Public
Edited
Apr 30, 2023
penguinData
Type Table, then Shift-Enter. Ctrl-space for more options.
// Add your code here to do the wrangling steps above!
p_df = aq.from(penguinData)
p_df
.derive({massKilograms: d => d.body_mass_g / 1000, billRatio: d => d.culmen_length_mm / d.culmen_depth_mm})
.select("species", "massKilograms", "billRatio")
.filter(d => d.billRatio > 3.5)
.view()
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)
// 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.
// 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")
// Find the mean body mass by penguin species:
d3.rollup(penguinData, v => d3.mean(v, d => d.body_mass_g), b => b.species)
// Find the mean flipper length by penguin species and sex:
d3.rollup(penguinData, v => d3.mean(v, d => d.flipper_length_mm), b => b.species, b => b.sex)
arrayOfArrays = [
[1, 2],
[3, 4]
]
arrayOfArrays.map(([firstNumber, secondNumber]) => ({
firstNumber,
secondNumber
}))
d3.flatRollup(penguinData, v => d3.mean(v, d => d.body_mass_g), b => b.species)
// 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), b => b.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"
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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.
