The dataSettings feature allows users to define settings for importing, processing, and transforming data from various sources like CSV, JSON, or APIs. Users can specify the data source URL, file type, data handling methods, and transformation options.

Example with a simple CSV file(s):

  "dataSettings": {
    "dataURL": "https://example.com/data.csv",
    "idColumnName": "id",
    "fileType": "csv",
    "delimiter": ",",
  }

dataURL can be an array of strings if you have multiple CSV files

  "dataSettings": {
     "dataURL": [
      "https://example.com/data1.csv",
      "https://example.com/data2.csv"
    ],
    "idColumnName": "id",
    "fileType": "csv",
    "delimiter": ",",
  }

• dataURL: A string or an array of URLs to the CSV file(s).
• idColumnName: The primary key column used to merge data across files.
• fileType: Specifies the file type as "csv" or "json".
• delimiter: Sets the delimiter for CSV data to a comma (,).

Example with an API data source:

 {
  "dataSettings": {
    "dataURL": "https://api.example.com/data",
    "idColumnName": "id",
    "fileType": "api",
    "apiMethod": "POST",
    "apiHeaders": {
      "Authorization": "Bearer token"
    },
    "apiRequestBody": {
      "query": "exampleQuery"
    },
    "dataTransformation": "{{json data}}"
  }
}

• dataURL: URL to the API endpoint.
• idColumnName: Primary key for merging data (in this case, "id").
• fileType: Specifies the file type as "api".
• apiMethod: Sets the HTTP method to POST.
• apiHeaders: Adds an authorization header for API access.
• apiRequestBody: Defines the body content for the API request.
• dataTransformation: Uses a Handlebars template to extract the data object from the API response.

Additionally, there is a feature columnsToArray that allows to transform strings 'tag 1, tag 2, tag 3' into an array or strings. This will help to visualize the number of tags. 

For example, if you have data like this:

[
  { "Country": "Country A", "Tags": "tag 1, tag 2, tag 3" },
  { "Country": "Country B", "Tags": "tag 1, tag 2" },
]

After using columnsToArray, this data would look like:

[
  { "Country": "Country A", "Tags": ["tag 1", "tag 2", "tag 3"] },
  { "Country": "Country B", "Tags": ["tag 1", "tag 2"] },
]

And after applying dataTransform to count each tag's occurrences (more information about dataTransform below):

"dataTransform":{
  "keyColumn": "Tags",
}

The data becomes:

[
  { "Tags": "tag 1", "count": 2 },
  { "Tags": "tag 2", "count": 2 },
  { "Tags": "tag 3", "count": 1 }
]

This final structure can now be used to create a chart, such as a donut chart, where each tag is visualized by its count:

"graphDataConfiguration": [
  { "columnId": "Tags", chartConfigId: "label" },
  { "columnId": "count", chartConfigId: "size" }
]