als-statistics/lib/table/index.js

A powerful and lightweight JavaScript library for descriptive statistics, regression, clustering, outlier detection, and noise analysis using a flexible table/column architecture.

const ColumnFilter = require('./filter')
const compute = require('./compute')
const extractMetrics = require('./extract-metric')
const transpose = require('./transpose')
const newColumn = require('../utils/new-column')
const filterColumns = require('./filter-columns')
const addRow = require('./add-row')
const { Comparative, CronbachAlpha, Dbscan, Hdbscan, LinearRegression } = require('./instruments/index')
class Table extends ColumnFilter {
   constructor(data) {
      super()
      this.n = 0;
      this.columns = {}
      this.columnsN = 0
      if(data && typeof data === 'object' && !Array.isArray(data) && data !== null) {
         for (let colName in data) this.addColumn(colName, data[colName])
      }
   }

   get Table() { return Table }
   get cronbachAlpha() { return new CronbachAlpha(this) }
   addRow(obj, index) { return addRow(obj, index, this) }
   deleteColumn(name) {
      if (!this.columns[name]) return;
      this.columns[name].columnFilter = new ColumnFilter();
      delete this.columns[name];
      const colValues = Object.values(this.columns)
      this.columnsN = colValues.length
      this.n = Math.max(...colValues.map(col => col.n), 0);
      return this
   }

   addColumn(name, values = []) {
      this.columns[name] = values.constructor.name.includes('Column') ? values : newColumn(values, this)
      if (this.columns[name].n > this.n) this.n = this.columns[name].n
      this.columnsN++
      return this.columns[name]
   }

   filterRowsBy(colName, fn) { 
      if (this.columns[colName]) this.columns[colName].filterRowsBy(fn);
      return this
   }

   clone(filtered = true, columnFilter = null) {
      const table = new Table();
      const filteredNames = filterColumns(this.columns, columnFilter);
      for (let columnName of filteredNames) {
         if (!this.columns[columnName]) continue
         table.addColumn(columnName, this.columns[columnName].clone(filtered, table));
      }
      return table;
   }
   compare(colName1, colName2) { return new Comparative(this.columns[colName1], this.columns[colName2]) }
   transpose() { return transpose(new Table(), this) }
   descriptive(metricName, table) { return newColumn(extractMetrics(this.columns, metricName), table) }
   compute(fn, targetName) {
      return targetName ? this.addColumn(targetName, compute(fn, this)) : newColumn(compute(fn, this))
   }
   where(fn) { return compute((obj, i) => {if(!this.filtered[i] && fn(obj, i)) return i }, this).filter((v) => v !== undefined) }
   dbscan(eps = 0.4, minPts = 3) { return new Dbscan(this, eps, minPts) }
   hdbscan(minPts = 3) { return new Hdbscan(this, minPts) }
   linearRegression(yName, xNames) { return new LinearRegression(this,yName,xNames) }
   get json() {
      const obj = {}
      for(const colName in this.columns) {
         obj[colName] = this.columns[colName].values
      }
      return obj
   }
}

module.exports = Table