43:["$","title",null,{"children":"Homework 2.1 — Arrays and Objects | Ethan Canas | Observable"}] 4b:T68b,Eventually, we'll want to compare case counts between countries (even though right now we're only focused on the USA). But different countries' different populations make this fraught. Consider this object, the 2025 USA data. (Remember that since the data is up to June 2025, this roughly represents the _first half_ of 2025.) ```ts { region: "AMRO" country: "United States of America" year: 2025 total_population: 347275808 measles_total: 1166 } ``` The `measles_total` (1166) tells us something useful to be sure. But it's less useful if we want to compare it to, say, Canada's `measles_total` in 2025 (2197). ```ts { region: "AMRO" country: "Canada" year: 2025 total_population: 40126723 measles_total: 2197 } ``` Is the difference we're seeing mostly an artifact of the population difference? Or is it actually a difference in _incidence of measles_? To do this, we're going to compute a new metric. Instead of saying "there were 1166 measles cases in the USA in the first half of 2025", we want to say how many measles cases there were _per 1 million people_ in that time period. ${flag()} In the cell below: - Get the object from `usaData` that corresponds to the year 2025 and save it in a new variable. - Compute the USA rate using the following formula:
`measles_total / total_population * 1e6` (cases per person, multiplied by 1 million = cases per million people), and save it in a variable. Note that you'll need to actually use the variable you created to access the properties within the object. - `display` the variable. For example, if you named your variable `usa2025mpm`, you need to end your cell with `display(usa2025mpm)`.4c:T5e6,

If you're curious about the code above, expand this.

The entire piece of code is a variable declaration. Its job is to prepare the `data` table (an array of objects). Let's go through it piece by piece. **The first piece:** ```ts await d3.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-06-24/cases_year.csv", { autoType: true }) ``` This line downloads the data from the specified URL, and reads it into an array of objects. It uses the `csv` function to do this, because the data is in "comma-separated value" (CSV) format. That is, each row of the data is a list of values written out like this: `AFRO,Algeria,DZA,2012,37646166,37646166,76,55,2,0,53,1.46,13,13,0,0,0.35,8,0.02` where each value corresponds to a specific column. **The next piece** is a bit more complicated. ```ts .map( d => ['region', 'country', 'year', 'total_population', 'measles_total'] .reduce((obj, key) => ({...obj, [key]: d[key]}), {} ) ) ``` The data, in its original form, contains a bunch of columns we don't really need right now. So, we transform each record in the downloaded data into a new, _smaller_ record (each containing fewer properties: only the ones we need). We're going to learn more about the `map` and `reduce` operations while this homework is active, so ideally this bit of code will make more sense as the homework progresses!

4d:[] 47:{"id":"7ab6fb89673b2ff2","slug":"homework-2-1-arrays-and-objects","title":"Homework 2.1 — Arrays and Objects","thumbnail":"953638b68462ff8272b6f5317771d31b729421ab25a905dae7188a6732cd295d","access_level":"public","create_time":"$D2026-09-23T20:52:38.967Z","update_time":"$D2026-09-24T00:25:01.759Z","view_time":null,"first_public_time":"$D2026-10-21T23:24:37.538Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"2abea9e8fd7bddd2","login":"ethan-canas","name":"Ethan Canas","avatar_url":"https://avatars-next.observableusercontent.com/b7c5322e2baa235ce8d27d4de331abc1acd942dbda7fe9060c4c270fdb6b26d9","tier":"starter_2024","bio":"","following":false},"version":599,"publish_version":599,"role":null,"authored":false,"authors":[{"type":"individual","id":"6bb51578ea0785b8","login":"ethan-canas","name":"Ethan Canas","avatar_url":"https://avatars-next.observableusercontent.com/b7c5322e2baa235ce8d27d4de331abc1acd942dbda7fe9060c4c270fdb6b26d9","tier":null,"bio":"","following":false}],"members":[{"type":"team","id":"2abea9e8fd7bddd2","login":"ethan-canas","name":"Ethan Canas","avatar_url":"https://avatars-next.observableusercontent.com/b7c5322e2baa235ce8d27d4de331abc1acd942dbda7fe9060c4c270fdb6b26d9","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Homework 2.1 — Arrays and Objects","cells":[{"id":1,"value":"# Homework 2.1 — Arrays and Objects","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"## Objectives\n\nThe goals of this homework are:\n\n- To work with real data about a worldly phenomenon.\n- To work with a dataset organized as an array of objects.\n- In the later parts of the homework, to work with _functions_ on arrays.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"As always, fork this notebook and read through it, completing ${flag()} items as you come across them.\n\nWe're going to work with data about [_Measles Cases Across the World_](https://rfordatascience.github.io/tidytuesday/data/2025/2025-06-24/readme.html), originally prepared by the [World Health Organization](https://immunizationdata.who.int/global?topic=Provisional-measles-and-rubella-data&location=) and cleaned up and organized by the [TidyTuesday](https://rfordatascience.github.io/tidytuesday/about.html) project.\n\n**The dataset goes up to June 2025**, so is a little out of date.\n\nThe data is available to us as in the `data` array, depicted below in \"table\" form.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"Inputs.table(data, {\n rows: 5,\n format: {\n year: x => x.toString()\n }\n})","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"The table above is useful to get a visual idea of the dataset's organization. For this homework, you also need to see the data in its _programmatic_ view, i.e., as an array of objects.\n\nThe cell below has, as its output, the entire `data` array. Click the little arrow next to `Array(${data.length})` to browse the array and see its contents.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"data","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"## Part 1: Get to know (a slice of) the data\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"Let's first get accustomed to the data's representation in our program, i.e., as an array of objects.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"$${flag()} How many elements are in the array? Write an expression below to find out.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"// Your code here.\ndata.length","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"$${flag()} In the cell below, write an expression that would allow you to inspect an element from the array. You can inspect the first item, the last item, and some other item.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"// Write an expression here to inspect the first item in the array\ndata[0]","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"// Write an expression here to inspect the LAST item in the array\ndata[data.length - 1 ]","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"// Write an expression here to inspect some item in the middle of the array\ndata[500]","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"$${flag()} As part of our data analysis, we are planning to only work with data from the USA. In our dataset, records for the USA run from index `${data.findIndex(d => d.country === 'United States of America')}` to `${data.findLastIndex(d => d.country === 'United States of America')}`.\n\nUse an array operation that we've learned about in class to declare a _new_ variable called `usaData`, that contains only the records from index `${data.findIndex(d => d.country === 'United States of America')}` to `${data.findLastIndex(d => d.country === 'United States of America')}`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"// Your code here\nconst usaData = data.slice(899 , 910);","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"$${flag()} Always conduct checks to verify the results of your data analysis steps!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"First, check that you got **11 objects**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"// Verify that usaData got you 11 objects.\nusaData.length","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"Then it's usually a good idea to inspect the _boundary cases_ when we're working with data, to make sure we didn't accidentally include data from other countries that would make our analyses incorrect.\n\nCheck that the first item in the `usaData` array is from the USA.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"// Check the first item in usaData.\nusaData[0]","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"Similarly, check the last item.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"// Check the last item in usaData.\nusaData[10]","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"$${flag()} The USA data runs from 2014 to 2025. In the cell below, compute the _change in case counts_ over that span of years. That is, compute the difference: (cases in 2025 – cases in 2014)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"// Your code here.\nusaData[10].measles_total - usaData[0].measles_total","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"$${flag()} Let's do a last verification check on the data. What do you think the value of `usaData.at(1)[\"year\"]` should be? _Think about it_, then evaluate that expression in the cell below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"// What's usaData.at(1)[\"year\"]?\nusaData.at(1)[\"year\"]","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":61,"value":"Was it what you expected?\n\nRun an eye over the whole dataset (it's only 11 rows, after all). Does it include a contiguous sequence of years, in order? _Is any data missing?_\n\n_If you're seeing an error saying `usaData 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":"$undefined"},{"id":55,"value":"usaData","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"$${flag()} From glancing at the data, write a brief sentence or two about any patterns you observe in the `usaData` array. Note that the cell below is a _markdown_ cell — you're not being asked to write code for this question.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"\nThe USA data runs from 2014 to 2015, but the years are not fully continuous. The second entry is 2016, which means the dataset is missing a row for 2015.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"## Part 2: Compute an \"incidence rate\" metric","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"$4b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"// Write your code here.\nconst usa2025 = usaData[10];\nconst usa2025mpm = usa2025.measles_total / usa2025.total_population * 1e6;\ndisplay(usa2025mpm);\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"$${flag()} Now, do the same for the 2025 data from Canada. I have made Canada's 2025 object available to you in the variable `canada2025` (see the cell below). Modify the code in the cell below to compute the same \"cases per million people\" metric for Canada.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"// Change this code to compute the new metric for Canada in 2025\ncanada2025\nconst canada2025mpm = canada2025.measles_total / canada2025.total_population * 1e6\ndisplay(canada2025mpm)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"$${flag()} Answer and reflect: Does population size alone explain the gap in the measles case counts between the two countries? Note that we can't really say _why_ any gap exists or doesn't exist — that story is impossible to tell from these numbers alone.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"\nPopulation size alone doesn’t fully explain the difference in measles case counts between the USA and Canada. Even though some populations are larger than others, there is some factors into it such as, public health policies, vaccination rates, and outbreaks.","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"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":19,"value":"$4c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"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":"$undefined"},{"id":74,"value":"const canada2025 = data.find(d => d.country === 'Canada' && d.year === 2025)","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4d","stdlib":"2"}} 42:null