Public
Edited
Apr 26, 2023
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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
biking[biking.map(d => d.day).indexOf("Friday")].miles
// For each day, find the average biking speed.
biking.map(v => v.miles / v.time_hr)
biking.map(d => ({ session: d.day, speed: d.miles / d.time_hr })).filter(d => d.speed > 0)
// Only keep objects (rows) where miles is greater than 10:
biking.filter(d => d.miles > 10)
//biking.filter(d => d.miles < 10 && d.miles != 0)
// 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_km = biking.map(d => ({ ...d, km: Math.round(d.miles * 1.61) }))
biking_km
Type Table, then Shift-Enter. Ctrl-space for more options.
carData
Type Table, then Shift-Enter. Ctrl-space for more options.
carTable = aq.from(carData)
// Add your data wrangling (using Arquero) here:
carTable // can hit enter here for "vertical" structure/organization
.select("name", "economy (mpg)", "cylinders", "weight (lb)")//.view()
.filter(d => d.cylinders == 4)
.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:
wbEAPArquero = 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)
Type JavaScript, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
carData = cars
import {aq, op} from "@uwdata/arquero"
import { showMe } from "@observablehq/show-me"
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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.
