Public
Edited
Apr 20, 2024
fishing = [{river: "San Juan", species: "rainbow", lengthInches: 18.2},
{river: "San Juan", species: "brown", lengthInches: 14.1},
{river: "San Juan", species: "brown", lengthInches: 21.5},
{river: "Rio Grande", species: "brown", lengthInches: 9.0},
{river: "Rio Grande", species: "brown", lengthInches: 12.5},
{river: "Rio Grande", species: "cutthroat", lengthInches: 10.6},
{river: "Rio Grande", species: "brown", lengthInches: 14.5},
{river: "Rio Chama", species: "rainbow", lengthInches: 8.5},
{river: "Rio Chama", species: "rainbow", lengthInches: 11.5}]
fishing
Type Table, then Shift-Enter. Ctrl-space for more options.
//Group the data by species, then get an array of objects for rainbow trout.
groupSpecies = d3.group(fishing, (d) => d.species).get("rainbow")
// Use Array.filter to get a subset only for rainbow trout in fishing:
fishing.filter((d) => d.species === "rainbow")
// Find the mean trout length by river:
riverTroutSize = d3.rollup(
fishing,
(v) => d3.mean(v, (d) => d.lengthInches),
(d) => d.river
)
// Get just the mean trout size for the San Juan River:
riverTroutSize.get("San Juan")
// Load the us_imports.csv file (attached in this notebook):
us_imports = FileAttachment("us_imports.csv").csv({typed: true})
us_imports
Type Table, then Shift-Enter. Ctrl-space for more options.
// Return an array of all values in the 'value' property of us_imports, log transformed
//Math.log, Math.exp, Math.sqrt, you can do this with ".map" or with ".plot"
us_imports.map((d) => Math.log(d.value))
// Create a new array, usLogValue, containing all existing properties and ADDING the log transformed value as a property named logValue:
usLogValue=us_imports.map(d => ({...d, logValue: Math.log(d.value)}))
Plot.plot({
marks: [
Plot.rectY(usLogValue, Plot.binX({y: "count"}, {x: "logValue"})),
Plot.ruleY([0])
]
})
Plot.plot({
marks: [
Plot.rectY(
us_imports,
Plot.binX({ y: "count" }, { x: (d) => Math.log(d.value) })
),
Plot.ruleY([0])
]
})
usLogValue
X
sum
logValue
Y
Sector
Color
—
Size
—
Facet X
year
Facet Y
—
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.
Plot.plot({
marks: [
Plot.frame({ strokeOpacity: 0.1 }),
Plot.barX(
usLogValue,
Plot.groupY(
{ x: "sum" },
{ fx: "year", x: "logValue", y: "Sector", tip: true }
)
),
Plot.ruleX([0])
]
})
viewof pickSector = Inputs.radio(
us_imports.map((d) => d.Sector),
{ label: "Select one", value: "A", unique: true }
)
viewof pickColor = Inputs.color({
label: "Pick your Favorite Background color",
value: "#c0ff33"
})
Plot.plot({
marks: [
Plot.barX(
us_imports,
Plot.groupY(
{ x: "sum" },
{
x: "value",
y: "Sector",
fill: (d)=>(d.Sector === pickSector ? "red" : pickColor),
sort: { y: "x", reverse: true }
}
)
)
],
x: { tickFormat: ".1s" },
width: 900
})
import { showMe } from "@observablehq/show-me"
import { addTooltips } from "@mkfreeman/plot-tooltip"
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.
