Public
Edited
Apr 4, 2024
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)
penguinData
Type Table, then Shift-Enter. Ctrl-space for more options.
// Find the minimum culmen length in penguinData:
d3.min(penguinData, (d) => d.culmen_length_mm)
// Alternatively:
d3.mean(penguinData.map((d)=> d.body_mass_g))
// 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)
// Group penguinData by species (store Map as groupSpecies): group value by key, identify unique in column and group object by group, return Map data structure (has key:value pairs like object, but key can be things other than string, iterable, remember element insertion order, and tell you size/ key:valu pairs)
groupSpecies = d3.group(penguinData, d=> d.species)
// Then get just the Adelies:
//Map data structure is good to easily get specific arrays with just the Key with ".get", basically slice up data for analysis
groupSpecies.get("Adelie")
// Group penguinData by species and island (store as groupSpeciesIsland): you can group with multiple conditions
groupSpeciesIsland = d3.group(
penguinData,
(d) => d.species,
(d) => d.island
)
// Get observations for Adelie penguins on Dream Island: get adelie specie just for dream island, get subset of subset, good for digging into specific groups in data
groupSpeciesIsland.get("Adelie").get("Dream")
//d3.map(penguinData)
// mean of mass for all pengui together
d3.mean(penguinData.map(d=>d.body_mass_g), d=>d)
d3.mean(penguinData, d=>d.body_mass_g)
// Find the mean body mass by penguin species:
d3.rollup(
penguinData,
(v) => d3.mean(v, (d) => d.body_mass_g),
(d, i) => d.species
)
// Find the mean flipper length by penguin species and sex: get transformed value with more than 1 grouping keys
d3.rollup(
penguinData,
(v) => d3.mean(v, (d) => d.flipper_length_mm),
(d) => d.species,
(d) => d.sex
)
arrayOfArrays = [
[1, 2],
[3, 4]
]
//transfer the values into objects, and make the bigger category of object the key
arrayOfArrays.map(([firstNumber, secondNumber]) => ({
firstNumber,
secondNumber
}))
// Use flatRollup to find mean body mass by penguin species (array of array)
d3
.flatRollup(
penguinData,
(v) => d3.mean(v, (d) => d.body_mass_g),
(d) => d.species
)
//then map to get an array of objects:
.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(
//specify reducer straight in observable plot instead of in d3 ahead
{ y: "mean" },
{ y: "body_mass_g", x: "species", sort: { x: "y", reverse: true } }
)
)
]
})
import { showMe } from "@observablehq/show-me"
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