visualization.json

Visualization.json

Accepts an array of objects, each object describing the visualization. Example:

#!json
[

    {
        "visualizationType": "dataTable",
        "attributes":[

        {"attributeName": "CancerType"},
        {"attributeName": "BCRPatientUIDFromClinical"},
        {"attributeName": "BCRSlideUID"},
        {"attributeName": "BCRPatientUIDFromPathology"}
        ],
        "heading": "TCGA",
        "subheading": ""
    },

    {
        "visualizationType": "imageGrid",
        "attributes":[
            {
                "attributeName": "image",
                "type": "image"
            }
        ],
        "heading": "Bubble Chart",
        "subheading": "Using synthetic data"
    },
    {

        "visualizationType": "heatMap",
        "attributes":[

            {
                "attributeName": "AgeatInitialDiagnosis",
                "type": "x"
            },
            {
                "attributeName": "KarnofskyScore",
                "type": "y"
            }
        ],
        "heading": "Heat Map",
        "subheading": "AgeatInitialDiagnosis vs KarnofskyScore"
    }

]

In the above example we have 3 visualizations dataTable, imageGrid and heatMap. Details of the supported visualizations are described below.

The system currently supports 6 types of visualizations:

1. dataTable
2. bubbleChart
3. imageGrid
4. heatMap
5. geoCloroplethMap
6. markerMap
7. twoDimStat

1. dataTable

Provides a tabular representation of the provided attributes. Shows 100 records at a time.

#!json
{
    "visualizationType": "dataTable",
    "attributes":[
    	{"attributeName": "id"},
    	{"attributeName": "Ai"},
    	{"attributeName": "Di"}
    ]
}

2. bubbleChart

A bubble chart representation of the provided attributes. Can be used to visualize 4 dimensions.

#!json

{
    "visualizationType": "bubbleChart",
    "attributes":[
        {
            "attributeName": "a1",
            "type": "x",
            "dimension": true
        },
        {
            "attributeName": "a2",
            "type": "y"
        },
        {
            "attributeName": "a3",
            "type": "color"
        },
        {
            "attributeName": "a4",
            "type": "r"
        },
    ]
}

Following types are used to represent 4 dimensions on the chart.

• x : on the x axis
• y : on the y axis
• r : radius of bubbles
• color: colors of bubbles

Atleast one attributes needs to have dimension: true.

3. imageGrid

Creates an image grid using the images from the attribute having type: image

#!json

{
    "visualizationType": "imageGrid",
    "attributes":[
        {
            "attributeName": "image",
            "type": "image"
        }
    ],
    "heading": "Image grid",
    "subheading": "Using dummy data"
}

Requires an attribute to have "type": "image" which shall be used as the location of the image.

4. heatMap

#!json
    {

        "visualizationType": "heatMap",
        "attributes":[

            {
                "attributeName": "AgeatInitialDiagnosis",
                "type": "x"
            },
            {
                "attributeName": "KarnofskyScore",
                "type": "y"
            }
        ],
        "heading": "Heat Map",
        "subheading": "AgeatInitialDiagnosis vs KarnofskyScore"
    }

Requires attributes having "type": "x" and "type": "y" for the x and y axes respectively.

5. geoChoroplethMap

Creates a cloropleth map by rendering a geo location file.

#!json
    {
      "visualizationType": "geoChoroplethMap",
      "attribute": {
        "name": "ProviderState",
        "type": "ABBR"
      },
      "geoJson": {
        "path": "data/geoJsonUSStates.json"
      },
      "heading": "Geo Choropleth Map",
      "subheading": "Medicare data"
    }

• visualizationType needs to be geoCloroplethMap
• attribute contains information about the attribute that is going to to be used for drawing the map
  • name is the name of the attribute in the dataset
  • type is the type of the attribute. Currently it supports two different types: NAME and ABBR. They basically represent the name of the attribute in the geo location file. For example if the dataset contains the entire names of the states, type will be NAME, if the dataset contains the abbreviations of the states' names, type will be ABBR
• geoJson contains information about the geo location file that will be rendered
  • path is the location of the geo location file. Currently Datascope can render only the states of the US and the file is data/geoJsonUSStates.json
• heading is the title that the visualization will have
• subheading is a subtitle that the visualization will have

Datascope is summing up the occurrences within a dataset for each state, the color gradient of a state meaning the number of the entries in the dataset from that state.

Using a shape other than US map

You'll have to change the dc.js projection to support whatever shape(county, world etc) you have. For example to view Atlanta, GA counties using Georgia's GeoJSON. Modify the projection to zoom and center into Georgia

     _chart.projection = function (projection) {
         projection = d3.geo.albers().center([14,33.7]).scale(20000);
         _geoPath.projection(projection);
         _projectionFlag = true;
         return _chart;

6. markerMap

Creates a leaflet map containing markers that are specified in the dataset.

#!json
    {
        "visualizationType": "markerMap",
        "attributeName": "geo",
        "heading": "Marker Map",
        "subheading": "Renewable Energy Plants in Bulgaria on 2012"
    }

• visualizationType needs to be markerMap
• attributeName is the name of the attribute in the dataset containing the markers. It can be set as either 'lat,lng' string or as an array [lat,lng]
• heading is the title that the visualization will have
• subheading is a subtitle that the visualization will have

7. twoDimStat

Creates a two dimensional statistics visualization. It shows a table containing the statistics performed on two attributes specified by the user in the configuration file.

#!json
    {
        "visualizationType": "twoDimStat",
        "attributes": {
          "attr1": "Age",
          "attr2": "Survived"
        },
        "statistics": [
          "correlation", "custom-twoDimMean", "dotProduct"
        ],
        "heading": "Two Dimensional Statistics: Age vs Survived"
  }

• visualizationType needs to be twoDimStat
• attributes contains the two attributes that are going to be used in computing the two dimensional statistics
  • attr1 represents the first attribute
  • attr2 represents the second attribute
• statistics contains the statistics to be computed for the desired attributes. By being left as default, all the possible statistics will be automatically computed (correlation, rankCorrelation, dotProduct, euclidianDistance, covariance, cohensd). Otherwise, the user needs to specify the statistics one by one within an array. In this array the user can also specify custom statistics that he previously declared in /modules/customStatistics.js file. They always need to be named starting with custom- (ex: custom-twoDimMean). Futher is an example of implementing a new statistic:

  var dl = require('datalib');
  global.twoDimMean = function (data, attr1, attr2) {
      var summary1 = dl.summary(data, [attr1])[0];
      var summary2 = dl.summary(data, [attr2])[0];
  
      var myMean = summary1["mean"] * summary2["mean"];
  
      return myMean;
  };
  

• heading is the title that the visualization will have.