Published
Edited
Apr 28, 2020
4 stars
chart = vl.markRect({tooltip: {"content": "encoding"}})
.data(pairedInterests)
.encode(
vl.x().fieldN('a').sort(interestsSorted),
vl.y().fieldN('b').sort(interestsSorted),
vl.color().fieldQ('n'),
// vl.tooltip().fieldN('tooltip')
)
.render()
vl.markBar({tooltip: {"content": "encoding"}})
.data(interestsFiltered)
.encode(
vl.y().fieldN('interest').sort('-x'),
vl.x().fieldQ('*').aggregate("count"),
vl.color().fieldQ('*').aggregate("count"),
).render()
survey = d3.csv("https://futureofcoding.org/episodes/046/survey-results.csv")
interests = survey
.map(x=>x["What FoC topics interest you most?"])
.map(x=>x.split(';'))
.flatMap((x,id) => x.map(interest=>({id, interest})))
n_respondents = survey
.filter(x=> workingOn.includes(x[filterQ])).length
_pairedInterests = survey
.filter(x=> workingOn.includes(x[filterQ]))
.map(x=>x["What FoC topics interest you most?"])
.map(x=>x.split(';'))
.flatMap(x=> d3.cross(x,x))
.map(([a,b]) => ({a,b}))
//.filter(x => x.a <= x.b)
//.filter(x=> interestsSorted.indexOf(x.a) <= interestsSorted.indexOf(x.b))
.filter(x=> interestsSorted.indexOf(x.a) < interestsSorted.length - 12)
.filter(x=> interestsSorted.indexOf(x.b) < interestsSorted.length - 12)
tooltip = x => `${x.n} people who are interested in ${x.a} are also interested in ${x.b}`
pairedInterests = groupedCount(_pairedInterests).map(x=> ({...x, tooltip: tooltip(x)}))
interestsFiltered = survey
.filter(x=> workingOn.includes(x[filterQ]))
.map(x=>x["What FoC topics interest you most?"])
.map(x=>x.split(';'))
.flatMap((x,id) => x.map(interest=>({id, interest})))
counts = _(interests).countBy("interest").toPairs().map(([name, count]) => ({name, count})).value()
interestsSorted = _(counts).sortBy('count').reverse().map(x=>x.name).value()
// what I'm doing instead of dplyr::count(a,b)
groupedCount = X => {
const counts = {};
for (const x of X) {
const key = JSON.stringify(x);
if (!(key in counts)) {
counts[key] = 1;
} else {
counts[key] ++;
}
}
const f = (val,key) => {
return { ...JSON.parse(key), n: val}
}
return _.map(counts, f)
}
d3 = require('d3@5')
import {vl} from '@vega/vega-lite-api'
_ = require('lodash')
import {checkbox, select} from '@jashkenas/inputs'
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.