SOURCEROWNUMBER Function

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:

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:

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:

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:

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

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

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:

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:

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: