# Aggregate Transform

The **aggregate** transform groups and summarizes an input data stream to produce a derived output stream. Aggregate transforms can be used to compute counts, sums, averages and other descriptive statistics over groups of data objects.

## Transform Parameters

Property | Type | Description |
---|---|---|

groupby | Field[ ] | The data fields to group by. If not specified, a single group containing all data objects will be used. |

fields | Field[ ] | The data fields for which to compute aggregate functions. This array should align with the ops and as arrays. If no fields and ops are specified, a `count` aggregation will be used by default. |

ops | String[ ] | The aggregation operations to apply to the fields, such as `sum` , `average` or `count` . See the aggregate operation reference for more. |

as | String[ ] | The output field names to use for each aggregated field in fields. If not specified, names will be automatically generated based on the operation and field names (e.g., `sum_field` , `average_field` ). |

cross | Boolean | Indicates if the full cross-product of all groupby values should be included in the aggregate output (default `false` ). If set to `true` , all possible combinations of groupby field values will be considered and zero count groups will be generated and returned for combinations that do not occur in the data itself. Cross-product output act as if the drop parameter is `false` . In the case of streaming updates, the number of output groups will increase if new groupby field values are observed; all prior groups will be retained. This parameter can be useful for generating facets that include groups for all possible partitions of the data. |

drop | Boolean | Indicates if empty (zero count) groups should be dropped (default `true` ). When a data stream updates (for example, in response to interactive filtering), aggregation groups may become empty. By default, the group is removed from the output. However, in some cases (such as histograms), one may wish to retain empty groups. |

key | Field | An optional key field used to optimize groupby key calculation. If specified, unique keys for each aggregation cell will not be generated from the groupby fields themselves, but instead use this single key field only. Using a key is helpful to speed up processing in situations where there are multiple groupby fields, but a single field is sufficient to distinguish each aggregation cell. For example, for a histogram it is faster to key solely on a bin0 property, and this is safe when the bin1 property (also included as a groupby field) contains redundant information with respect to grouping. This parameter should be used carefully, and only when one is certain that the key field uniquely distinguishes all combinations of groupby field values. |

## Aggregate Operation Reference

All valid aggregate operations.

Operation | Description |
---|---|

count | The total count of data objects in the group. |

valid | The count of field values that are not null, undefined or NaN. |

missing | The count of null or undefined field values. |

distinct | The count of distinct field values. |

sum | The sum of field values. |

mean | The mean (average) field value. |

average | The mean (average) field value. Identical to mean. |

variance | The sample variance of field values. |

variancep | The population variance of field values. |

stdev | The sample standard deviation of field values. |

stdevp | The population standard deviation of field values. |

stderr | The standard error of field values. |

median | The median field value. |

q1 | The lower quartile boundary of field values. |

q3 | The upper quartile boundary of field values. |

ci0 | The lower boundary of the bootstrapped 95% confidence interval of the mean field value. |

ci1 | The upper boundary of the bootstrapped 95% confidence interval of the mean field value. |

min | The minimum field value. |

max | The maximum field value. |

argmin | An input data object containing the minimum field value. |

argmax | An input data object containing the maximum field value. |

## Usage

For the following input data:

```
[
{"foo": 1, "bar": 1},
{"foo": 1, "bar": 2},
{"foo": null, "bar": 3}
]
```

The aggregate transform

```
{
"type": "aggregate",
"fields": ["foo", "bar", "bar"],
"ops": ["valid", "sum", "median"],
"as": ["v", "s", "m"]
}
```

produces the output:

```
[{"v": 2, "s": 6, "m": 2}]
```

## Usage with groupby

For the following input data:

```
[
{"foo": "a", "bar": 1},
{"foo": "a", "bar": 2},
{"foo": "b", "bar": 3}
]
```

The aggregate transform

```
{
"type": "aggregate",
"groupby": ["foo"],
}
```

produces the output:

```
[
{"foo": "a", "count": 2},
{"foo": "b", "count": 1}
]
```

## Usage with nested fields

For the following input data:

```
[
{"foo": {"baz": "a"}, "bar": 1},
{"foo": {"baz": "a"}, "bar": 2},
{"foo": {"baz": "b"}, "bar": 3}
]
```

The aggregate transform

```
{
"type": "aggregate",
"groupby": ["foo.baz"],
}
```

produces the output:

```
[
{"foo.baz": "a", "count": 2},
{"foo.baz": "b", "count": 1}
]
```

The field name `"foo.baz"`

is now a flat string, *not* a nested field reference. To reference this field name elsewhere in a specification, you must escape the dot character: `"foo\\.baz"`

. Otherwise, Vega will try to parse it as a nested field name. To avoid this nuisance, you can use the `project`

transform to unnest the data prior to aggregation.