ecStat

A statistical and data mining tool for ECharts. You can use it to analyze data and then visualize the results with ECharts, or just use it to process data.

It works both in node.js and in the browser.

Read this in other languages: English, 简体中文.

Installing

If you use npm, you can install it with:

 npm install echarts-stat

Otherwise, download this tool from dist directory:

<script src='./dist/ecStat.js'></script>
<script>

var result = ecStat.clustering.hierarchicalKMeans(data, clusterNumber, false);

</script>

API Reference

Histogram
Clustering
Regression
Statistics

Histogram

A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a quantitative variable. It is a kind of bar graph. To construct a histogram, the first step is to "bin" the range of values - that is, divide the entire range of values into a series of intervals - and then count how many original sample values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. Here the bins(intervals) must be adjacent, and are of equal size.

Syntax

var bins = ecStat.histogram(data, binMethod);

Parameter

data - Array<number>. Data samples of numbers.

 var data = [8.6, 8.8, 10.5, 10.7, 10.8, 11.0, ... ];

binMethod - string. There are four methods to calculate the number of bin, which are squareRoot, scott, freedmanDiaconis, and sturges. Of course, there is no "best" number of bin, and different bin size can reveal different feature of data.
- squareRoot - This is the default method, which is also used by Excel histogram. Returns the number of bin according to Square-root choice:
```
var bins = ecStat.histogram(data);
```
- scott - Returns the number of bin according to Scott's normal reference Rule:
```
 var bins = ecStat.histogram(data, 'scott');
```
- freedmanDiaconis - Returns the number of bin according to The Freedman-Diaconis rule:
```
 var bins = ecStat.histogram(data, 'freedmanDiaconis');
```
- sturges - Returns the number of bin according to Sturges' formula:
```
 var bins = ecStat.histogram(data, 'sturges');
```

Return Value

bins - Object. Contain detailed messages of each bin and data used for ECharts to draw the histogram.
- bins.bins - Array.<Object>. An array of bins, where each bin is an object, containing three attributes:
  - x0 - number. The lower bound of the bin (inclusive).
  - x1 - number. The upper bound of the bin (exclusive).
  - sample - Array.<number>. Containing the associated elements from the input data.
- bins.data - Array.<Array.<number>>. Used for bar chart to draw the histogram, each bin data is an array not only containing the mean value of x0 and x1, but also the length of sample, which is the number of sample values in this bin.
- bins.customData - Array.<Array<number>>. Used for custom chart to draw the histogram, each custom data is an array not only containing the x0 and x1, but also the length of sample, which is the number of sample values in this bin.

Examples

This example using ECharts custom chart to draw the histogram, which is the best type of chart we recommend.

<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>

var bins = ecStat.histogram(data);
var option = {
	...
	series: [{
		type: 'custom',
		...
	}],
	...
}

</script>

Run

Clustering

Clustering can divide the original data set into multiple data clusters with different characteristics. And through ECharts, you can visualize the results of clustering, or visualize the process of clustering.

Syntax

var result = ecStat.clustering.hierarchicalKMeans(data, clusterNumber, stepByStep);

Parameter

data － Array.<Array.<number>>. Two-dimensional numeric array, each data point can have more than two numeric attributes in the original data set. In the following example, data[0] is called data point and data[0][1] is one of the numeric attributes of data[0].
```
var data = [
  	[1, 2, 3, 4, 5],
  	[6, 7, 8, 9, 10],
  	[11, 12, 13, 14, 15],
  	...
      ];
```
clusterNumer － number. The number of clusters generated. Note that it has to be greater than 1.
stepByStep － boolean. Control whether doing the clustering step by step

Return Value

result － Object. Including the centroids, clusterAssment, and pointsInCluster. For Example:

 result.centroids = [

 	[-0.460, -2.778],
 	[2.934, 3.128],
     	...
 ];

 // Indicate which cluster each data point belonging to, and the distance to cluster centroids
 result.clusterAssment = [

 	[1, 0.145],
 	[2, 0.680],
 	[0, 1.022],
 	...
 ];

 // Concrete data point in each cluster
 result.pointsInCluster = [
 	[
 		[0.335, -3.376],
 		[-0.994, -0.884],
 		...
 	],
 	...
 ];

Examples

You can not only do cluster analysis through this interface, but also use ECharts to visualize the results.

Note: the clustering algorithm can handle multiple numeric attributes, but for the convenience of visualization, two numeric attributes are chosen here as an example.

Directly visualize the final results of clustering

<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>

var clusterNumber = 3;
var result = ecStat.clustering.hierarchicalKMeans(data, clusterNumber, false);

</script>

Run

Visualize the process of clustering

<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>

var clusterNumber = 6;
var result = ecStat.clustering.hierarchicalKMeans(data, clusterNumber, true);

</script>

Run

Regression

Regression algorithm can according to the value of the dependent and independent variables of the data set, fitting out a curve to reflect their trends. The regression algorithm here only supports two numeric attributes.

Syntax

var myRegression = ecStat.regression(regressionType, data, order);

Parameters

regressionType - string. There are four types of regression, whice are 'linear', 'exponential', 'logarithmic', 'polynomial'.
data - Array.<Array.<number>>. Two-dimensional numeric array, Each data object should have two numeric attributes in the original data set. For Example:
```
 var data = [

 	[1, 2],
 	[3, 5],
 	...
 ];
```
order - number. The order of polynomial. If you choose other types of regression, you can ignore it.

Return Value

myRegression - Object. Including points, parameter, and expression. For Example:

 myRegression.points = [
 	[1, 2],
 	[3, 4],
 	...
 ];

 // This is the parameter of linear regression, for other types, it shoule be a little different
 myRegression.parameter = {
 	gradient: 1.695,
 	intercept: 3.008
 };

 myRegression.expression = 'y = 1.7x + 3.01';

Examples

You can not only do regression analysis through this interface, you can also use ECharts to visualize the results.

Linear Regression

<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>

var myRegression = ecStat.regression('linear', data);

</script>

Run

Exponential Regression

<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>

var myRegression = ecStat.regression('exponential', data);

</script>

Run

Logarithmic Regression

<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>

var myRegression = ecStat.regression('logarithmic', data);

</script>

Run

Polynomial Regression

<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>

var myRegression = ecStat.regression('polynomial', data, 3);

</script>

Run

Statistics

This interface provides basic summary statistical services.

ecStat.statistics.deviation()

Syntax

var sampleDeviation = ecStat.statistics.deviation(dataList);

Parameter

dataList : Array.<number>

Return Value

sampleDeviation: number. Return the deviation of the numeric array dataList. If the dataList is empty or the length less than 2, return 0.

ecStat.statistics.sampleVariance()

Syntax

var varianceValue = ecStat.statistics.sampleVariance(dataList);

Parameter

dataList : Array.<number>

Return Value

varianceValue: number. Return the variance of the numeric array dataList. If the dataList is empty or the length less than 2, return 0.

ecStat.statistics.quantile()

Syntax

var quantileValue = ecStat.statistics.quantile(dataList, p);

Parameter

dataList : Array.<number>. Sorted array of numbers.
p: number. where 0 =< p <= 1. For example, the first quartile at p = 0.25, the seconed quartile at p = 0.5(same as the median), and the third quartile at p = 0.75.

Return Value

quantileValue: number. Return the quantile of the sorted array of numbers. If p <= 0 or the length of dataList less than 2, return the first element of the sorted array dataList; if p >= 1, return the last element of the sorted array dataList; If dataList is empty, return 0.

ecStat.statistics.max()

Syntax

var maxValue = ecStat.statistics.max(dataList);

Parameter

dataList : Array.<number>

Return Value

maxValue: number. The maximum value of the dataList.

ecStat.statistics.min()

Syntax

var minValue = ecStat.statistics.min(dataList);

Parameter

dataList : Array.<number>

Return Value

minValue: number. The minimum value of the dataList.

ecStat.statistics.mean()

Syntax

var meanValue = ecStat.statistics.mean(dataList);

Parameter

dataList : Array.<number>

Return Value

meanValue: number. The average of the dataList.

ecStat.statistics.median()

Syntax

var medianValue = ecStat.statistics.median(dataList);

Parameter

dataList : Array.<number>. Sorted array of numbers

Return Value

medianValue: number. The median of the dataList.

ecStat.statistics.sum()

Syntax

var sumValue = ecStat.statistics.sum(dataList);

Parameter

dataList : Array.<number>

Return Value

sumValue: number. The sum of the dataList.

Files

README.md

Latest commit

History

README.md

File metadata and controls

ecStat

Installing

API Reference

Histogram

Syntax

Parameter

Return Value

Examples

Clustering

Syntax

Parameter

Return Value

Examples

Directly visualize the final results of clustering

Visualize the process of clustering

Regression

Syntax

Parameters

Return Value

Examples

Linear Regression

Exponential Regression

Logarithmic Regression

Polynomial Regression

Statistics

ecStat.statistics.deviation()

Syntax

Parameter

Return Value

ecStat.statistics.sampleVariance()

Syntax

Parameter

Return Value

ecStat.statistics.quantile()

Syntax

Parameter

Return Value

ecStat.statistics.max()

Syntax

Parameter

Return Value

ecStat.statistics.min()

Syntax

Parameter

Return Value

ecStat.statistics.mean()

Syntax

Parameter

Return Value

ecStat.statistics.median()

Syntax

Parameter

Return Value

ecStat.statistics.sum()

Syntax

Parameter

Return Value