Statistician, map-maker, dataviz consultant and trainer, Observable ambassador & R instructor. I enjoy explaining things i love in detail, the most clearly i can, and building live analytical opendataflows.
@ericmauviere
/**
* Head/tail algorithm v1.0 based on Jiang (2019).
* @param {Number[]} values - An array of numerical values.
* @param {Number} nb - Optional number of class. If nb < natural breaks results, upper classes are merged. If nb > natural breaks results, nb is not used and the natural breaks results is returned.
* @returns {Number[]} An array of break points with min and max.
*/
function headtail_js(values, options = {}) {
const { nb, threshold = 0.4, minmax = false } = options;
let min, max;
if (minmax) [min, max] = d3.extent(values);
const breaks = [];
if (minmax) breaks.push(min);
/**
* A recursive function that calculates the next break point.
* @param {Number[]} values - An array of numerical values.
*/
function getBreak(values) {
// Add mean to breaks value
const mean = d3.mean(values);
breaks.push(mean);
// Recursive call to get next break point
const head = values.filter((d) => d > mean);
while (head.length > 1 && head.length / values.length <= threshold)
return getBreak(head);
}
getBreak(values);
// Handle optional number of class
if (nb && nb !== null) {
const diff = nb - 1 - breaks.length;
if (diff < 0) breaks.splice(nb - 1);
}
// Add max to breaks
if (minmax) breaks.push(max);
return breaks;
}
macro_sql = {
const conn = await db.connect()
await conn.query(`
CREATE OR REPLACE FUNCTION headtail_discr(tbname, varnum, nb := 10, threshold := 0.4) AS (
WITH RECURSIVE headtail(break, values_count) AS (
-- Initialisation avec break = moyenne, values_count = nb d'observations
FROM query_table(tbname)
SELECT avg(varnum), -- break
count(*) -- values_count
UNION ALL
-- headtail suivant se réfère à la dernière ligne de la table en cours de croissance
FROM query_table(tbname), headtail
SELECT avg(varnum), -- next break
count(*) head_count -- next values_count
WHERE varnum > headtail.break
GROUP BY ALL
HAVING head_count > 1 AND head_count / headtail.values_count <= threshold
)
FROM headtail
SELECT list(break)[1:nb] AS breaks
)`)
await conn.close()
return "Macro headtail_discr créée dans DuckDB"
}
macro_sql_v2 = {
const conn = await db.connect()
await conn.query(`
CREATE OR REPLACE FUNCTION headtail_discr_v2(tbname, varnum, nb := 10, threshold := 0.4) AS (
WITH RECURSIVE headtail(break, values_count, l_pct_head) AS (
FROM query_table(tbname)
SELECT avg(varnum),
count(*),
[]::double[]
UNION ALL (
WITH t1 AS (
FROM query_table(tbname), headtail
SELECT avg(varnum) next_break,
count(*) h_count,
headtail.values_count v_count,
headtail.l_pct_head l
WHERE varnum > headtail.break
GROUP BY ALL
)
FROM t1
SELECT next_break, h_count,
list_append(l, h_count / v_count) next_l
WHERE h_count > 1 AND list_avg(next_l) <= threshold
)
)
FROM headtail
SELECT list(break)[1:nb] AS breaks
)`)
await conn.close()
return "Macro headtail_discr_v2 créée dans DuckDB"
}
headtail_loyers_sql_v2 = {
loyers2023
macro_sql
const conn = await db.connect()
const rs = await conn.query(`SELECT headtail_discr_v2(loyers2023, loypredm2, threshold := 0.42) breaks`)
await conn.close()
return [...rs.getChild('breaks').get(0)].map(d => parseFloat(d.toFixed(3)))
}
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