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())
}
}
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.
