Published
Edited
Nov 24, 2021
Importers
Scratch
Find datasets that cover neobank & "nonbank" deposit share growth (Chime, Varo, etc.) + credit unions (NCUA) -- CFPB? FFIEC? etc.
geojson = locations.data.map(d => ({
type: "Feature",
geometry: {
type: "Point",
coordinates: [d.data.LONGITUDE, d.data.LATITUDE]
},
properties: d.data.NAME
}))
locations = d3.json(url2, d3.autoType)
locationData = locations.data.map(d => d.data)
locationTable = Inputs.table(locations.data.map(d => d.data), {columns: ["ID", "NAME", "ESTYMD", "ZIP", "CITY", "COUNTY", "LATITUDE", "LONGITUDE"]}, {sort: "ID", reverse: true})
geoLocation = {
const createGeoJSON = () => {
const data = [
{
lat: locationData.getElementById("LATITUDE").value,
lng: locationData.getElementById("LONGITUDE").value
}
];
data[0][locationData.getElementById("propertyname1").value] = document.getElementById("propertyvalue1").value;
data[0][locationData.getElementById("propertyname2").value] = document.getElementById("propertyvalue2").value;
const dataGeoJSON = locationData.parse(data, { Point: ["lat", "lng"] });
locationData.getElementById("output").value = JSON.stringify(dataGeoJSON, null, 4);
}
}
map = {
const container = html`<div style="height:650px;">`;
yield container;
const map = L.map(container).setView([45.2, -69.5], 7);
L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", {
attribution: "© <a href=https://www.openstreetmap.org/copyright>OpenStreetMap</a> contributors"
}).addTo(map);
L.svg().addTo(map)
const overlay = d3.select(map.getPanes().overlayPane)
const svg = overlay.select('svg')
const branches = svg.selectAll('circle')
.attr("class", "branch")
.data(geojson)
.join('circle')
.attr("id", "dots")
.attr("fill", "blue")
.attr("r", 5)
}
L = {
const L = await require("leaflet@1/dist/leaflet.js");
if (!L._style) {
const href = await require.resolve("leaflet@1/dist/leaflet.css");
document.head.appendChild(L._style = html`<link href=${href} rel=stylesheet>`);
}
return L;
}
bankTable = Inputs.table(banks.data.map(d => d.data))
simpleBankTable = Inputs.table(banks.data.map(d => d.data), {columns: ["ID", "NAME", "ZIP", "OFFICES", "DEPDOM"]}, {sort: "DEPDOM", reverse: true})
viewof filteredRows = Inputs.table(filteredBanks)
viewof deposits = Inputs.range([0,1500000], {value: 1200000, step: 100, label: "Minimum Deposits"})
chart = addTooltips(Plot.plot({
marks: [
Plot.barY(filteredBanks, {x: "ID", y: "DEP", title: "NAME", fill: colorScale})
],
width: 800
}))
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.