penguinData
Type Table, then Shift-Enter. Ctrl-space for more options.
// Add your code here to do the wrangling steps above!
penguinData
.map((d) => ({
species: d.species,
massKilograms: d.body_mass_g / 1000,
billRatio: d.culmen_length_mm / d.culmen_depth_mm
}))
.filter((d) => d.billRatio > 3.5)
// Find the minimum culmen length in penguinData:
d3.min(penguinData, (d) => d.culmen_length_mm)
// Find the range of culmen lengths in penguinData:
d3.extent(penguinData, (d) => d.culmen_length_mm) // watch out, it's not d3.range()
// Mean of all body mass (grams) values in penguinData:
d3.mean(penguinData, (d) => d.body_mass_g)
// Alternatively:
// Standard deviation of culmen depth:
d3.deviation(penguinData, (d) => d.culmen_depth_mm)
// Count of flipper length values:
d3.count(penguinData, (d) => d.flipper_length_mm)
penguinData
Type Table, then Shift-Enter. Ctrl-space for more options.
penguinData
// Group penguinData by species (store Map as groupSpecies):
groupSpecies = d3.group(penguinData, (d) => d.species)
// Then get just the Adelies:
groupSpecies.get("Adelie")
// Group penguinData by species and island (store as groupSpeciesIsland):
groupSpeciesIsland = d3.group(
penguinData,
(d) => d.species,
(d) => d.island
)
// Get observations for Adelie penguins on Dream Island:
groupSpeciesIsland.get("Adelie").get("Dream")
groupSpeciesIsland.get("Chinstrap").get("Dream")
// Find the mean body mass by penguin species:
d3
.rollup(
penguinData,
(v) => d3.mean(v, (d) => d.body_mass_g),
(d) => d.species
)
.get("Adelie") // if I just want one pengui
// The same with flatrollup
d3
.flatRollup(
penguinData,
(v) => d3.mean(v, (d) => d.body_mass_g),
(d) => d.species
)
// Find the mean flipper length by penguin species and sex:
d3.rollup(
penguinData,
(v) => d3.mean(v, (d) => d.flipper_length_mm),
(d) => d.species,
(d) => d.sex
)
// do the same with Arquero
penguinTable = aq.from(penguinData)
penguinTable
.groupby("species")
.rollup({
mean_mass: (d) => d3.mean(d.body_mass_g),
median_mass: (d) => d3.median(d.body_mass_g),
min_mass: (d) => d3.min(d.body_mass_g),
max_mass: (d) => d3.max(d.body_mass_g)
})
.view()
penguinTable
.groupby("species")
.rollup({
mean_mass: (d) => op.mean(d.body_mass_g),
sd_mass: (d) => op.stdev(d.body_mass_g)
})
.view()
arrayOfArrays = [
[1, 2],
[3, 4]
]
arrayOfArrays.map(([firstNumber, secondNumber]) => ({
firstNumber,
secondNumber
}))
// Use flatRollup to find mean body mass by penguin species, then map to get an array of objects:
d3
.flatRollup(
penguinData,
(v) => d3.mean(v, (d) => d.body_mass_g),
(d) => d.species
)
.map(([species, meanMass]) => ({ species, meanMass }))
// Create a new bar chart with Observable Plot to visualize mean body mass by species:
Plot.plot({
marks: [
Plot.barY(
penguinData,
Plot.groupX(
{ y: "mean" },
{ y: "body_mass_g", x: "species", sort: { x: "y", reverse: true } }
)
)
]
})
import { showMe } from "@observablehq/show-me"
import { aq, op } from "@uwdata/arquero"
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.
