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The Data Library

Examples

Note: A quick read through of the Concepts section will likely be useful in understanding the details of the examples.

Note: for all the following examples you should have included relevant Recline dependencies.

Simple in-memory dataset.

        // Some data you have
        // Your data must be in the form of list of documents / rows
        // Each document/row is an Object with keys and values
        var data = [
            {id: 0, x: 1, y: 2, z: 3, country: 'UK', label: 'first'}
            , {id: 1, x: 2, y: 4, z: 6, country: 'UK', label: 'second'}
            , {id: 2, x: 3, y: 6, z: 9, country: 'US', label: 'third'}
          ];
        
        // Create a Dataset object from local in-memory data
        // Dataset object is a Backbone model - more info on attributes in model docs below
        var dataset = recline.Backend.createDataset(data);
        
        // Now create the main explorer view (it will create other views as needed)
        // DataExplorer is a Backbone View
        var explorer = recline.View.DataExplorer({
          model: dataset,
          // you can specify any element to bind to in the dom
          el: $('.data-explorer-here')
        });
        // Start Backbone routing (if you want routing support)
        Backbone.history.start();

Creating a Dataset Explicitly with a Backend

        // Connect to ElasticSearch index/type as our data source
        // There are many other backends you can use (and you can write your own)
        var backend = new recline.Backend.ElasticSearch();
        
        // Dataset is a Backbone model so the first hash become model attributes
        var dataset = recline.Model.Dataset({
            id: 'my-id',
            // url for source of this dataset - will be used by backend
            url: 'http://localhost:9200/my-index/my-type',
            // any other metadata e.g.
            title: 'My Dataset Title'
          },
          backend
        );

Concepts and Structure

Recline has a simple structure layered on top of the basic Model/View distinction inherent in Backbone.

Models

There are two main model objects:

  • Dataset: represents the dataset. Holds dataset info and a pointer to list of data items (Documents in our terminology) which it can load from the relevant Backend.
  • Document: an individual data item (e.g. a row from a relational database or a spreadsheet, a document from from a document DB like CouchDB or MongoDB).

Additional, related models:

  • Field: a field/column on a dataset.
  • Query: an object to encapsulate a query to the backend (useful both for creating queries and for storing and manipulating query state - e.g. from a query editor).
  • Facet: Object to store Facet information, that is summary information (e.g. values and counts) about a field obtained by some faceting method on the backend.

More detail of how these work can be found in the Model source docs.

Backends

Backends connect Dataset and Documents to data from a specific 'Backend' data source. They provide methods for loading and saving Datasets and individuals Documents as well as for bulk loading via a query API and doing bulk transforms on the backend.

A template Base class can be found in the Backend base module of the source docs. It documents both the relevant methods a Backend must have and (optionally) provides a base 'class' for inheritance. You can also find detailed examples of backend implementations in the source documentation below.

Views

Complementing the model are various Views (you can also easily write your own). Each view holds a pointer to a Dataset:

  • DataExplorer: the parent view which manages the overall app and sets up sub views.
  • Grid: the data grid view.
  • Graph: a simple graphing view using Flot.
  • Map: a map view using Leaflet.

There are additional views which do not display a whole dataset but which are useful:

  • QueryEditor: a query editor view
  • FacetViewer: display facets

Source Docs (via Docco)

Models and Views (Widgets)

Backends