Data Analysis Features

SQLSeal Chart comes integrated with EChart-Stat module that allows you to perform:

clustering (i.e. KMeans)
regression
histogram generation

Enabling the feature

The feature is enabled by default, no extra work is needed

Examples

Clustering

You can generate your data cluster. This demo is based on the official demo from ECharts that can be found here.

TABLE clustering = file(./Clustering Data.csv)
ADVANCED MODE
CHART

const datasetArray = data.map(d => ([d.x, d.y]))

var CLUSTER_COUNT = 6;
var DIENSIION_CLUSTER_INDEX = 2;
var COLOR_ALL = [
  '#37A2DA',
  '#e06343',
  '#37a354',
  '#b55dba',
  '#b5bd48',
  '#8378EA',
  '#96BFFF'
];
var pieces = [];
for (var i = 0; i < CLUSTER_COUNT; i++) {
  pieces.push({
    value: i,
    label: 'cluster ' + i,
    color: COLOR_ALL[i]
  });
}
return {
  dataset: [
  {
      source: datasetArray
    },
    {
      transform: {
        type: 'ecStat:clustering',
        print: true,
        config: {
          clusterCount: CLUSTER_COUNT,
          outputType: 'single',
          outputClusterIndexDimension: DIENSIION_CLUSTER_INDEX
        }
      }
    }
  ],
  tooltip: {
    position: 'top'
  },
  visualMap: {
    type: 'piecewise',
    top: 'middle',
    min: 0,
    max: CLUSTER_COUNT,
    left: 10,
    splitNumber: CLUSTER_COUNT,
    dimension: DIENSIION_CLUSTER_INDEX,
    pieces: pieces
  },
  grid: {
    left: 120
  },
  xAxis: {},
  yAxis: {},
  series: {
    type: 'scatter',
    encode: { tooltip: [0, 1] },
    symbolSize: 15,
    itemStyle: {
      borderColor: '#555'
    },
    datasetIndex: 1
  }
};

SELECT * FROM clustering

Sample data:

x	y
3.275154	2.957587
-3.344465	2.603513
0.355083	-3.376585
1.852435	3.547351
-2.078973	2.552013
-0.993756	-0.884433
2.682252	4.007573
-3.087776	2.878713
-1.565978	-1.256985
2.441611	0.444826
-0.659487	3.111284
-0.459601	-2.618005
2.17768	2.387793
-2.920969	2.917485
-0.028814	-4.168078
3.625746	2.119041
-3.912363	1.325108
-0.551694	-2.814223
2.855808	3.483301
-3.594448	2.856651
0.421993	-2.372646
1.650821	3.407572
-2.082902	3.384412
-0.718809	-2.492514
4.513623	3.841029
-4.822011	4.607049
-0.656297	-1.449872
1.919901	4.439368
-3.287749	3.918836
-1.576936	-2.977622
3.598143	1.97597
-3.977329	4.900932
-1.79108	-2.184517
3.914654	3.559303
-1.910108	4.166946
-1.226597	-3.317889
1.148946	3.345138
-2.113864	3.548172
0.845762	-3.589788
2.629062	3.535831
-1.640717	2.990517
-1.881012	-2.485405
4.606999	3.510312
-4.366462	4.023316
0.765015	-3.00127
3.121904	2.173988
-4.025139	4.65231
-0.559558	-3.840539
4.376754	4.863579
-1.874308	4.032237
-0.089337	-3.026809
3.997787	2.518662
-3.082978	2.884822
0.845235	-3.454465
1.327224	3.358778
-2.889949	3.596178
-0.966018	-2.839827
2.960769	3.079555
-3.275518	1.577068
0.639276	-3.41284

Regression

You can use regression to match a function against your data. Following is the example from ECharts adapted for use in Obsidian:

TABLE regression = file(./Regression Data.csv)
ADVANCED MODE
CHART
const dataArray = data.map(d => ([d.x, d.y]))

return {
  dataset: [
    {
      source: dataArray
    },
    {
      transform: {
        type: 'ecStat:regression',
        config: {
          method: 'exponential'
          // 'end' by default
          // formulaOn: 'start'
        }
      }
    }
  ],
  title: {
    text: '1981 - 1998 gross domestic product GDP (trillion yuan)',
    subtext: 'By ecStat.regression',
    sublink: 'https://github.com/ecomfe/echarts-stat',
    left: 'center'
  },
  tooltip: {
    trigger: 'axis',
    axisPointer: {
      type: 'cross'
    }
  },
  xAxis: {
    splitLine: {
      lineStyle: {
        type: 'dashed'
      }
    }
  },
  yAxis: {
    splitLine: {
      lineStyle: {
        type: 'dashed'
      }
    }
  },
  series: [
    {
      name: 'scatter',
      type: 'scatter',
      datasetIndex: 0
    },
    {
      name: 'line',
      type: 'line',
      smooth: true,
      datasetIndex: 1,
      symbolSize: 0.1,
      symbol: 'circle',
      label: { show: true, fontSize: 16 },
      labelLayout: { dx: -20 },
      encode: { label: 2, tooltip: 1 }
    }
  ]
}
SELECT * FROM regression

Sample data:

x	y
1	4862.4
2	5294.7
3	5934.5
4	7171
5	8964.4
6	10202.2
7	11962.5
8	14928.3
9	16909.2
10	18547.9
11	21617.8
12	26638.1
13	34634.4
14	46759.4
15	58478.1
16	67884.6
17	74462.6
18	79395.7

Histogram

Example to be implemented. Check back soon!

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