141 lines
4.0 KiB
Markdown
141 lines
4.0 KiB
Markdown
stats-lite
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=====
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[](https://nodei.co/npm/stats-lite/)
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A fairly light statistical package. Works with numeric arrays, and will automatically filter out non-numeric values and attempt to convert string numeric values.
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## Install
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```
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npm install stats-lite --save
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```
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Example
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---
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[Live Demo using Browserify!](http://requirebin.com/embed?gist=brycebaril/9591291)
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```javascript
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var stats = require("stats-lite")
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var dice = require("dice")
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var rolls = []
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for (var i = 0; i < 3000; i++) {
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rolls.push(dice.sum(dice.roll("2d6")))
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}
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console.log("sum: %s", stats.sum(rolls))
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console.log("mean: %s", stats.mean(rolls))
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console.log("median: %s", stats.median(rolls))
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console.log("mode: %s", stats.mode(rolls))
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console.log("variance: %s", stats.variance(rolls))
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console.log("standard deviation: %s", stats.stdev(rolls))
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console.log("sample standard deviation: %s", stats.sampleStdev(rolls))
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console.log("85th percentile: %s", stats.percentile(rolls, 0.85))
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console.log("histogram:", stats.histogram(rolls, 10))
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/* Your exact numbers may vary, but they should be pretty similar:
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sum: 21041
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mean: 7.0136666666666665
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median: 7
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mode: 7
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variance: 5.8568132222220415
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standard deviation: 2.4200853749861886
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sample standard deviation: 2.4204888234135953
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85th percentile: 10
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histogram { values: [ 94, 163, 212, 357, 925, 406, 330, 264, 164, 85 ],
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bins: 10,
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binWidth: 1.05,
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binLimits: [ 1.75, 12.25 ] }
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*/
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```
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**Compatibility Notice**: Version 2.0.0+ of this library use features that require Node.js v4.0.0 and above
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API
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===
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All of the exported functions take `vals` which is an array of numeric values. Non-numeric values will be removed, and string numbers will be converted to Numbers.
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**NOTE**: This will impact some operations, e.g. `mean([null, 1, 2, 3])` will be calculated as `mean([1, 2, 3])`, (e.g. `6 / 3 = 2`, NOT `6 / 4 = 1.5`)
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`numbers(vals)`
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---
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Accepts an array of values and returns an array consisting of only numeric values from the source array. Converts what it can and filters out anything else. e.g.
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```js
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numbers(["cat", 1, "22.9", 9])
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// [1, 22.9, 9]
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```
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`sum(vals)`
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---
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[Sum](http://en.wikipedia.org/wiki/Summation) the values in the array.
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`mean(vals)`
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---
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Calculate the [mean](http://en.wikipedia.org/wiki/Mean) average value of `vals`.
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`median(vals)`
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---
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Calculate the [median](http://en.wikipedia.org/wiki/Median) average value of `vals`.
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`mode(vals)`
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---
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Calculate the [mode](http://en.wikipedia.org/wiki/Mode_statistics) average value of `vals`.
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If `vals` is multi-modal (contains multiple modes), `mode(vals)` will return a ES6 Set of the modes.
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`variance(vals)`
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---
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Calculate the [variance](http://en.wikipedia.org/wiki/Variance) from the mean for a population.
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`stdev(vals)`
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---
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Calculate the [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation) of the values from the mean for a population.
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`sampleVariance(vals)`
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---
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Calculate the [variance](http://en.wikipedia.org/wiki/Variance) from the mean for a sample.
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`sampleStdev(vals)`
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---
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Calculate the [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation) of the values from the mean for a sample.
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`percentile(vals, ptile)`
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---
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Calculate the value representing the desired [percentile](http://en.wikipedia.org/wiki/Percentile) `(0 < ptile <= 1)`. Uses the Estimation method to interpolate non-member percentiles.
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`histogram(vals[, bins])`
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---
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Build a histogram representing the distribution of the data in the provided number of `bins`. If `bins` is not set, it will choose one based on `Math.sqrt(vals.length)`. Data will look like:
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```
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histogram {
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values: [ 86, 159, 253, 335, 907, 405, 339, 270, 146, 100 ],
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bins: 10,
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binWidth: 1.05,
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binLimits: [ 1.75, 12.25 ]
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}
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```
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Where `values` are the bins and the counts of the original values falling in that range. The ranges can be calculated from the `binWidth` and `binLimits`. For example, the first bin `values[0]` in this example is from `1.75 < value <= 2.8`. The third bin `values[2]` would be `1.75 + (1.05 * 2) < value <= 1.75 + (1.05 * 3)` or `3.85 < value <= 4.9`.
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LICENSE
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=======
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MIT
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