// arquero is a lot like dplyr + tidyr
// this import command is pulling in a notebook that defines a bunch of these functions
// you could also do require('arquero') which is more like R's library
import {aq, op} from '@uwdata/arquero'
// vega-lite is similar to ggplot2
// require is pulling in the package vega-lite
vg = require('vega-lite')
penguins = await aq.loadCSV(
'https://gist.githubusercontent.com/slopp/ce3b90b9168f2f921784de84fa445651/raw/4ecf3041f0ed4913e7c230758733948bc561f434/penguins.csv'
)
penguins.view()
// we can do all sorts of dplyr-esq things
// Tip: rollup is like summarize; derive is like mutate
penguins
.groupby('species')
.filter(p => p.body_mass_g > 0)
.rollup({
count: op.count(),
avg_mass_g: op.average('body_mass_g')
})
.view()
import { vl } from "@vega/vega-lite-api"
weatherColors = ["darkgray", "steelblue", "paleturquoise", "gold"]
vl
//.markPoint() // the first two lines are akin to ggplot(data = penguins) + geom_point
.markCircle()
.data(penguins)
.encode( // encode is like aes
vl.x().fieldQ('body_mass_g') // aes(x = body_mass_g)
.scale({domain: [2000, 6500]}) // modify scale attributes, similar to scale_x_axis
.axis({title: "Mass (g)"}),
vl.y().fieldQ('bill_length_mm')
.scale({domain: [30, 65]})
.axis({title: "Bill Length (mm)"})
,
vl.color().fieldN('species').scale({ range: weatherColors }),
vl.size({legend: null}).fieldN('flipper_length_mm'),
vl.tooltip(['species', 'sex', 'year', 'island', 'flipper_length_mm']) // tooltips for free!
).render()
// This step is like a library command
d3 = require('d3')
// this is really the magic, we get to re-use cells from other observable documents as templates,
// over-riding attributes with our own data
import { chart } with { wait_times as data, bins } from "@d3/histogram"
import {slider} from "@jashkenas/inputs"
// load our data
faithful = await d3.tsv(
'https://gist.githubusercontent.com/curran/4b59d1046d9e66f2787780ad51a1cd87/raw/9ec906b78a98cf300947a37b56cfe70d01183200/data.tsv'
)
// this is probably the weirdest part for R users
// think about it like purrr:::map_dbl(faithful, ~.x$eruptions)
wait_times = faithful.map(o => o.eruptions)
// reactivity here is free, we don't have to do input$ to access the reactive value num_bins!
bins = d3.bin().thresholds(num_bins)(wait_times)
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.
