Frequency Tables and Relative Frequency

Install and import#

bash

npm install fintech-algorithms

import { frequencyTablesAndRelativeFrequency } from "fintech-algorithms/foundations/location-ranking-and-exploratory-summaries/frequency-tables-and-relative-frequency";

Signature#

frequencyTablesAndRelativeFrequency(input)

Counts how often each distinct value occurs in a numeric series and expresses those counts as shares of the total.

Parameters#

Name	Type	Notes
`input`	`{ values: number[] }`	The observations to tabulate, under the key `values`. Distinctness is exact numeric equality after `Number` coercion, so this suits repeated discrete levels rather than continuous measurements.

Returns#

{ frequency: Record<string, number>; relativeFrequency: Record<string, number> }

Both maps are keyed by the distinct values rendered as strings, in ascending numeric order. frequency holds raw counts and relativeFrequency holds each count divided by the number of observations.

Errors#

When input is null, an array, or not an object — throws TypeError
When values is missing, is not an array, or is empty — throws RangeError
When any entry of values does not coerce to a finite number — throws RangeError

Complexity: time O(n log n), space O(n).

Worked example#

verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

Input#

input

{
  "values": [1, 2, 2, 4, 9],
  "weights": [1, 1, 2, 1, 1],
  "trimProportion": 0.2,
  "bins": 4
}

Call#

frequencyTablesAndRelativeFrequency(input)

Returns#

object with 2 fields: frequency, relativeFrequency

{
  "frequency": {
    "1": 1,
    "2": 2,
    "4": 1,
    "9": 1
  },
  "relativeFrequency": {
    "1": 0.2,
    "2": 0.4,
    "4": 0.2,
    "9": 0.2
  }
}