Public
Edited
Jul 1, 2023
1 fork
biking = [
{ day: "Monday", miles: 6.2, time_hr: 0.53 },
{ day: "Tuesday", miles: 10.0, time_hr: 1.02 },
{ day: "Wednesday", miles: 4.9, time_hr: 0.48 },
{ day: "Thursday", miles: 0, time_hr: 0 },
{ day: "Friday", miles: 18.5, time_hr: 1.59 },
{ day: "Saturday", miles: 7.3, time_hr: 0.86 },
{ day: "Sunday", miles: 0, time_hr: 0 }
]
// Find the miles biked on Friday:
biking[4].miles
// Alternatively: biking[biking.map(d => d.day).indexOf("Friday")].miles
// For each day, find the average biking speed.
biking.map(d=> d.miles/d.time_hr)
// Only keep objects (rows) where miles is greater than 10:
biking.filter(d=>d.miles>10)
// Keep rows for Saturday and Monday:
biking.filter(d=>d.day=="Saturday" || d.day=="Monday")
// Keep rows where miles is less than 10 AND time_hr is less than 0.5:
biking.filter(d=>d.miles<10 && d.time_hr<0.5)
// Keep rows *except* for Thursday:
biking.filter(d=>d.day!="Thursday")
// Keep all existing properties; add a new one named 'km' with miles converted to kilometers;
biking.map(d=>({...d, km: d.miles*1.61}))
carData
Type Table, then Shift-Enter. Ctrl-space for more options.
carTable = aq.from(carData)
console(carTable)
Type JavaScript, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
// Add your data wrangling (using Arquero) here:
carTable
.select('name', 'economy (mpg)', 'cylinders', 'weight (lb)')
.filter(d=> d.cylinders==4 && d['economy (mpg)']!=null)
.derive({weight_kg: d=> d['weight (lb)']*0.45})
.orderby('economy (mpg)')
.rename({'economy (mpg)': 'mpg'})
.view()
world_bank_data.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
// Use Array map and filter to do the wrangling steps above, storing the output as wb2019:
wb2019 = wb.map(d=>({country: d.country, year:d.year, co2: d.co2, region: d.region, co2_thousands: d.co2/1000})).filter(d=> d.year==2019)
wbTable = aq.from(wb)
// Use Arquero verbs to do the same wrangling steps:
wbTable
.select('country', 'year', 'co2', 'region')
.derive({co2_thousands: d=>d.co2/1000})
.filter(d=> d.year==2019)
.view()
// Create a chart of the top 10 CO2 emitting countries in 2019 (using the wb2019 array created above)
Plot.plot({
marks: [
Plot.barX(wb2019, {
x: (d) => d.co2 / 1e6,
y: "country",
fill: "blueviolet",
stroke: "gray",
sort: { y: "x", reverse: true, limit: 10 }
}),
Plot.text(wb2019, {
x: (d) => d.co2 / 1e6,
y: "country",
text: (d) => d.co2 / 1e6,
dx: 2,
textAnchor: "start"
}),
Plot.ruleX([0])
],
marginLeft: 200,
x: { domain: [0, 12], grid: true, label: "CO2" }
})
carData = cars
import {aq, op} from "@uwdata/arquero"
import { showMe } from "@observablehq/show-me"
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.
