d3.mean, d3.median, and friends / D3

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d3-array

Edited

Oct 24, 2023

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numbers = [83, 32, 14, 52, 31, 66, 12, 11, 0, 78, 60, 97, 47, 37, 91, 58, 48, 55, 98, 45, 64, 1, 17, 39, 82, 24, 5, 40, 61, 27, 57, 34, 56, 26, 30, 36, 43, 80, 85, 68, 75, 50, 59, 44, 18, 19, 88, 87, 41, 90, 4, 81, 94, 89, 93, 22, 3, 67, 13, 35, 96, 16, 7, 15, 20, 76, 63, 49, 25, "95", 86, 99, 28, 62, 71, undefined, 21, 10, 72, 29, 51, 46, 73, 74, 9, 65, 77, 92, 6, 8, 2, 79, 53, 69, 70, 33, 54, 42, 23, 84, 38, NaN, "Fred"]

d3.mean(numbers)

d3.sum(numbers) / d3.count(numbers)

numbers.filter(d => d !== +d)

numbers.length - d3.count(numbers) // The number of missing values!

olympians

d3.mean(olympians, d => d.height)

d3.median(olympians, d => d.height)

{

const now = Date.now();

const year = 1000 * 60 * 60 * 24 * 365.25;

return d3.mean(olympians, d => (now - d.date_of_birth) / year);

}

d3.mean(olympians.filter(d => d.sport === "basketball"), d => d.height)

d3.mean(olympians, d => d.sport === "basketball" ? d.height : NaN)

d3.median([0, 1, 5])

d3.median([0, 1, 2, 5])

d3.variance(olympians, d => d.height)

d3.deviation(olympians, d => d.height)

d3.quantile(olympians, 0.05, d => d.height)

d3.quantile(olympians, 0.95, d => d.height)

sortedHeights = olympians

.map(d => d.height)

.filter(d => d !== null && !isNaN(d))

.sort(d3.ascending)

d3.quantileSorted(sortedHeights, 0.05)

d3.quantileSorted(sortedHeights, 0.95)

d3.deviation(Float32Array.from({length: 1000}, Math.random))

d3.sum(new Set([1, 2, 3]))

d3.quantile(Float64Array.from({length: 1000}, Math.random), 0.1)

frenchteam = olympians.filter(d => d.sport === "handball" && d.sex === "female" && d.nationality == "FRA")

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