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https://github.com/nathanepstein/datakit

A lightweight framework for data analysis in JavaScript.
https://github.com/nathanepstein/datakit

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A lightweight framework for data analysis in JavaScript.

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README 

          
# datakit








## About

A lightweight library/framework for data analysis in JavaScript.



## Usage



```npm install datakitjs --save```



## Documentation & Examples



### Reading, Filtering, & Plotting Data

```javascript

var dk = require('datakitjs');



//READ A CSV FILE



//file.csv

// COL1, COL2

// val11, val12

// val21, val22



dk.csv('file.csv', function(data) {

  console.log(data);

});



//Output:

//[{ COL1: val11, COL2: val12 }, { COL1: val21, COL2: val22 }]



//GET A COLUMN FROM AN ARRAY OF ROW OBJECTS

dk.csv('file.csv', function(data) {

  var c2 = dk.col(data, 'COL2');

  console.log(c2);

});



//Output:

//[val12, val22]



// By default, dk.csv will convert all values to strings. You can convert select

// columns to numbers by passing an array of column names to 'dk.numeric'.



//file2.csv

// COL1, COL2

// val11, 1

// val21, 2



dk.csv('file2.csv', function(data) {

  var d = dk.numeric(data, ['COL2'], 0) // The third parameter value will be filled

  // in to blank cells. Its default value is 0.

  var c2 = dk.col(d, 'COL2');

  console.log(c2);

});



//Output:

//[1, 2]



//PLOT ARRAY(S) OF DATA



var chart = new dk.Chart({

  //optional config

  height: 500,

  width: 500,

  xLab: 'x-Axis Label',

  yLab: 'y-Axis Label'

});



chart.addDataSet({

  x: [1, 2, 3],

  y: [4, 5, 6],

  z: [2, 3, 5],

  colors: ['blue', 'green', 'red']

}).addDataSet({

  x: [1, 10],

  y: [2, -1],

  type: 'line'

}).addDataSet({

  x: [10, 5, 1],

  y: [4, 5, 2],

  labels: ["first", "second", "third"]

}).plot();

```



### Statistical Methods



```javascript

var dk = require('datakitjs');



//MEAN OF AN ARRAY

dk.mean([1, 2, 3]); //returns 2



//STANDARD DEVIATION AND VARIANCE OF AN ARRAY

dk.sd([1, 2, 3]); //returns 1

dk.vari([1, 2, 3]); //returns 1



//COVARIANCE OF TWO ARRAYS

dk.cov([1, 2, 3], [3, 2, 1]); //returns -1



//SIMPLE LINEAR REGRESSION



var x = [1, 2, 3];

var y = [2, 1, 3];



var model = dk.reg(x, y);



// model.f is a function that returns the estimated y for an input x (estimated via standard OLS regression)

// model.f = function(x) {

//  return (a + b * x);

// };



// model.pts is an array of the estimated y for each element of x

// model.pts = [1.5, 2, 2.5];



// model.endPoints is an object with the coordinates of the boundary points

// model.endPoints = { x1: 1, x2: 3, y1: 1.5, y2: 2.5 };



```



### Convenience Methods

```javascript

var dk = require('datakitjs');



//GENERATE AN ARRAY WITH A SEQUENCE OF NUMBERS



dk.seq(1, 5); //returns [1, 2, 3, 4, 5]



dk.seq(0, 1, 0.25); //returns [0, 0.25, 0.5, 0.75, 1]



//GENERATE AN ARRAY WITH REPEATED VALUE



dk.rep(1, 5); //returns [1, 1, 1, 1, 1]



//CHECK IF NUMBERS ARE CLOSE

dk.isclose(0, Math.pow(10, -15)); //returns true



dk.isclose(0, Math.pow(10, -5)); //returns false



//SUM AN ARRAY OF NUMBERS

//uses Kahan summation



dk.sum([1, 2, 3]); //returns 6



//PRODUCT OF AN ARRAY OF NUMBERS

//implementation from 'Accurate Floating Point Product' - Stef Graillat



dk.prod([1, 2, 3]); //returns 6



//MAX AND MIN OF AN ARRAY

var x = [1, 2, 3];

dk.min(x); //returns 1

dk.max(x); //returns 3



```



### Random Numbers

```javascript

var dk = require('datakitjs');



//GET AN ARRAY OF EXPONENTIALLY DISTRIBUTED VALUES



dk.exp(3, 1); //returns [0.3584189321510761, 1.0466439500242446, 0.08887770301056963]



//GET AN ARRAY OF NORMALLY DISTRIBUTED VALUES



dk.norm(3, 0, 1); //returns [-1.709768103193772, 0.23530041388459744, 0.4431320382580479]



//GET AN ARRAY OF UNIFORMLY DISTRIBUTED VALUES



dk.uni(3); //returns [0.30658303829841316, 0.1601463456172496, 0.8538850131444633]



```



## Testing



Just run `npm test` to run the tests.



## Contributing



Additional methods for random number generation, data filtration, convenience functions, and common statistical analyses are welcome additions. Just add tests following the structure in `spec/test/testSpec.js`.



## License



**The MIT License (MIT)**



> Copyright (c) 2015 Nathan Epstein

>

> Permission is hereby granted, free of charge, to any person obtaining a copy

> of this software and associated documentation files (the "Software"), to deal

> in the Software without restriction, including without limitation the rights

> to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

> copies of the Software, and to permit persons to whom the Software is

> furnished to do so, subject to the following conditions:

>

> The above copyright notice and this permission notice shall be included in

> all copies or substantial portions of the Software.

>

> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

> IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

> AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

> LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

> OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN

> THE SOFTWARE.