Aggregate node

Aggregation is a data preparation task frequently used to reduce the size of a dataset. Before proceeding with aggregation, you should take time to clean the data, concentrating especially on missing values. Once you have aggregated, potentially useful information regarding missing values may be lost.

You can use an Aggregate node to replace a sequence of input records with summary, aggregated output records. For example, you might have a set of input sales records such as those shown in the following table.

Table 1. Sales record input example
Age Sex Region Branch Sales
23 M S 8 4
45 M S 16 4
37 M S 8 5
30 M S 5 7
44 M N 4 9
25 M N 2 11
29 F S 16 6
41 F N 4 8
23 F N 6 2
45 F N 4 5
33 F N 6 10

You can aggregate these records with Sex and Region as key fields. Then choose to aggregate Age with the mode Mean and Sales with the mode Sum. Select the Include record count in field aggregate node option and your aggregated output will be similar to the following table.

Table 2. Aggregated record example
Age (mean) Sex Region Sales (sum) Record Count
35.5 F N 25 4
29 F S 6 1
34.5 M N 20 2
33.75 M S 20 4

From this you learn, for example, that the average age of the four female sales staff in the North region is 35.5, and the sum total of their sales was 25 units.

Note: Fields such as Branch are automatically discarded when no aggregate mode is specified.