data
isBadCereal = // write function here
data[15].rating
isBadCereal(data[15]) // should be true
data[3].rating
isBadCereal(data[3]) // should be false
data.sort((a, b) => // write your answer here)
viewof aq_data = aq.from(data).view(5)
aq_data.filter(currentElement => currentElement.type === 'H').view()
aq_data.filter(currentElement => currentElement.type === 'H').objects()
favoriteBrands
aq_data.filter(d => favoriteBrands.has(d.mfr)).objects()
aq_data.filter(aq.escape(d => favoriteBrands.has(d.mfr))).objects()
aq_data.array('sodium')
abbreviationToBrandName
viewof aq_cleaned = aq_data
.derive({type: currentElement => currentElement.type === "H" ? "hot" : "cold",
brand: aq.escape(currentElement => abbreviationToBrandName[currentElement.mfr])})
.select('name', 'rating', 'type', 'shelf', 'brand')
.view({height:150})
aq_cleaned
.filter(d => d.brand === 'Kelloggs')
.orderby('rating')
.slice(0,3)
.array('name')
aq_cleaned.view(5)
// write answer here
// write answer here
visualizeTicks(d3.scaleLinear().domain([0, 10]))
visualizeTicks(d3.scaleLinear().domain([32, 212]))
visualizeTicks(d3.scaleLinear().domain([32, 212]).nice())
import {scores} from "@nyuvis/d3-introduction"
scores
x_width = 300
x = d3.scaleLinear()
.domain([0, d3.max(scores, d => d.score)])
.range([0, x_width])
visualizeTicks(x)
y_height = 100
y = d3.scaleBand()
.domain(scores.map(d => d.name))
.range([0, y_height])
.padding(0.2)
visualizeTicks(y)
// write code here
// write code here
// write code here
d3 = require("d3@7")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
