Creating More Advanced Charts

Source code is located at the end of this tutorial.

This tutorial introduces you to Symbol Type marks by creating a heatmap visualization. The heatmap shows contribution level to the Republican party within the continental United States:

The contribution data are obtained using the following SQL query:

"data": [
  {
   "name": "heatmap_query",
   "sql": "SELECT rect_pixel_bin(conv_4326_900913_x(lon), -13847031.457875465, -7451726.712679257, 733, 733) as x,
           rect_pixel_bin(conv_4326_900913_y(lat), 2346114.147993467, 6970277.197053557, 530, 530) as y,
           SUM(amount) as cnt
           FROM contributions
           WHERE (lon >= -124.39000000000038 AND lon <= -66.93999999999943) AND
               (lat >= 20.61570573311549 AND lat <= 52.93117449504004) AND
               amount > 0 AND
               recipient_party = 'R'
           GROUP BY x, y"
  }
]

The visualization uses a Symbol Type marks type to represent each data item in the heatmap_query data table:

"marks": [
    {
        "type": "symbol",
        "from": {
            "data": "heatmap_query"
        },
        "properties": { ... elided ... }
    }
]

The marks properties property specifies the symbol shape, which is a square. Each square has a pixel width and height of one pixel.

"marks": [
    {
        ... elided ...

        "properties": {
            "shape": "square",
            "x": {
                "field": "x"
            },
            "y": {
                "field": "y"
            },
            "width": 1,
            "height": 1,
            "fillColor": {
                "scale": "heat_color",
                "field": "cnt"
            }
        }
    }
]

Notice that the data x and y location values do not reference a scale. The location values are the values of the SQL query, transformed using extension functions.

The fill color of the square uses the heat_color scale to determine the color used to represent the data item.

Quantize scales are similar to linear scales, except they use a discrete rather than continuous range. The continuous input domain is divided into uniform segments based on the number of values in the output range.

"scales": [
    {
        "name": "heat_color",
        "type": "quantize",
        "domain": [
            10000.0,
            1000000.0
        ],
        "range": [ "#0d0887", "#2a0593", "#41049d", "#5601a4", "#6a00a8",
                   "#7e03a8", "#8f0da4", "#a11b9b", "#b12a90", "#bf3984",
                   "#cb4679", "#d6556d", "#e16462", "#ea7457", "#f2844b",
                   "#f89540", "#fca636", "#feba2c", "#fcce25", "#f7e425", "#f0f921"
        ],
        "default": "#0d0887",
        "nullValue": "#0d0887"
    }
]

A heatmap shows a continuous input domain divided into uniform segments based on the number of values in the output range. This is a quantize scales type. In the example, dollar amounts between $10,000 and $1 million are uniformly divided among 21 range values, where the larger amounts are represented by brighter colors.

Values outside the domain and null values are rendered as dark blue, #0d0887.

Source Code

Advanced Chart Type Tutorial Directory Structure

index.html
/js
  browser-connector.js
  vegaspec.js
  vegademo.js

HTML

Advanced Chart Type Tutorial index.html

<!DOCTYPE html>
<html lang="en">
  <head>
    <title>OmniSci</title>
    <meta charset="UTF-8">
  </head>
  <body>
    <script src="js/browser-connector.js"></script>
    <script src="js/vegaspec.js"></script>
    <script src="js/vegademo.js"></script>

    <script>
    document.addEventListener('DOMContentLoaded', init, false);
    </script>
  </body>
</html>

JavaScript

Advanced Chart Type Tutorial vegademo.js

function init() {
  var vegaOptions = {}
  var connector = new MapdCon()
    .protocol("http")
    .host("my.host.com")
    .port("6273")
    .dbName("omnisci")
    .user("omnisci")
    .password("changeme")
    .connect(function(error, con) {
      con.renderVega(1, JSON.stringify(exampleVega), vegaOptions, function(error, result) {
        if (error) {
          console.log(error.message);
        }
        else {
          var blobUrl = `data:image/png;base64,${result.image}`
          var body = document.querySelector('body')
          var vegaImg = new Image()
          vegaImg.src = blobUrl
          body.append(vegaImg)
        }
      });
    });
}

Advanced Chart Type Tutorial vegaspec.js

const exampleVega = {
  "width": 733,
  "height": 530,
  "data": [
    {
      "name": "heatmap_query",
      "sql": "SELECT rect_pixel_bin(conv_4326_900913_x(lon), -13847031.457875465, -7451726.712679257, 733, 733) as x,
                     rect_pixel_bin(conv_4326_900913_y(lat), 2346114.147993467, 6970277.197053557, 530, 530) as y,
                     SUM(amount) as cnt
                     FROM contributions
                     WHERE (lon >= -124.39000000000038 AND lon <= -66.93999999999943) AND
                           (lat >= 20.61570573311549 AND lat <= 52.93117449504004) AND
                           amount > 0 AND
                           recipient_party = 'R'
                           GROUP BY x, y"
    }
  ],
  "scales": [
    {
      "name": "heat_color",
      "type": "quantize",
      "domain": [
        10000.0,
        1000000.0
      ],
      "range": [ "#0d0887", "#2a0593", "#41049d", "#5601a4", "#6a00a8",
                 "#7e03a8", "#8f0da4", "#a11b9b", "#b12a90", "#bf3984",
                 "#cb4679", "#d6556d", "#e16462", "#ea7457", "#f2844b",
                 "#f89540", "#fca636", "#feba2c", "#fcce25", "#f7e425", "#f0f921"
      ],
      "default": "#0d0887",
      "nullValue": "#0d0887"
    }
  ],
  "marks": [
    {
      "type": "symbol",
      "from": {
        "data": "heatmap_query"
      },
      "properties": {
        "shape": "square",
        "x": {
          "field": "x"
        },
        "y": {
          "field": "y"
        },
        "width": 1,
        "height": 1,
        "fillColor": {
          "scale": "heat_color",
          "field": "cnt"
        }
      }
    }
  ]
};

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Last updated 1 year ago

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