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Here’s the SQL query that handles that. This CTE is aliased as data and has been referred to as the “unioned output” throughout this article.

To fix the misspelling of “Sciece” and “Engish”, I used two CASE statements and the wildcard character % which looks for every character in a string. In this case, I want to look for every string in the original Subject field that starts with “S” and “E” respectively. Once those strings have been found, I replace them with the correct spellings of “Science” and “English”.

Moving on, the below query and output shows us the min_averages CTE, which results in only one row with the correct values that we’re looking for. The reason why this challenge was difficult for me was because, at one point, I tried nesting two aggregate functions. It looked something like this MIN(AVG(english)) AS “min_avg_english_score” — but this is completely wrong and would return an error. The data of the Original_Joining Date represents the first clause of the REGEX_REPLACE function. In other words, that field is the original concatenation that needs to be modified further. This is what I was referring to earlier about how the Joining Day field has to be zero-padded. The second clause, which is the RegEx string pattern of ‘ Subject” is the name of the field that contains aliased values for each of the scores. So, while the actual value of MIN(“avg_english_score”) is 68.2, its aliased value is “lowest_average_english score”.

The next two fields, Attendance Percentage and TestScoreInteger, involved using the ROUND function. However, as you can see in the desired output, these two fields are two different types of numbers. Attendance Percentage is a decimal while TestScoreInteger is an integer. When using the ROUND function, it matters what the original number format is for the affected field. To see why, take a look at the supplemental query and output below. It showcases why configuring the ROUND function in an appropriate way matters.In other words, it’s crucial to be careful with how thresholds are defined if we want to convey our data accurately and meaningfully. We can get totally different results if we get confused about how we use comparison operators! One last thing that’s worth mentioning is the need for the WHERE statement. What it’s doing is limiting the result to only show the rows where grade values (which are lowest average scores across the three subjects) are equal to the average scores for the three subjects. To illustrate what this logic is actually doing, let’s take a look at a supplemental output which is returned when we remove the WHERE statement.

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