Public
Edited
May 14, 2023
1 fork
Importers
tsPeso
comparisonChart = {
comparisionCard(SelectedTime, { config: configImpact, colorScale: brushesScale})
viewof attribsImpact = {
const allAttribs = Object.keys(data[0]).filter(a => !isNaN(data[0][a])); // only use quant attribs
return searchCheckbox(allAttribs, {
label: "Atributos",
height: 150,
value: this?.value /* keep previous value */ || [
"V389",
"CD12"
]
});
}
groupsScale = d3.scaleOrdinal(["#1b9e77","#d95f02","#7570b3","#e7298a","#66a61e","#e6ab02","#a6761d","#666666"])
brushesScale = d3.scaleOrdinal(["#fdc086","#386cb0","#f0027f","#bf5b17","#666666"])
{
G1; G2 // make it reactive on filter updates
return Swatches(groupsScale, {label: "Grupos"})
}
curvaspc = FileAttachment("curvasPC.json").json()
curvaspeso = FileAttachment("curvasPeso.json").json()
curvastalla = FileAttachment("curvasTalla.json").json()
{
G1; G2 // make it reactive on filter updates
return Swatches(brushesScale, {label: "Grupos"})
}
configImpact
maxTimelines = 2000;
dataFiltered = {
const tsPesoE = viewof tsPeso
const tsTallaE = viewof tsTalla;
const tsPCE = viewof tsPC
let dataFiltered = getData([tsPeso, tsTalla, tsPC]);
tsPesoE.ts.setExternalSelection(dataFiltered)
tsTallaE.ts.setExternalSelection(dataFiltered)
tsPCE.ts.setExternalSelection(dataFiltered)
return dataFiltered
}
data = FileAttachment("KMC-50k-Base 1993-2022 Jul -20220929.csv.zip")
.zip()
.then((res) => res.file("KMC-50k-Base 1993-2022 Jul -20220929.csv").csv({ typed: true }))
selectedStages = {
let rawData = transformData(G1G2, "Code", stages, "__group");
rawData = rawData.filter(d => d["__time"] > 24 && d["__time"] < 100 )
rawData.forEach((d) => {
if (d["peso"] > 20000 || d["peso"] < 100) {
d["peso"] = null;
}
if (d["talla"] > 80 || d["talla"] < 20) {
d["talla"] = null;
}
if (d["PC"] > 50 || d["PC"] < 10) {
d["PC"] = null;
}
});
return rawData
}
G1G2 = G1.concat(G2)
SelectedTime = {
let aux = new Map();
dataFiltered.forEach((selectedIds, id) => {
let gData = dataF.filter((d) => selectedIds.has(d["Code"]) && d["V389"] < 20000);
aux.set(id, gData);
});
return aux;
}
dataF = data.filter(d => d["ERN_Ballard"] < 50 && d["ERN_Ballard"] > 20)
updateG1 = G1.forEach(d => d["__group"] = nameG1)
updateG2 = G2.forEach(d => d["__group"] = nameG2)
import {Treemap} from "@d3/treemap"
import {searchCheckbox} from "@john-guerra/search-checkbox"
import {transformData,stages} from "@ivelascog/to-stages"
import {StatisticalCard, config} from "@ivelascog/statistical-card"
import {comparisionCard} from "@ivelascog/comparison-cards"
import { Swatches } from "@d3/color-legend"
import {FacetedSearch} from "@john-guerra/faceted-search"
import {ViolinPlot, FacetViolinPlot} from "@john-guerra/violin-plot"
TimeSearcher = {
try {
return await require(`http://localhost:8080/dist/TimeSearcher.js?${Date.now()}`)
} catch (e) {
return await require(await FileAttachment("TimeSearcher@1.js").url())
}
}
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.