I love to build dataviz for insight discovery. I also love to put technology to the service of humanity
Public
Edited
May 22, 2023
// viewof selectedDiseases = searchCheckbox(diseases, {label: "Select Diseases to show", value: diseases, height: 150})
viewof selected = TimeSearcher({
data: filteredParsedMetadata,
id: d => d.disease,
x: d => d.date,
y: d => d.count,
width: width*.8,
height: 300,
hasDetails: false,
detailsHeight: 100,
yScale: () => d3.scalePow().exponent(0.5)
})
diseasesSelectedByTimelines
selected
makeHist = (data, attr) => {
const bar = vl.markBar().encode(vl.y().fieldN(attr), vl.x().count());
const all = bar.data(referenceMetadata).encode(vl.color().value("#eee"));
const selected = bar.data(data);
return vl.layer(selected).title(attr).render();
}
referenceMetadata = diseases.map(d => mapMetadata.get(d))
metadata = {
const geos = d3.tsvParseRows(
await FileAttachment("results_out.json_geo_regions.txt").text()
);
const diseases = d3.tsvParseRows(
await FileAttachment("results_out.json_disease.txt").text()
);
const dates = d3.tsvParseRows(
await FileAttachment("results_out.json_dates.txt").text()
);
return diseases.map((d, i) => ({
id: d[0],
disease: d[1],
geo: geos[i][1],
date: dates[i][1]
}));
}
parsedMetaData2 = metadata
.map((d) => ({ ...d, pdate: new Date(d.date) }))
.filter(
(d) =>
d.pdate instanceof Date &&
!isNaN(d.pdate) &&
d.pdate >= new Date(1980, 0, 0) &&
d.pdate <= new Date(2024, 0, 0)
)
d3.rollups(
parsedMetaData2,
(v) => d3.count(v, (d) => d.pdate),
(d) => d.disease,
// (d) => d.geo,
(d) => d.pdate,
)
navio(parsedMetaData2)
vl.markLine({tooltip: true})
.encode(vl.x().fieldT("date").timeUnit("yearweek"), vl.y().count(), vl.detail().fieldN("disease"))
.width(width*.8)
.data(navioSelected)
.render()
clusters = d3.tsvParseRows(await FileAttachment("clusters_1000@1.txt").text())
vl.markBar().encode(vl.y().fieldQ("0"), vl.x().count()).data(clusters).render()
vl
.markBar()
.encode(vl.y().fieldQ("id"), vl.x().count())
.data(
metadata.map((d) => {
d.id = +d.id;
return d;
})
)
.render()
// metadata.map((d) => {
// d.id = +d.id;
// return d;
// })
mapMetadata = new Map(metadata.map(d => [d.disease, d] ))
// data = FileAttachment("timelines.csv")
// .csv({ typed: true })
// .then((res) =>
// res.map((d) => {
// d.disease = d.name.split("_").slice(1).join(" ");
// const metadata = mapMetadata.get(d.disease)
// return Object.assign(d, metadata);
// })
// )
import {navio} from "@john-guerra/navio"
import {TimeSearcher} from "@ivelascog/timesearcher-plus-usabilidad"
import {multiAutoSelect} from "@john-guerra/multi-auto-select"
import {searchCheckbox} from "@john-guerra/search-checkbox"
import {TimeParser, flatTree} from "@john-guerra/time-parser"
import {FacetedSearch} from "@john-guerra/faceted-search"
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.