34:["$","title",null,{"children":"Homework 2.2 — Processing Arrays with Higher-order Functions | Jakob Li | Observable"}] 3c: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. ${flag()} In the cell below, write a function called `getGlobalIncidenceRate`. - It should take a single number as its input: a year. - It should `filter` `dataWithRates` to only that year's records. - It should compute the total measles cases and the total population for that year. To do this, use `map` to produce an array of numbers (like you did for `usaRates`), and then use `reduce` to add them up. - It should return the global measles cases per million people for that year. Refer to Part 1 of this homework for the formula. 3d:[] 38:{"id":"f74e2d490125aadb","slug":"homework-2-2-processing-arrays-with-higher-order-functions","title":"Homework 2.2 — Processing Arrays with Higher-order Functions","thumbnail":"f6ea2a37375de3f496de2d8497e93c6f7dbe16b54740d8547de99edaab61e24f","access_level":"public","create_time":"$D2026-09-24T03:40:19.742Z","update_time":"$D2026-10-21T18:58:24.329Z","view_time":null,"first_public_time":"$D2026-11-11T22:59:10.955Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"23018a00ede481f1","login":"jakob-li","name":"Jakob Li","avatar_url":"https://avatars-next.observableusercontent.com/6c393c83504f993fa9d48bc1447081bc56d334770c648a9bfa08ab204d3b367e","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":791,"publish_version":791,"role":null,"authored":false,"authors":[{"type":"individual","id":"166a45771d83b705","login":"jakob-li","name":"Jakob Li","avatar_url":"https://avatars-next.observableusercontent.com/6c393c83504f993fa9d48bc1447081bc56d334770c648a9bfa08ab204d3b367e","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"23018a00ede481f1","login":"jakob-li","name":"Jakob Li","avatar_url":"https://avatars-next.observableusercontent.com/6c393c83504f993fa9d48bc1447081bc56d334770c648a9bfa08ab204d3b367e","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","tool":"$undefined","providerMetadata":"$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":"$undefined","tool":"$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","tool":"$undefined","providerMetadata":"$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":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"$${flag()} In the cell below, write a function called `getCasesPerMillion`.\n- It should take a single object as its input (a country object for a given year).\n- It should return a number (the country's measles cases per million people, calculated based on its population and measles total).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"\nconst getCasesPerMillion = (cases) => {\n return cases[\"measles_total\"] / cases[\"total_population\"] / 1e6\n\n}\n\n\n\n\n\n// Your code here. Be sure to name your function getCasesPerMillion\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"getCasesPerMillion(data.at())","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"const dataWithRates = data.map((cases)=>({...cases, casesPerMillion: getCasesPerMillion(cases)}))\n// Your code here. Be sure to save your result in a variable called dataWithRates\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"$${flag()} Inspect some items from the data below to see that a new `casesPerMillion` property exists on your data. Work one or two out on a calculator to make sure you did it right!\n\n_If you're seeing an error saying `dataWithRates is not defined`, please make sure you've completed all the previous steps._","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"dataWithRates","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"const usaData = dataWithRates.slice(899, 910)// Your code here. Create a variable called usaData that slices dataWithRates to only the USA's data\ndisplay(usaData)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"$${flag()} First, use `map` on `usaData` to produce an array of measles incidence rates in the USA. Call your new variable `usaRates`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"const usaRates = usaData.map(getCasesPerMillion)// Your code here.\ndisplay(usaRates)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"The `d3` project provides a bunch of very useful functions for data processing. You can see in the appendix we're already using it to load our dataset.\n\nIn this section, we're going to use [`d3` array functions](https://d3js.org/d3-array/summarize) to compute summary statistics about our dataset.\n\nThe three operations I want you to read about are:\n\n- [d3.min](https://d3js.org/d3-array/summarize#min)\n- [d3.max](https://d3js.org/d3-array/summarize#max)\n- [d3.mean](https://d3js.org/d3-array/summarize#mean)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"d3.min(usaRates)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"$${flag()} Similarly, use `d3.max` to compute the _maximum_ incidence rate for the USA rates.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"d3.max(usaRates)// Your code here.\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"usaData","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"The lowest incident rates were in 2020, and the highest incident rates were in 2019. I see that 2019 is higher than the rest it seems to me that they have lowered the incident rate which is pretty cool to me how lower the next year was compared to 2019. \n","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"$${flag()} Those min and max numbers, by themselves, are a little difficult to make sense of. It would be helpful to compare them to some kind of baseline, like the mean (average) incidence rate. In the cell below, use `d3.mean` to compute the average measles incidence rate for the USA (i.e., `usaRates`)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"d3.mean(usaRates)// Your code below. Compute the average incidence rate for the `usaData` array\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"I see that USA min and USA max are quite similar to each other the number are almost the same, which is insane. \n","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$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","tool":"$undefined","providerMetadata":"$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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"const incidence2025 = dataWithRates.filter((year) => year[\"year\"] == \"2025\")// Your code here. Be sure to save your result in a variable called incidence2025\n\ndisplay(incidence2025)\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$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":"$undefined","tool":"$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) // Displays the found object above this cell","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$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":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"$${flag()} Similarly, use `reduce` to find the object in `incidence2025` with the *highest* `casesPerMillion`. Save the result in a variable called `highestIncidence2025`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"const highestIncidence2025 = incidence2025.reduce((acc, curr) =>\n curr.casesPerMillion > acc.casesPerMillion? curr : acc)\n\ndisplay(highestIncidence2025)// Your code here. Be sure to save your result in a variable called highestIncidence2025\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"### Accounting for population size\n\nTake a look at the country with the highest rate. How many people live there?\n\nRates computed from small populations can swing wildly. In a country of 500,000 people, just 50 cases works out to 100 cases per million, which is a higher rate than a much larger outbreak in a big country. To focus on countries where the rates are more stable, let's set a _minimum population_ and only consider countries above it.\n\nI've defined `populationFloor` in the cell below (50 million people).\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"const populationFloor = 30_000_000; // The underscores help with readability. This is just like writing 50000000","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"const largeCountries2025 = dataWithRates.filter((year) => year[\"year\"] === 2025 && year[\"total_population\"]\n > populationFloor) // Your code here. Be sure to save your result in a variable called largeCountries2025\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"largeCountries2025","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"$${flag()} Use `reduce` again to find the objects in `largeCountries2025` with the lowest and highest `casesPerMillion`. Save them in variables called `lowestLarge2025` and `highestLarge2025`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"const lowestLarge2025 = largeCountries2025.reduce((acc, curr) =>\n curr.casesPerMillion > acc.casesPerMillion? acc: curr)\ndisplay(lowestLarge2025)\n// Your code here. Be sure to save your result in a variable called lowestLarge2025\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":71,"value":"const highestLarge2025 = largeCountries2025.reduce((acc, curr) => curr.casesPerMillion < acc.casesPerMillion? acc : curr)// Your code here. Be sure to save your result in a variable called highestLarge2025\ndisplay(highestLarge2025)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"By looking at this data the difference is really big difference the cases are hugely different between the lowest and highest countries. I think the population floor made a difference because the amount of population can change the cases. \n","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":" const getGlobalIncidenceRate = (year: number) => {\n const yeardata = dataWithRates.filter((data) => data[\"year\"] === year)\n const data = yeardata\n .map((data) => data[\"measles_total\"])\n .reduce((acc, curr) => acc + curr)\n\n const popdata = yeardata\n .map((data) => data[\"total_population\"])\n .reduce((acc, curr) => acc + curr)\n\n return data / popdata * 1e6\n }\n\n display(getGlobalIncidenceRate) // Your code here. Be sure to name your function getGlobalIncidenceRate\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"$${flag()} I've given you an array of years below. We'd like to learn about the trend of global incidence rates over this time period.\n\nUse `map` on the array of `years`, and your `getGlobalIncidenceRate` function, to produce an array of global incidence rates, one for each year. Save the result in a variable called `globalRates`.\n\n(Recall that the data was collated in June 2025, so 2025 has partial data.)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"const years = [2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025];","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"const globalRates = years.map((year) => getGlobalIncidenceRate(year))\ndisplay(globalRates)// Your code here. Write an expression and inspect its value\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"$${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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"What I can see from the start of the incidence rate is that it started to declined, but during the middle the incidence rate started to increase.\n","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"## Part 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$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","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"There are some functions I dont understand in the code, like \"d3.csv\" and \"await\" i'm curious what they do. \n","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$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","tool":"$undefined","providerMetadata":"$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":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"","pinned":true,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3d","stdlib":"2"}} 33:null