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Intro to Data Wrangling
Edited
Feb 19
1
Intro to Data Wrangling
Introduction to data wrangling and analysis in JavaScript - Session 3 Follow-along NotebookIntroduction to data wrangling and analysis in JavaScript — Session 1 Follow-along version
Introduction to data wrangling and analysis in JavaScript — Session 2 follow-along notebook
Introduction to data wrangling and analysis in JavaScript - Session 4 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(d => d.miles / d.time_hr)
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// Only keep objects (rows) where miles is greater than 10:
biking.filter(d => d.miles > 10)
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// Keep rows for Saturday and Monday:
biking.filter(d => d.day == "Saturday" || d.day == "Monday")
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// 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 < .5)
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// Keep rows *except* for 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}))
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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(d => d.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:

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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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