#datakit

##About A lightweight library/framework for data analysis in JavaScript.

Check out this blog post on the need for more JavaScript data tools.

##Usage

npm install datakitjs --save

##Documentation & Examples

###Reading, Filtering, & Plotting Data

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

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

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

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.