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Interactive Info Vis
UW iSchool Course INFO 474: focused on designing and building visualizations to better understand and communicate about pressing issues.
Published
Edited
Jan 6, 2021
2 forks
2
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// Load the protests.csv data (a File Attachment) into a variable
protests = FileAttachment("protests.csv").csv()
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// How many protests are in the dataset?
num_protests = REPLACE_ME
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// How much information is available about each protest?
num_features = REPLACE_ME
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// Create an array of the number of attendees in each protest
// (make sure to return a *number* for each element in the array)
num_attendees = REPLACE_ME
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// What is the lowest number of attendees?
min_attendees = REPLACE_ME
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// What is the highest number of attendees?
max_attendees = REPLACE_ME
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// What is the mean number of attendees?
mean_attendees = REPLACE_ME
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// What is the median number of attendees?
median_attendees = REPLACE_ME
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// What is the absolute difference between the mean and median number of attendees?
mean_median_diff = REPLACE_ME
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// Create an array that holds the Location of each protest
locations = REPLACE_ME
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// How many *unique* locations are in the dataset?
num_locations = REPLACE_ME
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// How many protests occured in Washington?
// (hint: locations that end with "WA")
num_in_wa = REPLACE_ME
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// What proportion of protests (number / total) occured in Washington?
prop_in_wa = REPLACE_ME
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// How many protests occurred in each state?
// hint: use the d3.rollups method to create an *array of arrays*,
// where each one holds the state abbreviation and the number of protests in that state
// https://observablehq.com/@d3/d3-group
num_by_state = REPLACE_ME
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// What was the highest number of protests that occured in a single state?
highest_in_a_state = REPLACE_ME
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// Which state (two letter abbreviation) had the highest number of protests?
// hint: use the two variables calculated above
state_most_protests = REPLACE_ME
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// Create an array that holds the Date of each protest
// You should convert the string in the original dataset to a proper date object using the Date() function
dates = REPLACE_ME
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// What is the most recent date in the dataset?
most_recent = REPLACE_ME
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// What is the earliest date in the dataset?
earliest = REPLACE_ME
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// How many protests occurred in 2020?
// hint: use the getFullYear() method
num_in_2020 = REPLACE_ME
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// How many protests occurred in 2019?
num_in_2019 = REPLACE_ME
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// How many protests occurred in July (of any year)?
// Hint: you can use date.toLocaleDateString("default", { month: "long" }) to get the month name
num_in_july = REPLACE_ME
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// How many protest occured each month?
// hint: use the d3.rollups method to create an *array of arrays*,
// where each one holds the month name and the number of protests in that month
by_month = REPLACE_ME
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// Which month had the highest number of protests?
month_most_protests = REPLACE_ME
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// How many different purposes are listed in the dataset?
// (the number of unique values of the `Event (legacy; see tags)` attribute)
num_purposes = REPLACE_ME
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// That's quite a few -- if you look at the array, you'll notice
// a common pattern for each purpose. It's listed as:
// SOME_PURPOSE (additiona_detail)
// To get a higher level summary, create an array of `high_level_purposes` by
// extracting *everything before the first parenthesis* in each purpose ("Event (legacy; see tags)")
// You should return an array equal to the length of the protests dataset
high_level_purposes = REPLACE_ME
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// Which high-level purpose was the most common?
// Hint, you may want to open a JavaScript statement to write multiple lines of code,
// Or try out d3.greatest with d3.rollups
// https://observablehq.com/@d3/d3-least
most_common_purpose = REPLACE_ME
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import { check_answer, displayCaution } with {
answers
} from "@uw-info474/utilities"
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answers = await FileAttachment("answers.json").json({ typed: true })
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new Date(answers.earliest)
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d3 = require("d3")
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_ = require("lodash")
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REPLACE_ME = undefined
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