Use **Group By** to combine and aggregate data based on a selected field. The results of the operation are added to the project as a newly derived dataset.

Potential use cases for group by operations include:

- Computing the average air quality statistics of a static location over a period of time.
- Retrieving the total count of a specific feature on the map.
- Calculating the distribution of a COVID-19 outbreak for any given region.

# Run a Group By Operation

Follow these steps to run a group by operation in Studio:

1. Navigate to the **Columns** tab.
2. Click **⋮ More Options** > **Group By** on the column to base the group by operation on.

The selected column for the group by operation will appear at the top of the Group By panel. Click the selected column to choose a new column for the group by operation.

In this example, a dataset containing trees in San Francisco is grouped by `h3_9`, a column containing [H3](https://h3geo.org/) index strings.

3. Define aggregation rules for the group by operation.

A panel appears with fields to define the aggregation rules for combining data in each column.

First, select/unselect fields to include/exclude in the grouped dataset. If your dataset contains many columns you wish to remove, click **Unselect all** and then select only the columns you wish to include.

Next, change the aggregation methods for any column by clicking the purple aggregation text. Based on the data type of each column, Studio will provide a list of all applicable aggregation methods.

4. Click **Run** to run the group by operation.

A window will appear containing a preview of the grouped dataset. If you notice any issues with the grouped dataset, alter the aggregation rules then click **Rerun**.

5. Click **Confirm** to add the grouped dataset to the project.

The grouped dataset will appear connected to the source dataset. Studio may also automatically generate a new layer for the grouped dataset. You may start configuring this dataset to visualize the newly aggregated data.

In the example below, the newly aggregated `count TreeID` column visualizes the distribution of street trees throughout San Francisco in H3 cells at resolution 9. Green areas represent areas with more street trees, while brown areas represent areas with fewer street trees.

# Available Group By Operations

Refer to the table below to see the supported aggregation options based on the data type of the columns in your dataset.

| Aggregation Method | Numerical | Time | Categorical (String) | Boolean | Geometry | H3 |
| --- | --- | --- | --- | --- | --- | --- |
| COUNT | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |  |
| SUM | ✔️ |  |  |  |  |  |
| MEAN | ✔️ |  |  |  |  |  |
| MAX | ✔️ | ✔️ |  |  |  |  |
| MIN | ✔️ | ✔️ |  |  |  |  |
| DEVIATION | ✔️ |  |  |  |  |  |
| VARIANCE | ✔️ |  |  |  |  |  |
| PERCENTILE (05, 25, 50, 75, 95) | ✔️ |  |  |  |  |  |
| MODE |  | ✔️ | ✔️ | ✔️ |  | ✔️ |
| UNIQUE |  |  | ✔️ |  | ✔️ | ✔️ |
| MERGE |  |  |  |  | ✔️ | ✔️ |

# Spatial Merge

Use spatial merge with `geojson` or `h3` data to dissolve borders or otherwise combine geometries based on a common field. When merging `geojson` data, all polygons, LineStrings, Points, and other features will be merged. When merging `h3` data, all `h3` indexes that share the field will be merged.

Follow these steps to complete a spatial merge:

1. Start a new group by operation, then select a field shared by the geometries you wish to merge.
2. Choose any other columns you wish to include in the group-by operation.
3. Select the `Merge` aggregation option for your `polygon` or `h3` column.

Upon completing the group by operation, a new dataset will appear with your merged spatial objects.

### Example

In the above example, a dataset containing election data is loaded into Studio. This election data has the column `_geojson`, which contains GeoJSON `MultiPolygon` objects representing congressional districts, as well as a column `PARTY_LEADING`, indicating which party controls the congressional district.

To view the number of congressional districts controlled by an individual party, we run a group by operation on the `STATENAME` field. Then, we choose the `Merge` aggregation option on our `_geojson` column and `Mode` on our `PARTY_LEADING` column.

Upon completing the merge, a new layer is automatically generated with the new `Merge _geojson` column. We can quickly configure this layer to display the data calculated by the group by operation.