Image from https://www.flickr.com/photos/ain-t_looking_for_nothing/8757900003/
Plot.plot({
width: 1200,
height: 1000,
insetLeft: 10,
insetRight: 60,
r: {range: [0, 80]},
marks: [
Plot.frame({anchor: "bottom"}),
Plot.dot(
fails,
Plot.dodgeY({
sort,
anchor: "bottom",
padding: 2,
x: "Date",
r: Assets, // N.B. sqrt scale
title: (d) => `${d["Bank Name"]}\n${(d[Assets] / 1000).toFixed(1)}B`,
fill: "#ddd",
stroke: "#000",
strokeWidth: 1
})
),
Plot.text(
fails,
Plot.filter((d) => d.Assets >= 2000, Plot.dodgeY({
sort,
anchor: "bottom",
padding: 2,
x: "Date",
lineWidth: 5,
r: Assets,
text: (d) => d.Assets > 12900
? `${d["Bank Name"]}\n${(d[Assets] / 1000).toFixed(0)}B`
: `${(d[Assets] / 1000).toFixed(1)}`,
pointerEvents: "none",
fill: "#000",
stroke: "#ddd"
}))
)
]
})
fails = (await FileAttachment("bfb-data@1.csv").csv({array: true}))
.slice(1, -2)
.map((d) => {
const date = parseDate(d[2]);
const assets = parseAssets(d[3]);
return {
"Bank Name": d[0].split(", ")[0],
"City, State": d[0].split(", ").slice(1).join(", "),
"Date": date,
"Assets": assets,
"Assets (adj.)": adjustForInflation(date, assets),
"Acquirer": d[5]
};
}).concat(pre2000)
pre2000 = (function () {
// Data from https://www.fdic.gov/bank/historical/managing/chronological/appendices/index.html --> "FDIC at a Glance"
var raw = `Total Failed and Assisted Banks 11 10 42 48 80 120 145 203 279 207 169 127 122 41 13 6 6 1 3 8 7 4 11 3
Total Assets of Failed and Assisted Banks $8,192.4 $4,947.4 $11,722.6 $7,191.7 $43,432.5 $8,977.3 $8,069.1 $9,407.0 $53,899.4 $28,935.0 $16,937.7 $64,635.0 $45,391.1 $3,828.9 $1,463.9 802.1 232.6 27.9 290.2 1592.2 414.5 1821.8 2914.5 1138`;
var table = raw.split("\n").map((s) =>
s
.split("\t")
.slice(1)
.map((s) => Number(s.replace(/[^0-9.]/g, "")))
);
table = table[0].map((elt, i) => [1980 + i, elt, table[1][i]]);
function randomDateInYear(year) {
const date = new Date(`${year}-01-01`);
const millis = date.valueOf();
const randomDate = new Date(
millis + Math.random() * (365 * 24 * 3600 * 1e3)
);
return new Date(randomDate).toISOString().slice(0, 10);
}
var table2 = table.flatMap(([year, numFail, totalAssets]) =>
Array.from(Array(numFail), (_, i) => [
`${year}-${i + 1}`,
"(None)",
randomDateInYear(year),
totalAssets / numFail,
0,
"(None)",
])
).filter(row => row[0] < '2001'); // Nice individualized FRED data starts in 2001
return table2.map((row) => {
const date = pre2000ParseDate(row[2]);
const assets = row[3];
return {
"Bank Name": row[0],
"City, State": "",
Date: date,
Assets: row[3],
"Assets (adj.)": adjustForInflation(date, assets),
Acquirer: "",
};
});
})();
pre2000ParseDate = d3.utcParse("%Y-%m-%d");
parseDate = d3.utcParse("%d-%b-%y")
parseAssets = (x) => parseFloat(x.replace(/[^\d.]/g, ""))
findCpi = {
const bisector = d3.bisector((d) => d.DATE);
return (date) => cpiaucsl[bisector.center(cpiaucsl, date)].CPIAUCSL;
}
adjustForInflation = {
const currentCpi = findCpi(new Date("2023-05-01"));
return (date, value) => currentCpi / findCpi(date) * value;
}
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.