Public
Edited
Nov 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[4].miles
// Alternatively: biking[biking.map(d => d.day).indexOf("Friday")].miles
showMe(`biking[4].miles`, {dependencies: {biking}})
// For each day, find the average biking speed.
biking.map(d => d.miles / d.time_hr)
showMe(`biking.map(d => d.miles / d.time_hr)`, {dependencies: {biking}})
// Only keep objects (rows) where miles is greater than 10:
biking.filter(d => d.miles > 10)
showMe(`biking.filter(d => d.miles > 10)`, {dependencies: {biking}})
// Keep rows for Saturday and Monday:
biking.filter(d => d.day == "Saturday" || d.day == "Monday")
showMe(`biking.filter(d => d.day == "Saturday" || d.day == "Monday")`, {dependencies: {biking}})
// 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)
showMe(`biking.filter(d => d.miles < 10 && d.time_hr < 0.5)`, {dependencies: {biking}})
// Keep rows *except* for Thursday:
biking.filter(d => d.day != "Thursday")
showMe(`biking.filter(d => d.day != "Thursday")`, {dependencies: {biking}})
// 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}))
showMe(`biking_km = biking.map(d => ({...d, km: d.miles * 1.61}))`, {dependencies: {biking}})
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['economy (mpg)'] != null)
.derive({weight_kg: d => d['weight (lb)'] * 0.45})
.orderby('economy (mpg)')
.rename({'economy (mpg)': 'mpg'})
.view()
showMe(`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()`,
{dependencies: {carTable}})
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.c02 / 1000
})).filter(d => d.year == 2019)
showMe(`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
)`,
{dependencies: {wb}})
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()
showMe(`wbEAPArquero = wbTable
.select("country", "year", "co2", "region")
.derive({ co2_thousands: (d) => d.co2 / 1000 })
.filter(d => d.year == 2019)
.view()`,
{dependencies: {wbTable}})
// 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 },
fill: "steelblue" // Change the color to steel blue or any other color you prefer
}),
Plot.ruleX([0])
]
});
showMe(
`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" }
})`,
{ dependencies: { wb2019 } }
)
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.
