .datum(([purpose, donations]) => donations) .attr('fill', 'none') .attr('stroke-width', 1.5) .attr('d', line); return svg.node(); }4d:T830,// Copyright 2021, Observable Inc. // Released under the ISC license. // https://observablehq.com/@d3/color-legend function Swatches(color, { columns = null, format, unknown: formatUnknown, swatchSize = 15, swatchWidth = swatchSize, swatchHeight = swatchSize, marginLeft = 0 } = {}) { const id = `-swatches-${Math.random().toString(16).slice(2)}`; const unknown = formatUnknown == null ? undefined : color.unknown(); const unknowns = unknown == null || unknown === d3.scaleImplicit ? [] : [unknown]; const domain = color.domain().concat(unknowns); if (format === undefined) format = x => x === unknown ? formatUnknown : x; function entity(character) { return `&#${character.charCodeAt(0).toString()};`; } if (columns !== null) return htl.html`

${domain.map(value => { const label = `${format(value)}`; return htl.html`

${label}

`; })}

`; return htl.html`

${domain.map(value => htl.html`${format(value)}`)}

`; }4e:[{"id":"55260abbc777c0a3b8fed19f3929dd822fef9d5118b53b76b2176d20782910e599eac919999ea8ee85a60b783fd37082574f6591fd46c0d70ddf9b00df71ce27","href":"https://static.observableusercontent.com/files/55260abbc777c0a3b8fed19f3929dd822fef9d5118b53b76b2176d20782910e599eac919999ea8ee85a60b783fd37082574f6591fd46c0d70ddf9b00df71ce27","name":"countries-50m.json","type":"application/json","lastModified":1681096765228.308,"size":4177662}] 29:{"id":"b40885715364e195","slug":null,"title":"Mini-Project 2 (Temporal Data)","thumbnail":"3f7e52aee9bc1d8fb995de34bceed95f1187aeadc82d78d8b352e164ef7d04a0","access_level":"public","create_time":"$D2023-04-10T03:19:25.228Z","update_time":"$D2023-04-13T16:58:47.824Z","view_time":null,"first_public_time":"$D2023-04-10T03:19:25.228Z","has_unpublished_edits":null,"fork":{"id":"edccd153ac3fa1c6","slug":"mini-project-2-temporal-data","title":"Mini-Project 2 (Temporal Data)","owner":{"id":"0bf1c981cc4de90c","login":"kroneml","name":"Michael Krone"}},"stars":0,"starred":false,"owner":{"type":"team","id":"f20a617e5ad34e39","login":"rjf9225","name":"Richard Fu","avatar_url":"https://avatars-next.observableusercontent.com/05cc71b64a7e2c70386d6fb495a12747ccfb51091e42b766225dec019241af59","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":498,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"e1c56a8af8a5a3d1","login":"rjf9225","name":"Richard Fu","avatar_url":"https://avatars-next.observableusercontent.com/05cc71b64a7e2c70386d6fb495a12747ccfb51091e42b766225dec019241af59","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"e1c56a8af8a5a3d1","login":"rjf9225","name":"Richard Fu","avatar_url":"https://avatars-next.observableusercontent.com/05cc71b64a7e2c70386d6fb495a12747ccfb51091e42b766225dec019241af59","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Mini-Project 2 (Temporal Data)","cells":[{"id":115,"value":"md`# Mini-Project 2 (Temporal Data)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"md`## Data\n\nHere we load a [simplified and reduced version](https://docs.google.com/spreadsheets/d/1YiuHdfZv_JZ-igOemKJMRaU8dkucfmHxOP6Od3FraW8/) of the [AidData dataset](https://www.aiddata.org/data/aiddata-core-research-release-level-1-3-1). You are free to change this code as you wish. If you don't want to preprocess your data on Observable, then you can do the preprocessing using whatever languages or tools you'd like and then [upload](/@observablehq/file-attachments) your preprocessed data to Observable. If you take this approach, then you should delete the \\`aiddata\\` cell below so that you're not loading unnecessary data.\n\nThe dataset contains the following columns:\n- **yearDate**: year of the commitment as a Date object\n- **yearInt**: year of the commitment as an integer\n- **donor**: country providing the financial resource\n- **recipient**: country receiving the financial resource\n- **amount**: the total amount of financial resources provided\n- **purpose**: the purpose of the transaction`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"Inputs.table(aiddata)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"aiddata = {\n const data = await d3.csv(googleSheetCsvUrl, row => ({\n yearDate: d3.timeParse('%Y')(row.year),\n yearInt: +row.year,\n donor: row.donor,\n recipient: row.recipient,\n amount: +row.commitment_amount_usd_constant,\n purpose: row.coalesced_purpose_name,\n }));\n \n data.columns = Object.keys(data[0]);\n \n return data;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"md`Next we load GeoJSON data for countries. This file is derived from data from [Natural Earth](https://www.naturalearthdata.com).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"geoJSON = FileAttachment(\"countries-50m.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"md`### Remember\n\nThe questions above are a very useful tool for you to verify whether you are producing a good solution or not. As you go about solving this exercise you can use the questions above as an evaluation tool in two main ways:\n1. Verify that you can indeed answer the questions with your visualization. If you can’t fully answer the questions, it means your solution has some problems.\n2. Compare multiple solutions to the same problem and ask yourself: “which one makes it easier for me to answer these questions?”, where easier may mean, more accurately, with less effort or faster.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"md`## Instructions\n- Your coded solutions must use D3. Other visualization libraries are not allowed.\n- You should feel free to process and transform the data any way you deem relevant and use any tool you prefer for data processing. Data transformation does not necessarily have to be in JavaScript nor within Observable. It’s ok to pre-process the data and then load it in Observable if you prefer.\n- You can use Mike Bostock's [Color Legend](/@d3/color-legend) notebook to create your legends.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"import {legend, swatches} from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":143,"value":"md`## Grading Criteria\n\nThe main parameters we will use for grading are the following:\n\n- Does the information extracted answer the questions posed in the exercise?\n- Does the visualization answer the questions posed in the exercise?\n- Is the visual representation clear? Does it have good aesthetics?\n - Review this [notebook on clarity](/@nyuvis/contextual-elements-aesthetics-and-clarity) for reference.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"md`## Solutions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":497,"value":"## Question 1 Visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":250,"value":"minYear = d3.extent(aiddata, d=>d.yearDate)[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":254,"value":"maxYear = d3.extent(aiddata, d=>d.yearDate)[1]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"aidByDonor = d3.group(aiddata, d => d.donor)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":326,"value":"aidByRecipient = d3.group(aiddata, d=>d.recipient)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":328,"value":"aidByCountry = {\n\n var aidMap = new Map()\n var aid = d3.group((aiddata).map(\n d=>({\n 'country':d.donor,\n 'amount':d.amount,\n 'year':d.yearInt,\n })), d=>d.country)\n\n var received = d3.group((aiddata).map(\n d=>({\n 'country':d.recipient,\n 'amount':-d.amount,\n 'year':d.yearInt,\n })), d=>d.country)\n\n for (const [key, value] of received.entries()){\n if (aid.has(key)){\n var arr = aid.get(key)\n aidMap.set(key, value.concat(arr))\n }\n else{\n aidMap.set(key, value)\n }\n }\n\n return aidMap\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":164,"value":"aidPerYear = {\n\n var yrArr = new Map()\n for (const [key, value] of aidByCountry.entries()) {\n \n var donatedArr = new Array()\n var donated = d3.rollup(value, v => d3.sum(v, d => d.amount), d => d.year)\n\n for (const [k, v] of d3.sort(donated.entries())){\n var donatedObj = new Object()\n donatedObj.donor = key\n var d = new Date(k, 0, 1)\n donatedObj.year = d\n donatedObj.amount = v/1e6\n\n donatedArr.push(donatedObj)\n }\n \n yrArr.set(key, donatedArr)\n \n \n }\n\n return yrArr\n}\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":186,"value":"countries = [...aidPerYear.keys()]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":366,"value":"\nhighlightCountries = ['India',\n'Thailand',\n'Brazil',\n'Colombia',\n'Korea',\n'Poland',\n'United Kingdom',\n'Germany',\n'Japan',\n'United States']\n\n\n//highlightCountries = countries","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":287,"value":"maxPrice = d3.max(aidPerYear, ([countries, donations]) => d3.max(donations, p => p.amount))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":353,"value":"minPrice = d3.min(aidPerYear, ([countries, donations]) => d3.min(donations, p => p.amount))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":358,"value":"Swatches(d3.scaleOrdinal(highlightCountries, d3.schemeCategory10))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":404,"value":"$4b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":417,"value":"## Question 2 Visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":421,"value":"sortedPurpose = [...d3.rollup(d3.filter(aiddata, d=>d.purpose != \"Sectors not specified\"), v=>d3.sum(v, d=>d.amount), d=>d.purpose).entries()].sort((a, b) => b[1] - a[1]).slice(0, 10)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"top10Purpose = sortedPurpose.map(x=>x[0])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":455,"value":"purposes = d3.group(aiddata, d=>d.purpose)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":443,"value":"aidByPurpose = {\n \n var yrArr = new Map()\n \n for (const [key, value] of purposes.entries()) {\n\n if (top10Purpose.includes(key)) {\n var donatedArr = new Array()\n var donated = d3.rollup(value, v => d3.sum(v, d => d.amount), d => d.yearInt)\n \n for (const [k, v] of d3.sort(donated.entries())){\n var donatedObj = new Object()\n donatedObj.donor = key\n var d = new Date(k, 0, 1)\n donatedObj.year = d\n donatedObj.amount = v/1e6\n \n donatedArr.push(donatedObj)\n }\n \n yrArr.set(key, donatedArr)\n }\n \n }\n\n return yrArr\n}\n \n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":465,"value":"minPrice_purpose = d3.min(aidByPurpose, ([purposes, donations]) => d3.max(donations, p => p.amount))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"maxPrice_purpose = d3.max(aidByPurpose, ([purposes, donations]) => d3.max(donations, p => p.amount))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":488,"value":"Swatches(d3.scaleOrdinal(top10Purpose, d3.schemeTableau10))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":463,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":492,"value":"## Notes\n\nThis visualization shows the purposes of each donation across time sequentially, and we can easily see where there were spikes in donation amounts for specific purposes. For example, we see in the 1980s, early 1990s, and 2000s had brief booms in air transport donations, which is indicative of growth in certain countries or new technologies being developed. There was a very large spike in the early 1990s in the Rescheduling and Refinancing sector as well as the Import Support category. Interestingly, Rail support doesn't get much donations until the late 2000s and early 2010s, probably indicating donations to more significantly underdeveloped countries.\n\nThis visualization answers the questions in Problem 2 since not only are we able to see the trends in donation purpose, but we can also see clearly which specific purposes dominated overall.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"md`-----------------\n## Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"d3 = require('d3@7')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"googleSheetCsvUrl = 'https://docs.google.com/spreadsheets/d/1YiuHdfZv_JZ-igOemKJMRaU8dkucfmHxOP6Od3FraW8/gviz/tq?tqx=out:csv'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":360,"value":"$4d","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4e","stdlib":"1"}} 24:null