Overview: Transpose

Pivoting data in a table is an important technique for data summarization - it assists in producing meaningful information from a table of information. While the structure of the original dataset is not changed in any way, transposing using pivot tables can automatically sort, count, and sum the data stored in one table and create a second table (called the "pivot table") to present the summarized data.

A pivot table usually consists of row, column, and data fields, which allow several kinds of aggregations including: sum, average, standard deviation, and count for example. Data is said to be rotated or pivoted in order to produce summarized information, and this concept gives the table its name.

When pivoting a column field, this field is then pivoted to become a row field, and provides the structural confines within which different aggregations can be completed. The aggregations will be calculated on the data of the other column fields, and will provide summaries as requested and defined during configuration.

Properties

Category:	Transform
Performance risk:	Low performance risk
Knowledge required:	None

Effect on datasets

How many datasets are required to perform this operation?	One
Does it create a new dataset?	Yes
Can you reconfigure this operation?	Yes
Can you apply this operation to a locked dataset?	Yes
Does it modify the current dataset in any way?	No

Requirements

This operation requires a dataset that contains at least two fields: one to be used as the pivot field, the other containing the data on which various aggregations will be performed.
Note: You cannot apply the same aggregation twice on the same data group.

Results

A new dataset will be created containing newly created fields of summarized, aggregate values, based on the configuration specifications of the pivot field.
If the user selected the grouping and sorting options, the dataset will display the fields accordingly.