# aggregate-rows

## Description

<mark style="color:purple;">`aggregate-rows`</mark> *adaptor groups rows with the same identified variable to produce a new column with a chosen mathematic function.*

## Inputs

**`data`**\
Type: `datatable`\
Required: Yes\
The datatable to be aggregated.

**`group column names`**\
Type: `list`\
Required: Yes\
The names of the columns containing values to be grouped.

**`aggregations`**\
Type: `dictionary`\
Required: Yes\
A dictionary of column names with aggregation method, where the keys are the column names, and the values should be either `max`, `mean`, `median`, `min`, `mode`, `sum`, `unique-values` (list of distinct values), `unique-number` (number of distinct values).

## Outputs

**`data`**\
Type: `datatable`\
A datatable with aggregated data.

## Examples

### Example 1: Default behaviour.

#### Inputs:

`data`:

<table><thead><tr><th width="143">name</th><th width="116">eye colour</th><th width="213">systolic blood pressure</th></tr></thead><tbody><tr><td>Patient A</td><td>blue</td><td>120</td></tr><tr><td>Patient B</td><td>brown</td><td>125</td></tr><tr><td>Patient C</td><td>brown</td><td>110</td></tr><tr><td>Patient D</td><td>blue</td><td>90</td></tr></tbody></table>

`group column names`: eye colour

`aggregations`:

<table><thead><tr><th width="300">KEY</th><th width="100">VALUE</th></tr></thead><tbody><tr><td>systolic blood pressure</td><td>mean</td></tr></tbody></table>

#### Outputs:

`data`:

<table><thead><tr><th width="133">eye colour</th><th width="212">systolic blood pressure</th></tr></thead><tbody><tr><td>blue</td><td>105</td></tr><tr><td>brown</td><td>117.5</td></tr></tbody></table>

-> In this example, we use the <mark style="color:purple;">`aggregate-rows`</mark> adaptor to calculate the average (mean) value of systolic blood pressure between blue eyed patients and brown eyed patients.

## Use Cases

* Determine the mean values of defined attributes of a population (mean height in men versus women in Country Y).
* Determine the max number of reported cases of pathogen Y in each region in the north of country W.
* Pull out the earliest (min) or latest (max) dates different pathogens were identified.
* Show the frequency distribution of values in a dataset to help visualise data in a histogram (count).


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