You can use a variety of mathematical and statistical functions to calculate metrics within a column.

• See Aggregate Functions.
• See Math Functions.

To calculate metrics across columns, you can use a generalized version of the following example.

Source:

Your dataset tracks swimmer performance across multiple heats in a race, and you would like to calculate best, worst, and average times in seconds across all three heats. Here's the data:

In the above data, Racer Y was disqualified (DQ) in Heat 2.

Transformation:

To compute the metrics, you must bundle the data into an array, break out the array into separate rows, and then calculate your metrics by grouping. Here are the steps:

1. When the data is imported, you may need to create a header for each row:
   
   

   D trans
   Type step p01Name Option p01Value Use row as header p02Name Row p02Value 1 SearchTerm Rename columns with a row

2. The columns containing heat time data may need to be retyped. From the drop-down next to each column name, select Decimal type.

3. The DQ value in the Heat2 column is invalid data for Decimal type. You can use the following transformation to turn it into a missing value. For purposes of calculating averages, you may or may not want to turn invalid data into zeroes or blanks. In this case, replacing the data as 0.00 causes improper calculations for the metrics.
   

   D trans
   p03Value '' Type step p01Name Column p01Value Heat2 p02Name Find p02Value 'DQ' p03Name Replace with SearchTerm Replace text or patterns

   
   

4. Use the following to gather all of the heat data into two columns:
   

   D trans
   Type step p01Name Columns p01Value Heat1,Heat2,Heat3 p02Name Group size p02Value 1 SearchTerm Unpivot columns

   
   

5. You can now rename the two columns. Rename key to HeatNum and value to HeatTime.

6. You may want to delete the rows that have a missing value for HeatTime:
   

   D trans
   Type step p01Name Condition p01Value ISMISSING([value]) SearchTerm Delete rows

   
   

7. You can now perform calculations on this column. The following transformations calculate minimum, average (mean), and maximum times for each racer:
   

   D trans
   p03Value Racer Type step p01Name Formula type p01Value Multiple row formula p02Name Formula p02Value MIN(HeatTime) p03Name Group rows by p04Value 'BestTime' p04Name New column name SearchTerm New formula

   
   
   

   D trans
   p03Value Racer Type step p01Name Formula type p01Value Multiple row formula p02Name Formula p02Value AVERAGE(HeatTime) p03Name Group rows by p04Value 'AvgTime' p04Name New column name SearchTerm New formula

   D trans
   p03Value Racer Type step p01Name Formula type p01Value Multiple row formula p02Name Formula p02Value MAX(HeatTime) p03Name Group rows by p04Value 'WorstTime' p04Name New column name SearchTerm New formula

    

8. To make the data look better, you might want to reformat the values in the AvgTime column to two decimal points:
   

   D trans
   Type step p01Name Columns p01Value AvgTime p02Name Formula p02Value NUMFORMAT(AvgTime, '##.00') SearchTerm Edit column with formula

Results:

After you use the Move transformation to re-organize your columns, the dataset should look like the following: