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wiki:pivotdescription [2014/02/10 15:17] tony |
wiki:pivotdescription [2014/02/10 15:36] tony |
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To break down the dataset into categories and country, you just have to configure the x and y Dimentions: \\ | To break down the dataset into categories and country, you just have to configure the x and y Dimentions: \\ | ||
+ | <code javascript> | ||
xDimension : [{ | xDimension : [{ | ||
dataName: 'CategoryName' | dataName: 'CategoryName' | ||
Line 45: | Line 46: | ||
dataName: 'Country' | dataName: 'Country' | ||
}] | }] | ||
+ | </code> | ||
+ | Multiple levels are supported, so you can just specify them in the y and x Dimensions. \\ | ||
+ | Grouping the rows and/or columns is done automatically when two or more levels are set in \\ | ||
+ | xDimension and/or yDimension. | ||
+ | |||
+ | Now that you broke down the dataset on the y and x Dimension, it’s time to aggregate \\ | ||
+ | the cell values. Several kinds of aggregations are available including: \\ | ||
+ | sum, min, max, count etc. Future release will provide your own aggregation function. | ||
+ | |||
+ | <code javascript> | ||
+ | |||
+ | aggregates : [{ | ||
+ | member : 'Price', | ||
+ | aggregator : 'sum', | ||
+ | width:50, | ||
+ | label:'Sum' | ||
+ | },{ | ||
+ | member : 'Quantity', | ||
+ | aggregator : 'count', | ||
+ | width:50, | ||
+ | label: 'Count' | ||
+ | }] | ||
+ | </code> | ||
+ | |||
+ | As you have probably noticed in the above example, you could aggregate multiple data \\ | ||
+ | fields, say "Price" and "Quantity". You can easily achieve this by configuring all \\ | ||
+ | required aggregations. |