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Jun 1, 2023
Fork of Introduction to data wrangling and analysis in JavaScript — Session 2 follow-along notebook
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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 }
]
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// Find the miles biked on Friday:

biking[4].miles

// Alternatively: biking[biking.map(d => d.day).indexOf("Friday")].miles
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// For each day, find the average biking speed.
biking.map(x => x.miles / x.time_hr)
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// Only keep objects (rows) where miles is greater than 10:
biking.filter(x => x.miles > 10)
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// Keep rows for Saturday and Monday:
biking.filter(x => x.day == "Saturday" || x.day == "Monday")
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// Keep rows where miles is less than 10 AND time_hr is less than 0.5:
biking.filter(x => x.miles < 10 & x.time_hr < 0.5)
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// Keep rows *except* for Thursday:
biking.filter(x => x.day !== "Thursday")
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// Keep all existing properties; add a new one named 'km' with miles converted to kilometers;
biking_km = biking.map(d => ({...d, km: d.miles * 1.61})) // uses the spread element

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carData
Type Table, then Shift-Enter. Ctrl-space for more options.

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carTable = aq.from(carData)
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// Add your data wrangling (using Arquero) here:

carTable
.select("name","economy (mpg)","cylinders","weight (lb)")
.filter(x => x.cylinders ==4)
.view()

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world_bank_data.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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// 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)
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wbTable = aq.from(wb)
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// Use Arquero verbs to do the same wrangling steps:
wbEAPArquero = wbTable
.select("country", "year", "co2", "region")
.derive({ co2_thousands: (d) => d.co2 / 1000 }) // if you only have one parameter, you dont need (d)
.filter(d => d.year == 2019)
.view()
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// Create a chart of the top 10 CO2 emitting countries in 2019 (using the wb2019 array created above)

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Plot.plot({
marks: [
Plot.barX(wb2019, {x: "co2", y: "country", sort: {y: "x", reverse: true, limit:10}}),
Plot.ruleX([0])
]
})
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carData = cars
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import {aq, op} from "@uwdata/arquero"
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import { showMe } from "@observablehq/show-me"
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