Economic demographer by training; data and coding nerd at heart.
Public
Edited
Apr 20, 2023
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[biking.map(d => d.day).indexOf("Friday")].miles
// Alternatively: biking[biking.map(d => d.day).indexOf("Friday")].miles
biking[i].miles
i = biking.map(d => d.day).indexOf("Friday")
// For each day, find the average biking speed.
average_speed = biking.map(d => d.miles / d.time_hr)
// Only keep objects (rows) where miles is greater than 10:
// .filter() also iterates over an array.
biking.filter(d => d.miles > 10).map(d => d.day)
// 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;
// ({}) indicates that the map callback function will return a new object
// the ...d is the "spread" operator, which includes all the previous elements in the object
biking_km = biking.map(d => ({...d, km: d.miles * 1.61}))
carData
Type Table, then Shift-Enter. Ctrl-space for more options.
carTable = aq.from(carData)
// Add your data wrangling (using Arquero) here:
carTable
.select("name", "economy (mpg)", "cylinders", "weight (lb)")
.filter((d) => d.cylinders === 4 && d["weight (lb)"] != null)
.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:
// (d), parentheses are not necessary when there's a single parameter, but they are necessary if there are multiple parameters.
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: "co2", y: "country", sort: {y: "x", reverse: true, limit: 10}}),
Plot.ruleX([0])
]
})
carData = cars
import {aq, op} from "@uwdata/arquero"
import { showMe } from "@observablehq/show-me"
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