Public
Edited
Dec 7, 2023
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)
showMe(
`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)`,
{ dependencies: { penguinData } }
)
// Find the minimum culmen length in penguinData:
d3.min(penguinData, d => d.culmen_length_mm)
showMe(`d3.min(penguinData, d => d.culmen_length_mm)`, {
dependencies: { penguinData }
})
// Find the range of culmen lengths in penguinData:
d3.extent(penguinData, d => d.culmen_length_mm)
showMe(`d3.extent(penguinData, d => d.culmen_length_mm)`, {
dependencies: { penguinData }
})
// Mean of all body mass (grams) values in penguinData:
d3.mean(penguinData, d => d.body_mass_g)
showMe(`d3.mean(penguinData, d => d.body_mass_g)`, {
dependencies: { penguinData }
})
// Alternatively:
d3.mean(penguinData.map((d) => d.body_mass_g))
// Standard deviation of culmen depth:
d3.deviation(penguinData, d => d.culmen_depth_mm)
showMe(`d3.deviation(penguinData, d => d.culmen_depth_mm)`, {
dependencies: { penguinData }
})
// Count of flipper length values:
d3.count(penguinData, d => d.flipper_length_mm)
showMe(`d3.count(penguinData, d => d.flipper_length_mm)`, {
dependencies: { penguinData }
})
penguinData
Type Table, then Shift-Enter. Ctrl-space for more options.
// Group penguinData by species (store Map as groupSpecies):
groupSpecies = d3.group(penguinData, d => d.species)
showMe(`groupSpecies = d3.group(penguinData, (d) => d.species)`, {
dependencies: { penguinData }
})
// Then get just the Adelies:
groupSpecies.get("Adelie")
showMe(`groupSpecies.get("Adelie")`, {
dependencies: { penguinData, groupSpecies }
})
// Group penguinData by species and island (store as groupSpeciesIsland):
groupSpeciesIsland = d3.group(penguinData, d => d.species, d => d.island)
showMe(
`groupSpeciesIsland = d3.group(penguinData, d => d.species, d => d.island)`,
{
dependencies: { penguinData }
}
)
// Get observations for Adelie penguins on Dream Island:
groupSpeciesIsland.get("Adelie").get("Dream")
showMe(`groupSpeciesIsland.get("Adelie").get("Dream")`, {
dependencies: { penguinData, groupSpeciesIsland }
})
// Find the mean body mass by penguin species:
d3.rollup(penguinData, v => d3.mean(v, d => d.body_mass_g), d => d.species)
penguinData
d3.mean(penguinData, d => d.body_mass_g)
showMe(
`d3.rollup(
penguinData,
(v) => d3.mean(v, (d) => d.body_mass_g),
(d) => d.species
)`,
{
dependencies: { penguinData }
}
)
// 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
)
showMe(
`d3.rollup(penguinData, v => d3.mean(v, d => d.flipper_length_mm), d => d.species, d => d.sex)`,
{
dependencies: { penguinData }
}
)
arrayOfArrays = [
[1, 2],
[3, 4]
]
// 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 }))
showMe(
`d3
.flatRollup(
penguinData,
(v) => d3.mean(v, (d) => d.body_mass_g),
(d) => d.species
)
.map(([species, meanMass]) => ({ species, meanMass }))`,
{
dependencies: { penguinData }
}
)
Plot.plot({
marks: [
Plot.barY(
penguinData,
Plot.groupX(
{ y: "mean" },
{ y: "body_mass_g", x: "species", sort: {x: "y", reverse: true} }
)
)
]
})
showMe(
`Plot.plot({
marks: [
Plot.barY(
penguinData,
Plot.groupX(
{ y: "mean" },
{ y: "body_mass_g", x: "species", sort: { x: "y", reverse: true } }
)
)
]
})`,
{
dependencies: { penguinData }
}
)
penguinData = penguins
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.
