資料轉換

option = {
  dataset: [
    {
      source: [] // The original data
    },
    {
      // Declare transforms in an array to pipe multiple transforms,
      // which makes them execute one by one and take the output of
      // the previous transform as the input of the next transform.
      transform: [
        {
          type: 'filter',
          config: { dimension: 'Product', value: 'Tofu' }
        },
        {
          type: 'sort',
          config: { dimension: 'Year', order: 'desc' }
        }
      ]
    }
  ],
  series: {
    type: 'pie',
    // Display the result of the piped transform.
    datasetIndex: 1
  }
};

option = {
  dataset: [
    {
      // Original source data.
      source: []
    },
    {
      transform: {
        type: 'boxplot'
      }
      // After this "boxplot transform" two result data generated:
      // result[0]: The boxplot data
      // result[1]: The outlier data
      // By default, when series or other dataset reference this dataset,
      // only result[0] can be visited.
      // If we need to visit result[1], we have to use another dataset
      // as follows:
    },
    {
      // This extra dataset references the dataset above, and retrieves
      // the result[1] as its own data. Thus series or other dataset can
      // reference this dataset to get the data from result[1].
      fromDatasetIndex: 1,
      fromTransformResult: 1
    }
  ],
  xAxis: {
    type: 'category'
  },
  yAxis: {},
  series: [
    {
      name: 'boxplot',
      type: 'boxplot',
      // Reference the data from result[0].
      datasetIndex: 1
    },
    {
      name: 'outlier',
      type: 'scatter',
      // Reference the data from result[1].
      datasetIndex: 2
    }
  ]
};

{
  fromDatasetIndex: 1,
  fromTransformResult: 1,
  transform: {
    type: 'sort',
    config: { dimension: 2, order: 'desc' }
  }
}

option = {
  dataset: [
    {
      source: []
    },
    {
      transform: {
        type: 'filter',
        config: {},
        // The result of this transform will be printed
        // in dev tool via `console.log`.
        print: true
      }
    }
  ]
};

option = {
  dataset: [
    {
      source: [
        // ...
      ]
    },
    {
      transform: {
        type: 'filter',
        config: {
          // Use operator "and".
          // Similarly, we can also use "or", "not" in the same place.
          // But "not" should be followed with a {...} rather than `[...]`.
          and: [
            { dimension: 'Year', '=': 2011 },
            { dimension: 'Price', '>=': 20, '<': 30 }
          ]
        }
        // The condition is "Year" is 2011 and "Price" is greater
        // or equal to 20 but less than 30.
      }
    }
  ],
  series: {
    type: 'pie',
    datasetIndex: 1
  }
};

transform: {
  type: 'filter',
  config: {
    or: [{
      and: [{
        dimension: 'Price', '>=': 10, '<': 20
      }, {
        dimension: 'Sales', '<': 100
      }, {
        not: { dimension: 'Product', '=': 'Tofu' }
      }]
    }, {
      and: [{
        dimension: 'Price', '>=': 10, '<': 20
      }, {
        dimension: 'Sales', '<': 100
      }, {
        not: { dimension: 'Product', '=': 'Cake' }
      }]
    }]
  }
}

option = {
  dataset: [
    {
      source: [
        ['Product', 'Sales', 'Price', 'Date'],
        ['Milk Tee', 311, 21, '2012-05-12'],
        ['Cake', 135, 28, '2012-05-22'],
        ['Latte', 262, 36, '2012-06-02'],
        ['Milk Tee', 359, 21, '2012-06-22'],
        ['Cake', 121, 28, '2012-07-02'],
        ['Latte', 271, 36, '2012-06-22']
        // ...
      ]
    },
    {
      transform: {
        type: 'filter',
        config: {
          dimension: 'Date',
          '>=': '2012-05',
          '<': '2012-06',
          parser: 'time'
        }
      }
    }
  ]
};

type FilterTransform = {
  type: 'filter';
  config: ConditionalExpressionOption;
};
type ConditionalExpressionOption =
  | true
  | false
  | RelationalExpressionOption
  | LogicalExpressionOption;
type RelationalExpressionOption = {
  dimension: DimensionName | DimensionIndex;
  parser?: 'time' | 'trim' | 'number';
  lt?: DataValue; // less than
  lte?: DataValue; // less than or equal
  gt?: DataValue; // greater than
  gte?: DataValue; // greater than or equal
  eq?: DataValue; // equal
  ne?: DataValue; // not equal
  '<'?: DataValue; // lt
  '<='?: DataValue; // lte
  '>'?: DataValue; // gt
  '>='?: DataValue; // gte
  '='?: DataValue; // eq
  '!='?: DataValue; // ne
  '<>'?: DataValue; // ne (SQL style)
  reg?: RegExp | string; // RegExp
};
type LogicalExpressionOption = {
  and?: ConditionalExpressionOption[];
  or?: ConditionalExpressionOption[];
  not?: ConditionalExpressionOption;
};
type DataValue = string | number | Date;
type DimensionName = string;
type DimensionIndex = number;

import {
  DatasetComponent,
  TransformComponent
} from 'echarts/components';

echarts.use([
  DatasetComponent,
  TransformComponent
]);

option = {
  dataset: [
    {
      dimensions: ['name', 'age', 'profession', 'score', 'date'],
      source: [
        [' Hannah Krause ', 41, 'Engineer', 314, '2011-02-12'],
        ['Zhao Qian ', 20, 'Teacher', 351, '2011-03-01'],
        [' Jasmin Krause ', 52, 'Musician', 287, '2011-02-14'],
        ['Li Lei', 37, 'Teacher', 219, '2011-02-18'],
        [' Karle Neumann ', 25, 'Engineer', 253, '2011-04-02'],
        [' Adrian Groß', 19, 'Teacher', null, '2011-01-16'],
        ['Mia Neumann', 71, 'Engineer', 165, '2011-03-19'],
        [' Böhm Fuchs', 36, 'Musician', 318, '2011-02-24'],
        ['Han Meimei ', 67, 'Engineer', 366, '2011-03-12']
      ]
    },
    {
      transform: {
        type: 'sort',
        // Sort by score.
        config: { dimension: 'score', order: 'asc' }
      }
    }
  ],
  series: {
    type: 'bar',
    datasetIndex: 1
  }
  // ...
};

option = {
  dataset: [
    {
      dimensions: ['name', 'age', 'profession', 'score', 'date'],
      source: [
        [' Hannah Krause ', 41, 'Engineer', 314, '2011-02-12'],
        ['Zhao Qian ', 20, 'Teacher', 351, '2011-03-01'],
        [' Jasmin Krause ', 52, 'Musician', 287, '2011-02-14'],
        ['Li Lei', 37, 'Teacher', 219, '2011-02-18'],
        [' Karle Neumann ', 25, 'Engineer', 253, '2011-04-02'],
        [' Adrian Groß', 19, 'Teacher', null, '2011-01-16'],
        ['Mia Neumann', 71, 'Engineer', 165, '2011-03-19'],
        [' Böhm Fuchs', 36, 'Musician', 318, '2011-02-24'],
        ['Han Meimei ', 67, 'Engineer', 366, '2011-03-12']
      ]
    },
    {
      transform: {
        type: 'sort',
        config: [
          // Sort by the two dimensions.
          { dimension: 'profession', order: 'desc' },
          { dimension: 'score', order: 'desc' }
        ]
      }
    }
  ],
  series: {
    type: 'bar',
    datasetIndex: 1
  }
  // ...
};

type SortTransform = {
  type: 'sort';
  config: OrderExpression | OrderExpression[];
};
type OrderExpression = {
  dimension: DimensionName | DimensionIndex;
  order: 'asc' | 'desc';
  incomparable?: 'min' | 'max';
  parser?: 'time' | 'trim' | 'number';
};
type DimensionName = string;
type DimensionIndex = number;

import {
  DatasetComponent,
  TransformComponent
} from 'echarts/components';

echarts.use([
  DatasetComponent,
  TransformComponent
]);

// Register the external transform at first.
echarts.registerTransform(ecStatTransform(ecStat).regression);

option = {
  dataset: [
    {
      source: rawData
    },
    {
      transform: {
        // Reference the registered external transform.
        // Note that external transform has a namespace (like 'ecStat:xxx'
        // has namespace 'ecStat').
        // built-in transform (like 'filter', 'sort') does not have a namespace.
        type: 'ecStat:regression',
        config: {
          // Parameters needed by the external transform.
          method: 'exponential'
        }
      }
    }
  ],
  xAxis: { type: 'category' },
  yAxis: {},
  series: [
    {
      name: 'scatter',
      type: 'scatter',
      datasetIndex: 0
    },
    {
      name: 'regression',
      type: 'line',
      symbol: 'none',
      datasetIndex: 1
    }
  ]
};