RANGE Function

Release 5.0.1

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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:

derive type:single value: RANGE(0,3,1 ) as: 'threeSteps'

Output: Generates a column containing the following array:

[0,1,2]

Column reference example:

derive type:single value: RANGE(0,MaxValue,stepValue) as: 'mySteps'

Output: Generates the mySteps column containing arrays of values from zero to the value in the MaxValue column stepping by the stepValue column value.

Syntax and Arguments

derive type:single value: RANGE(column_integer_start, column _integer_end, column_integer_step)

Argument	Required?	Data Type	Description
column_integer_start	Y	string or integer	Name of column or Integer literal that represents the start of the range
column_integer_end	Y	string or integer	Name of column or Integer literal that represents the end of the range
column_integer_step	Y	string or integer	Name 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 Type	Example Value
Yes	Integer	`0`

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 Type	Example Value
Yes	Integer	`20`

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 Type	Example Value
Yes	Integer	`2`

Examples

Tip: For additional examples, see Common Tasks.

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:

Timestamp	Errors
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"]

Transform:

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.

derive type:single value:ARRAYLEN(Errors) as:'arraylength_Errors'

This transform deletes rows that contain no errors:

delete row:(arraylength_Errors == 0)

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

derive type:single value:RANGE(0,arraylength_Errors,1) as:'range_Errors'

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

derive type:single value:ARRAYZIP([range_Errors,Errors]) as:'zipped_Errors'

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

unnest col:zipped_Errors

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

arraylength_Errors
range_Errors
zipped_Errors

Results:

Timestamp	Errors	individual_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:

One row for each student test
Unique identifier for each student-score combination

LastName	FirstName	Scores
Adams	Allen	[81,87,83,79]
Burns	Bonnie	[98,94,92,85]
Cannon	Charles	[88,81,85,78]

Transform:

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

header

replace col:Scores with:'' on:`"` global: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:

derive type:single value: (4 - ARRAYLEN(Scores)) as: '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:

derive type:single value:RANGE(0,ARRAYLEN(Scores)) as:'Tests'

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

derive type:single value:SOURCEROWNUMBER() as:'orderIndex'

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

LastName	FirstName	Scores	Tests	orderIndex
Adams	Allen	[81,87,83,79]	[0,1,2,3]	2
Burns	Bonnie	[98,94,92,85]	[0,1,2,3]	3
Cannon	Charles	[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:

derive type:single value:ARRAYZIP([Tests,Scores])

Your dataset has been changed:

LastName	FirstName	Scores	Tests	orderIndex	column1
Adams	Allen	[81,87,83,79]	[0,1,2,3]	2	[[0,81],[1,87],[2,83],[3,79]]
Adams	Bonnie	[98,94,92,85]	[0,1,2,3]	3	[[0,98],[1,94],[2,92],[3,85]]
Cannon	Charles	[88,81,85,78]	[0,1,2,3]	4	[[0,88],[1,81],[2,85],[3,78]]

With the flatten transform, you can unpack the nested array:

flatten col: 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 unnest:

unnest col:column1 keys:'[0]','[1]'

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

rename mapping:[column_0,'TestNum']

rename mapping:[column_1,'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:

derive type:single value: (orderIndex * 10) + TestNum as: 'TestId'

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

merge col:'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:

merge col:'LastName','FirstName' with:'-' as:'studentId'

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

derive type:single value:AVERAGE(TestScore) group:studentId as:'avg_TestScore'

Results:

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

TestId	LastName	FirstName	TestNum	TestScore	studentId	avg_TestScore
TestId0021	Adams	Allen	0	81	Adams-Allen	82.5
TestId0022	Adams	Allen	1	87	Adams-Allen	82.5
TestId0023	Adams	Allen	2	83	Adams-Allen	82.5
TestId0024	Adams	Allen	3	79	Adams-Allen	82.5
TestId0031	Adams	Bonnie	0	98	Adams-Bonnie	92.25
TestId0032	Adams	Bonnie	1	94	Adams-Bonnie	92.25
TestId0033	Adams	Bonnie	2	92	Adams-Bonnie	92.25
TestId0034	Adams	Bonnie	3	85	Adams-Bonnie	92.25
TestId0041	Cannon	Chris	0	88	Cannon-Chris	83
TestId0042	Cannon	Chris	1	81	Cannon-Chris	83
TestId0043	Cannon	Chris	2	85	Cannon-Chris	83
TestId0044	Cannon	Chris	3	78	Cannon-Chris	83

See Also for RANGE Function:

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RANGE Function

Basic Usage

Syntax and Arguments

column_integer_start

column_integer_end

column_integer_step

Examples

Example - Breaking out log messages

Example - unnest test scores