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Add reference Markdown: operators/accumulators #37

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390 changes: 390 additions & 0 deletions reference/operators/accumulators/$avg.md
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---
title: $avg
description: Computes the average of numeric values for documents in a group, bucket, or window.
type: operators
category: accumulators
---

# $avg

The `$avg` operator computes the average of numeric values across groups of documents or within defined windows.

## Syntax

```javascript
$avg: <field or expression>
```

## Parameters

| Parameter | Description |
| --- | --- |
| **`<field or expression>`** | Specifies the field or expression to calculate the average. Non-numeric values are ignored. |

## Examples

Consider this sample document from the stores collection.

```json
{
"_id": "0fcc0bf0-ed18-4ab8-b558-9848e18058f4",
"name": "First Up Consultants | Beverage Shop - Satterfieldmouth",
"location": {
"lat": -89.2384,
"lon": -46.4012
},
"staff": {
"totalStaff": {
"fullTime": 8,
"partTime": 20
}
},
"sales": {
"totalSales": 75670,
"salesByCategory": [
{
"categoryName": "Wine Accessories",
"totalSales": 34440
},
{
"categoryName": "Bitters",
"totalSales": 39496
},
{
"categoryName": "Rum",
"totalSales": 1734
}
]
},
"promotionEvents": [
{
"eventName": "Unbeatable Bargain Bash",
"promotionalDates": {
"startDate": {
"Year": 2024,
"Month": 6,
"Day": 23
},
"endDate": {
"Year": 2024,
"Month": 7,
"Day": 2
}
},
"discounts": [
{
"categoryName": "Whiskey",
"discountPercentage": 7
},
{
"categoryName": "Bitters",
"discountPercentage": 15
},
{
"categoryName": "Brandy",
"discountPercentage": 8
},
{
"categoryName": "Sports Drinks",
"discountPercentage": 22
},
{
"categoryName": "Vodka",
"discountPercentage": 19
}
]
},
{
"eventName": "Steal of a Deal Days",
"promotionalDates": {
"startDate": {
"Year": 2024,
"Month": 9,
"Day": 21
},
"endDate": {
"Year": 2024,
"Month": 9,
"Day": 29
}
},
"discounts": [
{
"categoryName": "Organic Wine",
"discountPercentage": 19
},
{
"categoryName": "White Wine",
"discountPercentage": 20
},
{
"categoryName": "Sparkling Wine",
"discountPercentage": 19
},
{
"categoryName": "Whiskey",
"discountPercentage": 17
},
{
"categoryName": "Vodka",
"discountPercentage": 23
}
]
}
]
}
```

### Example 1: Calculate the average sales by category

To calculate the average sales across all stores within each category, first run a query to group documents within each sales category. Then, calculate the average sales across all documents within each group.

```javascript
db.stores.aggregate([{
$unwind: "$sales.salesByCategory"
},
{
$group: {
_id: "$sales.salesByCategory.categoryName",
avgSales: {
$avg: "$sales.salesByCategory.totalSales"
}
}
}
])
```

This query returns the following results:

```json
[
{
"_id": "Christmas Trees",
"avgSales": 25987.956989247312
},
{
"_id": "Nuts",
"avgSales": 25115.98795180723
},
{
"_id": "Camping Tables",
"avgSales": 25012.546153846153
},
{
"_id": "Music Theory Books",
"avgSales": 26138.80769230769
},
{
"_id": "Fortified Wine",
"avgSales": 24748.672727272726
},
{
"_id": "Children's Mystery",
"avgSales": 23764.044444444444
},
{
"_id": "Short Throw Projectors",
"avgSales": 27157.472222222223
},
{
"_id": "Pliers",
"avgSales": 26712.875
},
{
"_id": "Bluetooth Headphones",
"avgSales": 26311.58653846154
},
{
"_id": "Video Storage",
"avgSales": 26121.475
},
{
"_id": "Cleansers",
"avgSales": 25836.397058823528
},
{
"_id": "Camera Straps",
"avgSales": 22487.609375
},
{
"_id": "Carry-On Bags",
"avgSales": 24294.263157894737
},
{
"_id": "Disinfectant Wipes",
"avgSales": 27066.929411764704
},
{
"_id": "Insignia Smart TVs",
"avgSales": 27096.83950617284
},
{
"_id": "Toner Refill Kits",
"avgSales": 24963.71052631579
},
{
"_id": "iPads",
"avgSales": 22583.882352941175
},
{
"_id": "Memory Foam Mattresses",
"avgSales": 28073.05172413793
},
{
"_id": "Storage Baskets",
"avgSales": 24092.514705882353
},
{
"_id": "Body Spray",
"avgSales": 26080.84375
}
]
```

### Example 2: Using `$avg` in `$bucket`

To get the averages sales within specific sales boundaries, this query creates buckets based on sales values and calculates the avg sales within each bucket.

```javascript
db.stores.aggregate([{
$bucket: {
groupBy: "$sales.totalSales",
boundaries: [0, 1000, 5000, 10000],
default: "Other",
output: {
avgSales: {
$avg: "$sales.totalSales"
}
}
}
}])
```

This query returns the following results:

```json
[
{
"_id": 1000,
"avgSales": 3029.053674121406
},
{
"_id": "Other",
"avgSales": 52169.85442987472
},
{
"_id": 0,
"avgSales": 576.3164179104477
},
{
"_id": 5000,
"avgSales": 7538.786819770345
}
]
```

### Example 3: Using `$avg` in `$setWindowFields`

To get the average discount per store in 2023 for "Laptops", first run a query to partition stores by company and filter discount promotions for "Laptops". Then calculate the average discount percentage within each partitioned result set.

```javascript
db.stores.aggregate([{
$unwind: "$promotionEvents"
},
{
$unwind: "$promotionEvents.discounts"
},

// Filter only Laptops category and events in 2023
{
$match: {
"promotionEvents.promotionalDates.startDate.Year": 2023,
"promotionEvents.discounts.categoryName": "Laptops"
}
},

// Use $setWindowFields to calculate average discount by city
{
$setWindowFields: {
partitionBy: "$company",
output: {
avgDiscount: {
$avg: "$promotionEvents.discounts.discountPercentage",
window: {
documents: ["unbounded", "unbounded"]
}
}
}
}
},

// Group by city to return one result per city
{
$group: {
_id: "$company",
avgDiscount: {
$first: "$avgDiscount"
}
}
}
])
```

This query returns the following results:

```json
[
{
"_id": "VanArsdel, Ltd.",
"avgDiscount": 14.461538461538462
},
{
"_id": "Proseware, Inc.",
"avgDiscount": 16.25
},
{
"_id": "Fabrikam, Inc.",
"avgDiscount": 14.454545454545455
},
{
"_id": "Contoso, Ltd.",
"avgDiscount": 14.384615384615385
},
{
"_id": "Fourth Coffee",
"avgDiscount": 13.625
},
{
"_id": "Trey Research",
"avgDiscount": 17.785714285714285
},
{
"_id": "Adatum Corporation",
"avgDiscount": 11.666666666666666
},
{
"_id": "Relecloud",
"avgDiscount": 14.375
},
{
"_id": "Lakeshore Retail",
"avgDiscount": 15.846153846153847
},
{
"_id": "Northwind Traders",
"avgDiscount": 14.2
},
{
"_id": "First Up Consultants",
"avgDiscount": 11.25
},
{
"_id": "Wide World Importers",
"avgDiscount": 15.571428571428571
},
{
"_id": "Tailwind Traders",
"avgDiscount": 16.166666666666668
}
]
```
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