The avatar for @ericmauviere
Éric Mauvière
Statistician, map-maker, dataviz consultant and trainer, Observable ambassador & R instructor. I enjoy explaining things i love in detail, the most clearly i can, and building live analytical opendataflows. @ericmauviere
Public
Edited
Jul 19, 2023
Fork of Data science workflows in JavaScript - Session 2 Key
1 star
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
db = {
const gentoo = {
url : () => "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.220.7&entityid=e03b43c924f226486f2f0ab6709d2381",
mimeType: 'text/csv',
name: 'gentoo'
}

const adelie = {
url : () => "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.219.5&entityid=002f3893385f710df69eeebe893144ff",
mimeType: 'text/csv',
name: 'adelie'
}

const chinstrap = {
url : () => "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.221.8&entityid=fe853aa8f7a59aa84cdd3197619ef462",
mimeType: 'text/csv',
name: 'chinstrap'
}
return DuckDBClient.of({
gentoo: {file: gentoo},
adelie: {file: adelie},
chinstrap: {file: chinstrap}
})

// ou cette écriture plus compacte
const myCSVFiles = {
gentoo : "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.220.7&entityid=e03b43c924f226486f2f0ab6709d2381",
adelie: "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.219.5&entityid=002f3893385f710df69eeebe893144ff",
chinstrap: "https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.221.8&entityid=fe853aa8f7a59aa84cdd3197619ef462"
}

const myCSVFilesForDuckDB = Object.entries(myCSVFiles).map(d => ({ url: () => d[1], mimeType: 'text/csv', name: d[0] }))
.map(d => [d.name, {file : d}])
return DuckDBClient.of(
Object.fromEntries(myCSVFilesForDuckDB)
)
}
Insert cell
db.query('SHOW TABLES')
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
db
WITH penguinsCombo AS (
SELECT * FROM gentoo
UNION SELECT * FROM adelie
UNION SELECT * FROM chinstrap
)
--SELECT * replace (REGEXP_EXTRACT(Species, '\w+') as Species) from penguinsCombo
SELECT island, str_split(Species, ' ')[1] as species,
CASE WHEN sex is null OR sex = '.' THEN NULL ELSE lcase(sex) END AS sex,
"Culmen Length (mm)" AS bill_length_mm, "Culmen Depth (mm)" AS bill_depth_mm,
"Body Mass (g)" AS body_mass_g, "Flipper Length (mm)" AS flipper_length_mm
FROM penguinsCombo
WHERE bill_length_mm IS NOT null
Insert cell
Insert cell
Insert cell
penguins
SELECT species, AVG(bill_length_mm / bill_depth_mm) AS bill_ratio
FROM penguins
WHERE sex = 'female'
GROUP BY ALL
ORDER BY 1
Insert cell
Insert cell
Insert cell
Insert cell
penguins
X
bill_length_mm
Y
bill_depth_mm
Color
species
Size
body_mass_g
Facet X
—
Facet Y
—
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.

Insert cell
Insert cell
Plot.plot({
color: { legend: true },
marks: [
Plot.dot(penguins, {
x: "bill_length_mm",
y: "bill_depth_mm",
fill: "species",
tip: true,
r: "body_mass_g",
opacity: 0.5
}),
Plot.frame()
],
color: {range: ["teal", "darkorange", "orchid"]},
r: {domain: d3.extent(penguins.map(d => d.body_mass_g)), range: [1, 20]},
grid: true
})
Insert cell
Insert cell
Insert cell
import {PlotMatrix} with {data} from "@observablehq/autoplot-matrix"
Insert cell
Insert cell
data = penguins
Insert cell
PlotMatrix(data)
Insert cell
Insert cell
Insert cell
Insert cell
Plot.plot({
marks: [
Plot.rectY(
penguins,
Plot.binX(
{ y: "count" },
{ x: (d) => d[chooseVar], fill: "species", fy: "species"}
)
),
Plot.frame()
],
marginRight: 100
})
Insert cell
penguins
X
body_mass_g
Y
—
Color
—
Size
—
Facet X
sex
Facet Y
species
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.

Insert cell
viewof chooseWidth = Inputs.range([50, 500], {label: "Bin width:", step: 10})
Insert cell
Plot.plot({
marks: [
Plot.frame({ strokeOpacity: 0.1 }),
Plot.rectY(
penguins,
Plot.binX(
{ y: "count" },
{ fx: "sex", fy: "species", x: "body_mass_g", tip: true, fill: "species", interval: chooseWidth }
)
),
Plot.ruleY([0])
],
marginRight: 100,
width: 800
})
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
import {scale} from "@chrispahm/hierarchical-clustering"
Insert cell
penguins
Type Table, then Shift-Enter. Ctrl-space for more options.

Insert cell
penguinsComplete = penguins.filter((d) => d.bill_length_mm !== null)// && d.sex == "female")
Insert cell
penguinsScale = scale(penguinsComplete)
Insert cell
penguinsArray = penguinsScale.map(
(d) => [d.bill_length_mm, d.bill_depth_mm, d.body_mass_g, d.flipper_length_mm]
)
Insert cell
Insert cell
Insert cell
penguinsClusters = ML.KMeans(penguinsArray, 3)
Insert cell
clusters = penguinsClusters.clusters.map(d => ({clusterNo: d}))
Insert cell
Insert cell
db2 = DuckDBClient.of({penguinsComplete, clusters})
Insert cell
db2
select * from penguinsComplete POSITIONAL JOIN clusters
Insert cell
Insert cell
myChart = Plot.plot({ // Remember to name if you want to use embeds!
marks: [
Plot.text(penguinsKmeans, {
x: "body_mass_g",
y: "flipper_length_mm",
text: "clusterNo",
fontSize: "15px",
fontWeight: 500,
fill: "species",
tip: true
})
],
color: { legend: true }
})
Insert cell
penguinsKmeans
select species, clusterno, count(*) as eft from penguinskmeans group by all
Insert cell
Insert cell
Insert cell
ML = require("https://www.lactame.com/lib/ml/6.0.0/ml.min.js")
Insert cell
penguinsKeyCopy = penguins
Insert cell

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.
Learn more