2d:null 2e:["$","title",null,{"children":"Homework 2.2 — Processing Arrays with Higher-order Functions | Jazmine | Observable"}] 33:T467,### A global incidence rate So far, we've looked at individual countries. What about the world as a whole? One tempting idea is to average every country's `casesPerMillion`. But that treats a country of 500,000 people the same as a country of 1.4 billion people. Instead, we'll compute the global rate the same way we computed each country's rate: total measles cases divided by total population, times one million. The difference is that the totals are now summed across _all_ countries. We still want to do this on a _per-year_ basis. We'll write a reusable function to help with this. ${flag()} In the cell below, write a function called `getGlobalIncidenceRate`. - It should take a single number as its input: a year. - It should `filter` `dataWithRates` to only that year's records. - It should compute the total measles cases and the total population for that year. To do this, use `map` to produce an array of numbers (like you did for `usaRates`), and then use `reduce` to add them up. - It should return the global measles cases per million people for that year. Refer to Part 1 of this homework for the formula. 34:[] 31:{"id":"1895349fb95fd84d","slug":"homework-2-2-processing-arrays-with-higher-order-functions","title":"Homework 2.2 — Processing Arrays with Higher-order Functions","thumbnail":"c4d2b1683509dfd86147734a5756c655ce5890e94bc31f75b1b22d2e64909cd0","access_level":"public","create_time":"$D2026-09-30T06:52:46.241Z","update_time":"$D2026-09-30T22:26:19.338Z","view_time":null,"first_public_time":"$D2026-09-30T22:28:42.282Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"f9794693d27004e3","login":"jhazzy","name":"Jazmine","avatar_url":null,"tier":"starter_2024"},"version":511,"publish_version":511,"role":null,"authored":false,"authors":[{"type":"individual","id":"542eace3fbc752f9","login":"jhazzy","name":"Jazmine","avatar_url":null,"tier":null}],"members":[{"type":"team","id":"f9794693d27004e3","login":"jhazzy","name":"Jazmine","avatar_url":null,"tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Homework 2.2 — Processing Arrays with Higher-order Functions","cells":[{"id":1,"value":"# Homework 2.2 — Processing Arrays with Higher-order Functions\n\nThis homework is a continuation of [Homework 2.1 — Arrays and Objects](https://observablehq.com/@ayaankazerouni/csc1024-hw2-arrays-and-objects). Please complete that before moving on to this one.\n\nIn this homework, you'll continue working with the `data` array, containing data about [_Measles Cases Across the World_](https://rfordatascience.github.io/tidytuesday/data/2025/2025-06-24/readme.html).\n\nHere's the data depicted as a table.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"Inputs.table(data, {\n rows: 5,\n format: {\n year: y => y.toString()\n }\n})","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"## Part 1: Functions and `map`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"In HW 2.1, you wrote TypeScript code to compute a \"cases per million people\" metric:\n\n`measles_total / total_population * 1e6`\n\nYou did this for a _single_ record at a time (USA in 2025, and Canada in 2025). But that's not really feasible for our entire dataset. We can't be writing that same formula ${data.length} times!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"$${flag()} In the cell below, write a function called `getCasesPerMillion`.\n- It should take a single object as its input (a country object for a given year).\n- It should return a number (the country's measles cases per million people, calculated based on its population and measles total).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"function getCasesPerMillion(obj) {\n return (obj.measles_total / obj.total_population) * 1e6;\n}","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"$${flag()} Now that we have a reusable function that can compute our measles incidence rate metric, we want to augment our dataset with the added column.\n\nUse `map` on the `data` array to produce a _new_ dataset, where each object has a new `casesPerMillion` property, whose value is that country's cases per million people in that given year.\n\nSave the result in a new array called `dataWithRates`.\n\nHints:\n- You'll want to use the spread operator
`({ ...obj, casesPerMillion: getCasesPerMillion(obj) })`
to perform this task. It's important to wrap it in parentheses `( )`, otherwise TypeScript will interpret the `{ }` as being the opening of a function body.\n- Alternatively, you could write a \"full-bodied\" function with an explicit `return` statement inside the `map`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"const dataWithRates = data.map(obj => ({\n ...obj,\n casesPerMillion: getCasesPerMillion(obj)\n}))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"$${flag()} Inspect some items from the data below to see that a new `casesPerMillion` property exists on your data. Work one or two out on a calculator to make sure you did it right!\n\n_If you're seeing an error saying `dataWithRates is not defined`, please make sure you've completed all the previous steps._","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"dataWithRates.slice(0, 3)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"$${flag()} For the time being, let's limit our analysis to the USA's data. In HW 2.1 you used an array operation to compute `usaData` (including indices from 899 to 910). Use that same operation on `dataWithRates` to limit to only the USA's data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"const usaData = dataWithRates.slice(899, 911);","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"## Part 2: Computing some summary statistics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"$${flag()} First, use `map` on `usaData` to produce an array of measles incidence rates in the USA. Call your new variable `usaRates`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"const usaRates = usaData.map(obj => obj.casesPerMillion)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"The `d3` project provides a bunch of very useful functions for data processing. You can see in the appendix we're already using it to load our dataset.\n\nIn this section, we're going to use [`d3` array functions](https://d3js.org/d3-array/summarize) to compute summary statistics about our dataset.\n\nThe three operations I want you to read about are:\n\n- [d3.min](https://d3js.org/d3-array/summarize#min)\n- [d3.max](https://d3js.org/d3-array/summarize#max)\n- [d3.mean](https://d3js.org/d3-array/summarize#mean)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"$${flag(\"TO-DONE\")} (This part is done for you.) Use `d3.min` to compute the minimum of the `usaRates` variable.\n\n_If you see the error `usaRates is not defined`, make sure you've completed the previous step._","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"const minUsaRate = d3.min(usaRates)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"$${flag()} Similarly, use `d3.max` to compute the _maximum_ incidence rate for the USA rates.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"const maxUsaRate = d3.max(usaRates)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"$${flag()} Inspect the usaData array below. Which years have the highest and lowest incidence rates? Is this striking to you? Why or why not?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"usaData","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"Highest incidence is in 2019 as well as 2025 yet 2019 is still higher and lowest incidence is 2020 this is pretty striking because there was a dramatic drop in 2020 which aligns with the COVID 19 pandemic which is around the time that everyone was staying inside, on the flip side there was a massive spike in 2019 which shows the highest number of U.S. measles record.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"$${flag()} Those min and max numbers, by themselves, are a little difficult to make sense of. It would be helpful to compare them to some kind of baseline, like the mean (average) incidence rate. In the cell below, use `d3.mean` to compute the average measles incidence rate for the USA (i.e., `usaRates`)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"const meanUsaRate= d3.mean(usaRates)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"$${flag()} You've computed three statistics so far. The USA min, USA max, and USA mean incidence rates. What are your takeaways from those numbers taken together? What's surprising, if anything? 1–3 sentences.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"Theres a massive difference between the minimum and maximum rates which shows that measles incidence is usually thrown off by another occasional outbreak.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"## Part 3: Using `filter`, `reduce`, and other functions\n\nIf we have not yet covered `filter` and `reduce` in class, please pause and come back to this homework!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"In HW 2.1, you compared the USA's and Canada's measles incidence in 2025. Now that every record in `dataWithRates` has a `casesPerMillion` property, we can look at *every* country in 2025.\n\n${flag()} Use `filter` on `dataWithRates` to keep only the records from 2025. Save the result in a variable called `incidence2025`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"const incidence2025= dataWithRates.filter(obj => obj.year === 2025)\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"### Which countries had the lowest and highest incidence in 2025?\n\nIn Part 2, we used `d3.min` and `d3.max` to find the lowest and highest incidence rates in the USA. That gave us the min and max _numbers_ but not the countries they came from. With only 11 USA records, we could eyeball the table to find the matching years. But we can't do that with the whole dataset!\n\nWhat we really want is the whole *object* with the lowest (or highest) rate, so we can see which country it is, along with its population and case count. This is where `reduce` comes in.\n\n${flag(\"TO-DONE\")} (This part is done for you.) The cell below uses `reduce` to find the object in `incidence2025` with the lowest `casesPerMillion`. Read the code, then read the explanation below it.\n\n_Hint: If you're seeing the error `incidence2025 is not defined`, please make sure you've done the previous steps._","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":61,"value":"const lowestIncidence2025 = incidence2025.reduce((acc, curr) =>\n curr.casesPerMillion < acc.casesPerMillion ? curr : acc\n)\ndisplay(lowestIncidence2025)\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"const filteredLargeCountries = dataWithRates.filter(obj =>\n obj.year === 2025 && obj.total_population > populationFloor\n)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"**Code explanation**:\n\n- `reduce` walks through the array one object at a time, carrying along an _accumulator_. Here, the accumulator is `acc`: the lowest-incidence object we've seen _so far_.\n- Since we didn't give `reduce` a starting value, `acc` starts out as the first object in the array.\n- At each step, the callback compares the current object `curr` with `acc`, and returns whichever one has the lower rate. That return value becomes `acc` for the next step.\n- Once every object has been visited, the final value of `acc` is our answer.\n\nIf several countries tie for the lowest rate, this returns the first one it comes across.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"$${flag()} Similarly, use `reduce` to find the object in `incidence2025` with the *highest* `casesPerMillion`. Save the result in a variable called `highestIncidence2025`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"const highestIncidence2025 = incidence2025.reduce((acc, curr) =>\n curr.casesPerMillion > acc.casesPerMillion ? curr : acc\n)\n\ndisplay(highestIncidence2025)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"### Accounting for population size\n\nTake a look at the country with the highest rate. How many people live there?\n\nRates computed from small populations can swing wildly. In a country of 500,000 people, just 50 cases works out to 100 cases per million, which is a higher rate than a much larger outbreak in a big country. To focus on countries where the rates are more stable, let's set a _minimum population_ and only consider countries above it.\n\nI've defined `populationFloor` in the cell below (50 million people).\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"const populationFloor = 30_000_000; // The underscores help with readability. This is just like writing 50000000","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"$${flag()} Use `filter` on `dataWithRates` to keep only the records that are from 2025 **and** whose `total_population` is greater than `populationFloor`. Save the result in a variable called `largeCountries2025`.\n\n_Do you remember how to combine two conditions for a check like this?_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"const largeCountries2025 = dataWithRates.filter(obj =>\n obj.year === 2025 && obj.total_population > populationFloor\n)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"$${flag()} Use `reduce` again to find the objects in `largeCountries2025` with the lowest and highest `casesPerMillion`. Save them in variables called `lowestLarge2025` and `highestLarge2025`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"const highestLarge2025 = largeCountries2025.reduce((acc, curr) =>\n curr.casesPerMillion > acc.casesPerMillion ? curr : acc\n);\n\nhighestLarge2025;","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"const lowestLarge2025 = largeCountries2025.reduce((acc, curr) =>\n curr.casesPerMillion < acc.casesPerMillion ? curr : acc\n);\n\nlowestLarge2025;","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"$${flag()} Compare these with the lowest and highest countries you found using _all_ of `incidence2025`. How are they different? Why do you think the population floor made a difference?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"Without the floor the extreme ere just tiny nations prone to have drastic changes, with the 50M floor they are large countries like Yemen with stable data, this all matters because smaller populations floor filters out everything that way you get to see health trends.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"const getGlobalIncidenceRate = year => {\n const yearData = dataWithRates.filter(d => d.year === year)\n\n const totalCases = yearData\n .map(d => d.measles_total)\n .reduce((sum, val) => sum + val, 0);\n\n const totalPopulation = yearData\n .map(d => d.total_population)\n .reduce((sum, val) => sum + val, 0);\n\n return (totalCases / totalPopulation) * 1_000_000;\n}","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"$${flag()} I've given you an array of years below. We'd like to learn about the trend of global incidence rates over this time period.\n\nUse `map` on the array of `years`, and your `getGlobalIncidenceRate` function, to produce an array of global incidence rates, one for each year. Save the result in a variable called `globalRates`.\n\n(Recall that the data was collated in June 2025, so 2025 has partial data.)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"const years = [2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025];","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"const globalRates = years.map(year => getGlobalIncidenceRate(year))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"$${flag()} The first rate in `globalRates` goes with the first year in `years` (2014), and so on. How has the global measles incidence rate changed from 2014 to 2025? 1–3 sentences.\n\nAdditionally, link to at least one online article that discusses, contextualizes, or analyzes the trend, and summarize its main point in 1–2 sentences.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"Measles incidence rates shift significantly through the mid 2010s before it spikes up in 2020s and 2025, this shows that there are setbacks in childhood immunization.\n[UNICEF: Measles Cases Are On the Rise Globally](https://www.unicefusa.org/stories/measles-cases-are-rise-globally-what-it-means-children)\nThe overall report in this article is that million of children missed the routine vaccination which leaves a lot of vulnerability in the population and driving resurgence of outbreaks.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"## Part 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"So far, you've performed analytic steps that I've prescribed to you.\n\n${flag()} In the cell below, outline some questions _you_ have about this dataset, in a few sentences. Don't worry if you don't yet know how to go about answering them (yet!). If you have an idea, try it out in cells below the next one.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"How do measles incidence rates connect with ones national income level or there healthcare access across global regions?\n","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"const data = (await d3.csv(\"https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-06-24/cases_year.csv\", d3.autoType))\n .map(\n d => ['region', 'country', 'year', 'total_population', 'measles_total']\n .reduce((obj, key) =>\n ({...obj, [key]: d[key]}),\n {}\n )\n )","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"import { stylesheet, callout, flag } from 'observable:@ayaankazerouni/utils'","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"","pinned":true,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"2"}} 32:"$31:body"