Formula to detect the grade level of text according to the gunning fog index.
- What is this?
- When should I use this?
- Install
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- API
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This package exposes an algorithm to detect ease of reading of English texts.
You’re probably dealing with natural language, and know you need this, if you’re here!
This algorithm is based on syllables, whereas some others are not, which means it’s tougher to get right and slower to calculate.
See syllable
for detecting syllables.
This package is ESM only. In Node.js (version 14.14+, 16.0+), install with npm:
npm install gunning-fog
In Deno with esm.sh
:
import {gunningFog} from 'https://esm.sh/gunning-fog@2'
In browsers with esm.sh
:
<script type="module">
import {gunningFog} from 'https://esm.sh/gunning-fog@2?bundle'
</script>
import {gunningFog} from 'gunning-fog'
// For “The Australian platypus is seemingly a hybrid of a mammal and reptilian
// creature.”
// 1 sentence; 13 words; 4 polysillabic words, of which two are jargon, proper
// nouns, or compound words.
gunningFog({sentence: 1, word: 13, complexPolysillabicWord: 2})
// => 11.35384…
This package exports the identifier gunningFog
.
There is no default export.
Given an object containing the number of words (word
), the number of
sentences (sentence
), and the number of complex (i.e., jargon, proper
nouns, compound words) polysillabic (three or more syllables) words
(complexPolysillabicWord
) in a document, returns the grade level
associated with the document.
Counts from input document.
Number of sentences (number
, required).
Number of words (number
, required).
Number of words that consist of three or more syllables, that are jargon,
proper nouns, or compound words (number
, required).
Grade level associated with the document (number
).
This package is fully typed with TypeScript.
It exports the additional type Counts
.
This package is at least compatible with all maintained versions of Node.js. As of now, that is Node.js 14.14+ and 16.0+. It also works in Deno and modern browsers.
retext-readability
— complete readability measuring solutionautomated-readability
— uses character count instead of error-prone syllable parsercoleman-liau
— uses letter count instead of an error-prone syllable parserdale-chall-formula
— uses a dictionary; suited for higher reading levelsflesch
— uses syllable countflesch-kincaid
— likeflesch
; returns U.S. grade levelssmog-formula
— likegunning-fog-index
; without needing advanced NLPspache-formula
— uses a dictionary; suited for lower reading levels
Yes please! See How to Contribute to Open Source.
This package is safe.