> For the complete documentation index, see [llms.txt](https://docs.mediarithmics.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mediarithmics.io/setup-and-monitor/operate-and-monitor/create-a-custom-dashboard/dashboard-cookbook.md).
# Dashboard cookbook
Recipes for the dashboard shapes people build most often.
## Your goal
Get to a working dashboard faster by starting from a shape that already solves your problem.
## Before you start
These recipes are advanced-mode JSON. The structure they use - sections, cards, charts, datasets, transformations - is described in [Dashboard](/setup-and-monitor/operate-and-monitor/create-a-custom-dashboard/dashboard.md), and the workflow is in [Create a custom dashboard](/setup-and-monitor/operate-and-monitor/create-a-custom-dashboard.md).
Each `query_id` below is a placeholder: save your own query and use its ID. The comments show the query the recipe assumes.
## Friendly dates
`@date_histogram` queries return timestamps. The **`format-dates`** transformation makes them readable.
{% tabs %}
{% tab title="Single series" %}
```json
{
"title": "Application events (last 6 months)",
"type": "Bars",
"dataset": {
"type": "format-dates",
"sources": [
{
// A @date_histogram query
"type": "OTQL",
"query_id": "666"
}
],
"date_options": {
"format": "YYYY-MM-DD"
}
}
}
```
{% endtab %}
{% tab title="Multiple series" %}
The same transformation applies to the result of a `join`, so you can format the dates of several series at once - for instance monthly events per channel.
Wrap the `join` dataset in `format-dates` rather than formatting each source separately.
{% endtab %}
{% endtabs %}
## A collection volume with its history
A good way to show a collection volume is the **current number** next to a **short history**. That is two charts in one card:
* a `Metric` chart on an OTQL query such as `SELECT @count{} FROM UserPoint`,
* a `Bars` chart on a **collection volumes** query.
```json5
{
// A small card with a vertical layout
"x": 0,
"y": 0,
"h": 2,
"w": 3,
"layout": "vertical",
"charts": [
{
"title": "UserPoint",
"type": "Metric",
"dataset": {
"type": "OTQL",
"query_id": "666" // SELECT @count{} FROM UserPoint
}
},
{
"title": "",
"type": "Bars",
"dataset": {
// format-dates so the axis shows dates, not timestamps
"type": "format-dates",
"sources": [
{
"type": "collection_volumes",
"query_json": {
"dimensions": [ { "name": "date_time" } ]
// plus metrics, filters, and date ranges
}
}
]
}
}
]
}
```
The collection volumes data source is described in [Monitor collection volume](/setup-and-monitor/operate-and-monitor/monitor-collection-volume.md).
## Comparing an audience to the datamart
To answer "do users in this audience behave differently from all users?", build a dashboard at the `builders` and/or `segments` scope with three things: the metric **for the audience**, the same metric **for all users**, and an **index** to show what is over- or under-represented.
```json5
{
"title": "Viewing modes",
"type": "Bars",
"dataset": {
"type": "index",
"sources": [
{
"type": "OTQL",
"query_id": "666", // SELECT {events {session_mode @map}} FROM UserPoint
"series_title": "Segment"
},
{
"type": "OTQL",
"query_id": "666", // the same query
"series_title": "Datamart",
"adapt_to_scope": false // <- this is what makes it the reference
}
],
"options": {
"limit": 10,
"minimum_percentage": 1,
"sort": "Descending"
}
},
"options": {
"type": "bar",
"plot_line_value": 100, // the 100 = "same as reference" line
"format": "index"
}
}
```
{% hint style="success" %}
The trick is `adapt_to_scope: false` on the second source. Both sources use the **same query**; the first is automatically scoped to the segment or builder the dashboard sits in, and the second is not - which is what makes it the datamart-wide reference.
`plot_line_value: 100` draws the neutral line, so a reader can see at a glance which values are over-represented.
{% endhint %}
## Names instead of IDs
A chart returning channels, compartments, or segments shows IDs, which nobody recognises. The **`get-decorators`** transformation replaces them with names.
```json
{
"title": "Data by channels",
"type": "Bars",
"dataset": {
"type": "get-decorators",
"sources": [
{
"type": "to-percentages",
"sources": [
{
"type": "OTQL",
"query_id": "666" // SELECT { channel_id @map } FROM UserEvent WHERE ...
}
]
}
],
"decorators_options": {
"model_type": "CHANNELS"
}
},
"options": {
"format": "percentage"
}
}
```
{% hint style="info" %}
Note how transformations **nest**: the OTQL result goes through `to-percentages`, and that result goes through `get-decorators`. Order matters - decorate last, once the values are final.
Setting `"format": "percentage"` on the chart options is what makes the labels and tooltips read as percentages.
{% endhint %}
## A `@cardinality` query as a single number
`@cardinality` queries return a key/value dataset. Even when that dataset holds a single value, it cannot be displayed as a `Metric` chart as-is. The **`reduce`** transformation puts it in the right format.
```json
{
"title": "Number of different event names retrieved",
"type": "Metric",
"dataset": {
"type": "reduce",
"sources": [
{
"type": "OTQL",
"query_id": "666" // SELECT { nature @cardinality } FROM ActivityEvent
}
]
}
}
```
## Habits worth keeping
* **Several small dashboards, not one big one** - it avoids executing too many requests at once.
* **Save charts in the Query Tool** where you can, so a change propagates to every dashboard using them. Editing a chart in advanced mode **breaks that link**.
* **Decorate anything that returns an ID.**
* **Format dates** on every histogram, or your axis shows timestamps.
* Give every chart a title that states the question it answers.
## Related pages
---
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