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NOTE: This function has been superseded by the $sourcerownumber reference. While this function is still usable in the product, it is likely to be deprecated in a future release. Please use $sourcerownumber instead. For more information, see Source Metadata References.

Returns the row number of the current row as it appeared in the original source dataset before any steps had been applied.

The following transforms might make original row information invalid or otherwise unavailable. In these cases, the function returns null values:

  • pivot
  • flatten
  • join
  • lookup
  • union
  • unnest
  • unpivot

NOTE: If the dataset is sourced from multiple files, a predictable original source row number cannot be guaranteed, and null values are returned.

Tip: If the source row information is still available, you can hover over the left side of a row in the data grid to see the source row number in the original source data.


Basic Usage

Derive Example:

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

Output: Generates a new OriginalRowNums column containing the row numbers for each row as it appeared in the original data.

Sort Example:

sort order: SOURCEROWNUMBER()

Output: Rows in the dataset are re-sorted according to the original order in the dataset.

Delete Example:

delete row:SOURCEROWNUMBER() > 101

Output: Deletes the rows in the dataset that were after row #101 in the original source data.

Syntax

There are no arguments for this function.

Examples

Example - Header from row that is not the first one

Source:

You have imported the following racer data on heat times from a CSV file. When loaded in the Transformer page, it looks like the following:

(rowId)column2column3column4column5
1RacerHeat 1Heat 2Heat 3
2Racer X37.2238.2237.61
3Racer Y41.33DQ38.04
4Racer Z39.2739.0438.85

In the above, the (rowId) column references the row numbers displayed in the data grid; it is not part of the dataset. This information is available when you hover over the black dot on the left side of the screen.

Transform:

You have examined the best performance in each heat according to the sample. You then notice that the data contains headers, but you forget how it was originally sorted. The data now looks like the following:

(rowId)column2column3column4column5
1Racer Y41.33DQ38.04
2RacerHeat 1Heat 2Heat 3
3Racer X37.2238.2237.61
4Racer Z39.2739.0438.85

While you can undo your sort steps to return to the original sort order, this approach works best if you did not include other steps in between that are based on the sort order.

If you have steps that require retaining your sort steps, you can revert to the original sort order by adding this transform step:

NOTE: Source row information may be lost after operations such as joins, unions, and aggregations are performed. In these cases, you cannot sort by the source row information. You may be able to generate a column of source row number earlier in your recipe.

sort order:SOURCEROWNUMBER()

Then, you can create the header with the following simple step:

header sourcerownumber:1


Results:

After you have applied the last header transform, your data should look like the following:

(rowId)RacerHeat_1Heat_2Heat_3
3Racer Y41.33DQ38.04
2Racer X37.2238.2237.61
4Racer Z39.2739.0438.85

You can sort by the Racer column in ascending order to return to the original sort order.

Example - Using sourcerownumber to create unique row identifiers

The following example demonstrates how to unpack nested data. As part of this example, the SOURCEROWNUMBER function is used as part of a method to create unique row identifiers.

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]

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:

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:

derive type:single value: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]]

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 delete 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 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

Example - Delete rows based on source row numbers

Source:

Your dataset is the following set of orders.

CustIdFirstNameLastNameCityStateLastOrder
1001SkipJonesSan FranciscoCA25
1002AdamAllenOaklandCA1099
1003DavidWigginsOaklandMI125.25
1004AmandaGreenDetroitMI452.5
1005ColonelMustardLos AngelesCA950
1006PaulineHallSagninawMI432.22
1007SarahMillerCheyenneWY724.22
1008TeddySmithJuneauAK852.11
1009JoelleHigginsSacramentoCA100


Transform:

Initially, you want to review your list of orders by last name.

sort order:LastName

During your review, you notice that two customer orders are no longer valid and need to be removed. They are:

  • LastName: Hall
  • LastName: Jones

You might hover over the left side of the screen to reveal the row numbers. You select the row numbers for each of these rows, and a delete suggestion is provided for you. When you click Modify, you see the following transform:

 

delete row: IN(SOURCEROWNUMBER(), [2,7])

The above checks the results of the SOURCEROWNUMBER function, which returns the original row order for the selected rows. If a selected row matches values in the [2,7] array of row numbers, then the row is deleted.

Results:

When the preceding transform is added, your dataset looks like the following, and your sort order is maintained:

Source:

CustIdFirstNameLastNameCityStateLastOrder
1002AdamAllenOaklandCA1099
1004AmandaGreenDetroitMI452.5
1009JoelleHigginsSacramentoCA100
1007SarahMillerCheyenneWY724.22
1005ColonelMustardLos AngelesCA950
1008TeddySmithJuneauAK852.11
1003DavidWigginsOaklandMI125.25

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