Having Method

The having() method filters grouped data based on aggregate conditions, similar to SQL HAVING clause.

Basic Syntax

having<K extends keyof T>(key: K, op: string, value: any): CollectionOperations<T>

Examples

Basic Usage

import { collect } from 'ts-collect'

const orders = collect([
  { category: 'electronics', amount: 1000 },
  { category: 'books', amount: 50 }
])

const highValueCategories = orders
  .groupBy('category')
  .having('amount', '>', 500)

Real-world Example: E-commerce Analytics

interface OrderData {
  categoryId: string
  revenue: number
  quantity: number
  date: Date
}

class SalesAnalyzer {
  private orders: Collection<OrderData>

  constructor(orders: OrderData[]) {
    this.orders = collect(orders)
  }

  findHighPerformingCategories() {
    return this.orders
      .groupBy('categoryId')
      .having('revenue', '>=', 10000)
      .map(group => ({
        categoryId: group.first()?.categoryId,
        totalRevenue: group.sum('revenue'),
        orderCount: group.count()
      }))
  }

  getPopularCategories(minOrders: number) {
    return this.orders
      .groupBy('categoryId')
      .having('quantity', '>=', minOrders)
      .sortBy('quantity', 'desc')
  }

  analyzePerformance() {
    return {
      highRevenue: this.orders
        .groupBy('categoryId')
        .having('revenue', '>', 50000)
        .all(),

      highVolume: this.orders
        .groupBy('categoryId')
        .having('quantity', '>', 1000)
        .all(),

      lowPerforming: this.orders
        .groupBy('categoryId')
        .having('revenue', '<', 1000)
        .all()
    }
  }
}

// Usage
const analyzer = new SalesAnalyzer([
  {
    categoryId: 'C1',
    revenue: 12000,
    quantity: 100,
    date: new Date()
  }
])

const highPerforming = analyzer.findHighPerformingCategories()
const popular = analyzer.getPopularCategories(50)

Supported Operators

• >: Greater than
• <: Less than
• >=: Greater than or equal
• <=: Less than or equal
• =: Equal
• !=: Not equal

Return Value

• Returns filtered Collection
• Preserves grouping structure
• Maintains original types
• Chain-friendly operations
• Handles null values
• Type-safe comparisons

Common Use Cases

1. Sales Analysis

• Revenue thresholds
• Order volumes
• Category performance
• Customer segments
• Product analysis

2. Performance Metrics

• KPI thresholds
• Goal achievement
• Performance filters
• Metric analysis
• Target tracking

3. Inventory Management

• Stock thresholds
• Movement rates
• Category analysis
• Supply levels
• Demand patterns

4. Customer Analysis

• Spending levels
• Order frequency
• Segment analysis
• Value thresholds
• Behavior patterns

5. Business Intelligence

• Performance filters
• Threshold analysis
• Group filtering
• Metric comparison
• Data segmentation