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Computes an array of integers, from a beginning integer to an end (stop) integer, stepping by a third parameter.

NOTE: If the function generates more than 100,000 values for a cell, the output is a null value.

Basic Usage

Numeric literal example:

range(0,3,1 )

Output: Returns the following array:

[0,1,2]

Column reference example:

range(0,MaxValue,stepValue)

Output: Returns an array of values from zero to the value in the MaxValue column stepping by the stepValue column value. 

Syntax

range(column_integer_start, column _integer_end, column_integer_step)


ArgumentRequired?Data TypeDescription
column_integer_startYstring or integerName of column or Integer literal that represents the start of the range
column_integer_endYstring or integerName of column or Integer literal that represents the end of the range
column_integer_stepYstring or integerName of column or Integer literal that represents the steps in integers between values in the range

For more information on syntax standards, see Language Documentation Syntax Notes.

column_integer_start

Name of the column or value of the starting integer used to compute the range.

NOTE: This value is always included in the range, unless it is equal to the value for col-integer-stop, which results in a blank array.

  • Missing input values generate missing results.
  • Multiple columns and wildcards are not supported.

Usage Notes:


Required?Data TypeExample Value
YesInteger0

column_integer_end

Name of the column or value of the end integer used to compute the range.

NOTE: This value is not included in the output.

  • Missing input values generate missing results.
  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?Data TypeExample Value
YesInteger20

column_integer_step

Name of the column or value of the integer used to compute the integer interval (step) between each value in the range.

NOTE: This value must be a positive integer. If col-integer-start is greater than col-integer-stop, steps are negative values of this parameter.

  • Missing input values generate missing results.
  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?Data TypeExample Value
YesInteger2

Examples

Example - Breaking out log messages

Source:

Your dataset contains log data that is gathered each minute, yet each entry can contain multiple error messages in an array. The key fields might look like the following:

TimestampErrors
02/16/16 15:31["Unable to connect","File not found","Proxy down","conn. timeout"]
02/16/16 15:30[]
02/16/16 15:29["Access forbidden","Invalid password"]

Transformation:

You can use the following steps to break out the array values into separate rows. The following transform generates a column containing the number of elements in each row's Errors array.

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula arraylen(Errors)
Parameter: New column name 'arraylength_Errors'

This transform deletes rows that contain no errors:

Transformation Name Filter rows
Parameter: Condition Custom formula
Parameter: Type of formula Custom single
Parameter: Condition (arraylength_Errors == 0)
Parameter: Action Delete matching rows

For the remaining rows, you can generate a column containing an array of numbers to match the count of error messages:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula range(0,arraylength_Errors,1)
Parameter: New column name 'range_Errors'

You can then use the ARRAYZIP function to zip together the two arrays into a single one:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula arrayzip([range_Errors,Errors])
Parameter: New column name 'zipped_Errors'

The unnest transform uses the values in an array column as key values to break out rows in your dataset:

Transformation Name Unnest Objects into columns
Parameter: Column zipped_Errors

You might rename the above as individual_Errors. To clean up your dataset, you can now delete the following columns:

  • arraylength_Errors
  • range_Errors
  • zipped_Errors

Results:

TimestampErrorsindividual_Errors
02/16/16 15:31["Unable to connect","File not found","Proxy down","conn. timeout"][0, "Unable to connect"]
02/16/16 15:31["Unable to connect","File not found","Proxy down","conn. timeout"][1, "File not found"]
02/16/16 15:31["Unable to connect","File not found","Proxy down","conn. timeout"][2, "Proxy down"]
02/16/16 15:31["Unable to connect","File not found","Proxy down","conn. timeout"][3, "conn. timeout"]
02/16/16 15:29["Access forbidden","Invalid password"][0, "Access forbidden"]
02/16/16 15:29["Access forbidden","Invalid password"][1, "Invalid password"]

Example - unnest test scores

The following example includes a range example to define a new index array. 

Source:

You have the following data on student test scores. Scores on individual scores are stored in the Scores array, and you need to be able to track each test on a uniquely identifiable row. This example has two goals:

  1. One row for each student test
  2. Unique identifier for each student-score combination

 

LastNameFirstNameScores
AdamsAllen[81,87,83,79]
BurnsBonnie[98,94,92,85]
CannonCharles[88,81,85,78]

Transformation:

When the data is imported from CSV format, you must add a header transform and remove the quotes from the Scores column:

Transformation Name Rename column with row(s)
Parameter: Option Use row(s) as column names
Parameter: Type Use a single row to name columns
Parameter: Row number 1

Transformation Name Replace text or pattern
Parameter: Column colScores
Parameter: Find '\"'
Parameter: Replace with ''
Parameter: Match all occurrences true

Validate test date: To begin, you might want to check to see if you have the proper number of test scores for each student. You can use the following transform to calculate the difference between the expected number of elements in the Scores array (4) and the actual number:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula (4 - arraylen(Scores))
Parameter: New column name 'numMissingTests'

When the transform is previewed, you can see in the sample dataset that all tests are included. You might or might not want to include this column in the final dataset, as you might identify missing tests when the recipe is run at scale.

Unique row identifier: The Scores array must be broken out into individual rows for each test. However, there is no unique identifier for the row to track individual tests. In theory, you could use the combination of LastName-FirstName-Scores values to do so, but if a student recorded the same score twice, your dataset has duplicate rows. In the following transform, you create a parallel array called Tests, which contains an index array for the number of values in the Scores column. Index values start at 0:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula range(0,arraylen(Scores))
Parameter: New column name 'Tests'

Also, we will want to create an identifier for the source row using the sourcerownumber function:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula sourcerownumber()
Parameter: New column name 'orderIndex'

One row for each student test: Your data should look like the following:

LastNameFirstNameScoresTestsorderIndex
AdamsAllen[81,87,83,79][0,1,2,3]2
BurnsBonnie[98,94,92,85][0,1,2,3]3
CannonCharles[88,81,85,78][0,1,2,3]4

Now, you want to bring together the Tests and Scores arrays into a single nested array using the arrayzip function:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula arrayzip([Tests,Scores])

Your dataset has been changed:

LastNameFirstNameScoresTestsorderIndexcolumn1
AdamsAllen[81,87,83,79][0,1,2,3]2[[0,81],[1,87],[2,83],[3,79]]
AdamsBonnie[98,94,92,85][0,1,2,3]3[[0,98],[1,94],[2,92],[3,85]]
CannonCharles[88,81,85,78][0,1,2,3]4[[0,88],[1,81],[2,85],[3,78]]

Use the following to unpack the nested array:

Transformation Name Expand arrays to rows
Parameter: Column column1

Each test-score combination is now broken out into a separate row. The nested Test-Score combinations must be broken out into separate columns using the following:

Transformation Name Unnest Objects into columns
Parameter: Column column1
Parameter: Paths to elements '[0]','[1]'

After you delete column1, which is no longer needed you should rename the two generated columns:

Transformation Name Rename columns
Parameter: Option Manual rename
Parameter: Column column_0
Parameter: New column name 'TestNum'

Transformation Name Rename columns
Parameter: Option Manual rename
Parameter: Column column_1
Parameter: New column name 'TestScore'

Unique row identifier: You can do one more step to create unique test identifiers, which identify the specific test for each student. The following uses the original row identifier OrderIndex as an identifier for the student and the TestNumber value to create the TestId column value:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula (orderIndex * 10) + TestNum
Parameter: New column name 'TestId'

The above are integer values. To make your identifiers look prettier, you might add the following:

Transformation Name Merge columns
Parameter: Columns 'TestId00','TestId'

Extending: You might want to generate some summary statistical information on this dataset. For example, you might be interested in calculating each student's average test score. This step requires figuring out how to properly group the test values. In this case, you cannot group by the LastName value, and when executed at scale, there might be collisions between first names when this recipe is run at scale. So, you might need to create a kind of primary key using the following:

Transformation Name Merge columns
Parameter: Columns 'LastName','FirstName'
Parameter: Separator '-'
Parameter: New column name 'studentId'

You can now use this as a grouping parameter for your calculation:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula average(TestScore)
Parameter: Group rows by studentId
Parameter: New column name 'avg_TestScore'

Results:

After you delete unnecessary columns and move your columns around, the dataset should look like the following:

TestIdLastNameFirstNameTestNumTestScorestudentIdavg_TestScore
TestId0021AdamsAllen081Adams-Allen82.5
TestId0022AdamsAllen187Adams-Allen82.5
TestId0023AdamsAllen283Adams-Allen82.5
TestId0024AdamsAllen379Adams-Allen82.5
TestId0031AdamsBonnie098Adams-Bonnie92.25
TestId0032AdamsBonnie194Adams-Bonnie92.25
TestId0033AdamsBonnie292Adams-Bonnie92.25
TestId0034AdamsBonnie385Adams-Bonnie92.25
TestId0041CannonChris088Cannon-Chris83
TestId0042CannonChris181Cannon-Chris83
TestId0043CannonChris285Cannon-Chris83
TestId0044CannonChris378Cannon-Chris83

 

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