R's cor in TypeScript

How to use

Install via yarn add locutus and import: import { cor } from 'locutus/r/stats/cor'.

Or with CommonJS: const { cor } = require('locutus/r/stats/cor')

Use a bundler that supports tree-shaking so you only ship the functions you actually use. Vite, webpack, Rollup, and Parcel all handle this. For server-side use this is less of a concern.

Examples

These examples are extracted from test cases that automatically verify our functions against their native counterparts.

#	code	expected result
1	cor([1, 2, 3], [1, 5, 7])	0.9819805060619659
2	cor([1, 2, 3], [7, 5, 3])	-1
3	cor([1, 2, 3], [2, 4, 6])	1

Notes

• Implements R’s default Pearson correlation for two plain numeric vectors.

Dependencies

This function uses the following Locutus functions:

• _stats (r/_helpers)

Here's what our current TypeScript equivalent to R's cor looks like.

TSTypeScript JSJavaScript TSStandalone TS JSStandalone JS

Copy code

import { sampleCovariance, sampleVariance, toRNumericArray } from '../_helpers/_stats.ts'

export function cor(x: unknown, y: unknown): number {
  //      discuss at: https://locutus.io/r/cor/
  // parity verified: R 4.4
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Implements R's default Pearson correlation for two plain numeric vectors.
  //       example 1: cor([1, 2, 3], [1, 5, 7])
  //       returns 1: 0.9819805060619659
  //       example 2: cor([1, 2, 3], [7, 5, 3])
  //       returns 2: -1
  //       example 3: cor([1, 2, 3], [2, 4, 6])
  //       returns 3: 1

  const left = toRNumericArray(x, 'cor')
  const right = toRNumericArray(y, 'cor')
  const covariance = sampleCovariance(left, right, 'cor')
  const denominator = Math.sqrt(sampleVariance(left) * sampleVariance(right))

  return denominator === 0 ? Number.NaN : covariance / denominator
}

import { sampleCovariance, sampleVariance, toRNumericArray } from '../_helpers/_stats.ts'

export function cor(x, y) {
  //      discuss at: https://locutus.io/r/cor/
  // parity verified: R 4.4
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Implements R's default Pearson correlation for two plain numeric vectors.
  //       example 1: cor([1, 2, 3], [1, 5, 7])
  //       returns 1: 0.9819805060619659
  //       example 2: cor([1, 2, 3], [7, 5, 3])
  //       returns 2: -1
  //       example 3: cor([1, 2, 3], [2, 4, 6])
  //       returns 3: 1

  const left = toRNumericArray(x, 'cor')
  const right = toRNumericArray(y, 'cor')
  const covariance = sampleCovariance(left, right, 'cor')
  const denominator = Math.sqrt(sampleVariance(left) * sampleVariance(right))

  return denominator === 0 ? Number.NaN : covariance / denominator
}

// r/_helpers/_stats (Locutus helper dependency)
function toRNumericArray(data: unknown, functionName: string): number[] {
  if (!Array.isArray(data)) {
    throw new TypeError(`${functionName}() data must be an array`)
  }

  return data.map((value) => toRNumber(value, functionName))
}

function toRNumber(value: unknown, functionName: string): number {
  if (typeof value === 'number') {
    return value
  }

  if (typeof value === 'boolean') {
    return value ? 1 : 0
  }

  if (typeof value === 'bigint') {
    const numericValue = Number(value)
    if (!Number.isSafeInteger(numericValue)) {
      throw new RangeError(`${functionName}() bigint values must fit within JS safe integer precision`)
    }

    return numericValue
  }

  throw new TypeError(`${functionName}() data must contain only numeric values`)
}

function sampleVariance(values: number[]): number {
  if (values.length < 2) {
    return Number.NaN
  }

  const mean = values.reduce((sum, value) => sum + value, 0) / values.length
  const squaredDeltaSum = values.reduce((sum, value) => {
    const delta = value - mean
    return sum + delta * delta
  }, 0)

  return squaredDeltaSum / (values.length - 1)
}

function sampleCovariance(left: number[], right: number[], functionName: string): number {
  if (left.length !== right.length) {
    throw new Error(`${functionName}() requires arrays with the same length`)
  }

  if (left.length < 2) {
    return Number.NaN
  }

  const leftMean = left.reduce((sum, value) => sum + value, 0) / left.length
  const rightMean = right.reduce((sum, value) => sum + value, 0) / right.length
  let total = 0

  for (let index = 0; index < left.length; index += 1) {
    total += ((left[index] ?? 0) - leftMean) * ((right[index] ?? 0) - rightMean)
  }

  return total / (left.length - 1)
}

// r/stats/cor (target function module)
function cor(x: unknown, y: unknown): number {
  //      discuss at: https://locutus.io/r/cor/
  // parity verified: R 4.4
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Implements R's default Pearson correlation for two plain numeric vectors.
  //       example 1: cor([1, 2, 3], [1, 5, 7])
  //       returns 1: 0.9819805060619659
  //       example 2: cor([1, 2, 3], [7, 5, 3])
  //       returns 2: -1
  //       example 3: cor([1, 2, 3], [2, 4, 6])
  //       returns 3: 1

  const left = toRNumericArray(x, 'cor')
  const right = toRNumericArray(y, 'cor')
  const covariance = sampleCovariance(left, right, 'cor')
  const denominator = Math.sqrt(sampleVariance(left) * sampleVariance(right))

  return denominator === 0 ? Number.NaN : covariance / denominator
}

// r/_helpers/_stats (Locutus helper dependency)
function toRNumericArray(data, functionName) {
  if (!Array.isArray(data)) {
    throw new TypeError(`${functionName}() data must be an array`)
  }

  return data.map((value) => toRNumber(value, functionName))
}

function toRNumber(value, functionName) {
  if (typeof value === 'number') {
    return value
  }

  if (typeof value === 'boolean') {
    return value ? 1 : 0
  }

  if (typeof value === 'bigint') {
    const numericValue = Number(value)
    if (!Number.isSafeInteger(numericValue)) {
      throw new RangeError(`${functionName}() bigint values must fit within JS safe integer precision`)
    }

    return numericValue
  }

  throw new TypeError(`${functionName}() data must contain only numeric values`)
}

function sampleVariance(values) {
  if (values.length < 2) {
    return Number.NaN
  }

  const mean = values.reduce((sum, value) => sum + value, 0) / values.length
  const squaredDeltaSum = values.reduce((sum, value) => {
    const delta = value - mean
    return sum + delta * delta
  }, 0)

  return squaredDeltaSum / (values.length - 1)
}

function sampleCovariance(left, right, functionName) {
  if (left.length !== right.length) {
    throw new Error(`${functionName}() requires arrays with the same length`)
  }

  if (left.length < 2) {
    return Number.NaN
  }

  const leftMean = left.reduce((sum, value) => sum + value, 0) / left.length
  const rightMean = right.reduce((sum, value) => sum + value, 0) / right.length
  let total = 0

  for (let index = 0; index < left.length; index += 1) {
    total += ((left[index] ?? 0) - leftMean) * ((right[index] ?? 0) - rightMean)
  }

  return total / (left.length - 1)
}

// r/stats/cor (target function module)
function cor(x, y) {
  //      discuss at: https://locutus.io/r/cor/
  // parity verified: R 4.4
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Implements R's default Pearson correlation for two plain numeric vectors.
  //       example 1: cor([1, 2, 3], [1, 5, 7])
  //       returns 1: 0.9819805060619659
  //       example 2: cor([1, 2, 3], [7, 5, 3])
  //       returns 2: -1
  //       example 3: cor([1, 2, 3], [2, 4, 6])
  //       returns 3: 1

  const left = toRNumericArray(x, 'cor')
  const right = toRNumericArray(y, 'cor')
  const covariance = sampleCovariance(left, right, 'cor')
  const denominator = Math.sqrt(sampleVariance(left) * sampleVariance(right))

  return denominator === 0 ? Number.NaN : covariance / denominator
}

Improve this function

Locutus is a community effort following The McDonald's Theory: we ship first iterations, hoping others will improve them. If you see something that could be better, we'd love your contribution.

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