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Data summarizations and aggregations
Includes recipes utilizing tabulations, g functions, selections, and performing sorts and various mathematical operations.

Data summarizations and aggregations
Includes recipes utilizing tabulations, g functions, selections, and performing sorts and various mathematical operations.
- Determining the most highly correlated pairs
  You can calculate the correlation coefficient of two items using g_cor(G;S;X;Y), but sometimes it is necessary to compute the correlation coefficient for multiple pairs and determine which items are the most highly correlated.
- Parameterizing column names
  Sometimes it is necessary to dynamically determine column names for computed columns based on the information contained in a variable.
- Dividing data into buckets
  Dividing data into buckets can make it easier to perform analyses and gather information about the data, especially if the data set is large. If the location of the items are not important, you can randomly divide the data into a predefined number of equal groups.
- Calculating unique counts with different criteria
  Calculating the unique count of a specific column can be accomplished with a tabulation. However, calculating that unique count for two different situations requires more manipulation.
- Creating pairs without duplication
  You can create a list of every possible combination of two lists of items by performing a link and expand. However, with this method, you will get duplicate pairs, where the items are simply listed in a different order (i.e., reciprocals). Using computed columns and g_functions, you can create a list of pairs without any reciprocals.

Data summarizations and aggregations

Includes recipes utilizing tabulations, g functions, selections, and performing sorts and various mathematical operations.