Campaign Lab Data Pipeline

What?

• We want to be able to structure our dataset (see "Campaign Lab Data Inventory").
• In order to do this, we first should define what the structure (schema) of the different data sources are.
• This will help us down the line to create modules that transform our raw data into our target data, for later export into a database, R package, or any other tools for utilising the data in a highly structured and annotated format.

How can I contribute?

• We need to go through each of the datasources that we have defined in "Campaign Lab Data Inventory", create a transformer (in the transformers folder), and associated schema for each datasource.
• The transformer should be able to be run in a machine locally, downloading the data and transforming it into a CSV (later importing it into a local database).

• To contribute:

  1. Open an Issue with the name of the issue formatted as description-rowIdentifier, where description and rowIdentifier are what is in the excel spreadsheet "Campaign Lab Data Inventory".
  2. Write a small description of which dataset you are trying to transform and create a schema for.
  3. Open a Pull Request (create a branch with an appropriate name) when you're finished

Formatting

• We need to make sure we format similar fields between datasources in the same way.

• For now, the standardization should follow:

• Timestamp fields: 2015-06-30T22:30:00.000Z

What is a schema?

• A schema in this case is basically just a JSON (JavaScript Object Notation) that describes the structure and format of the dataset.

• an example schema would be

  "title": "Election results",
  "source": "https://data.police.uk/docs/method/forces/"
  "description": "A dataset of election results",
  "properties": {
    "county": {
      "type": ["string"],
      "description": "The county in which the result was"
    },
    "number_of_votes": {
      "type": ["integer"],
      "description": "The number of votes that were received"
    },
    "party": {
      "type": ["string"],
      "description": "the party which was receiving votes"
    }
  }
}

• The title tells you the name of the dataset (you can make this up)
• source is a link (if available) to the actual dataset.
• The description is a one liner that describes the dataset
• properties is a list of the datapoints that we want to end up with after transforming the raw dataset.