Mastering Number Conversions in Looker: Custom Dimensions vs. Casting

In the realm of data analysis, Looker shines as a dynamic tool that empowers businesses to glean insights from raw data. However, the intricate interplay between strings and numbers often presents challenges that require creative solutions. In this article, we will explore two robust techniques to tackle this challenge head-on: custom dimensions and casting. By diving into both approaches, we aim to equip you with the tools you need to seamlessly convert strings into numbers within Looker’s Explore, enabling you to extract maximum value from your data.

The String and Number Conundrum

Merging different data types is a common scenario in data analysis. However, when it comes to calculations, aggregations, and comparisons, the mix of strings and numbers can lead to errors and unexpected results. Looker’s calculation engine thrives on homogenous data types, making this situation a roadblock for meaningful analysis.

Solution 1: Custom Dimensions for Customized Transformations

Custom dimensions within Looker offer a versatile solution to convert strings into numbers. These dimensions allow users to define their own fields with tailored calculations. Let’s explore this method:

Step 1: Identify the Data

Identify the field containing the string data you wish to convert into numbers.

Step 2: Create a Custom Dimension

In your LookML model, navigate to the relevant explore and create a custom dimension. Give it a descriptive name to reflect the transformation.

Step 3: Write the Conversion Logic

Utilize LookML scripting to define the conversion logic. For example:

LookML:

dimension: converted_number {
 type: number
 sql: ${STRING_FIELD}::float ;;
}        

Step 4: Incorporate the Custom Dimension

Use the new custom dimension in your visualization, allowing you to seamlessly integrate the converted number into calculations.

Benefits of Custom Dimensions

1. Flexibility:?Custom dimensions empower users to perform complex transformations, making converting strings to numbers a breeze.

2.?Data Integrity:?Custom dimensions maintain data integrity, preventing errors from mixing string and number data.

3.?Reusability:?Once created, custom dimensions can be used across various Explores, dashboards, and visualizations, saving time and effort.

Solution 2: The Elegance of Casting

Casting data from one type to another offers a powerful solution to the string-to-number challenge. Casting involves explicitly converting data types, and it can be particularly useful in Looker.

Step 1: Define the Casting Dimension

Create a dimension that uses the `CAST` function to convert the desired string field into a number:

LookML:

dimension: star_to_num {
 type: number
 sql: CAST(${TABLE}.stream_id AS INTEGER)
}        

Benefits of Casting

1. Precision:?Casting allows for accurate conversions tailored to your needs.

2. Efficiency:?Casting is a lightweight operation that maintains performance.

3. Simplicity:?The syntax is simple, making it accessible to users of varying skill levels.

Choosing the Right Approach

Both approaches have their merits. Custom dimensions offer flexibility, while casting provides simplicity and efficiency. The choice depends on your analysis context, conversion complexity, and familiarity with LookML scripting.

Conclusion

When navigating the intricacies of data analysis, the ability to convert strings into numbers is a crucial skill. Looker empowers users with two effective techniques: custom dimensions and casting. By embracing these approaches, you transform your data into a strategic asset, unlocking insights that drive informed decision-making. Whether you’re a seasoned Looker user or just beginning your journey, these techniques will elevate your data analysis game, enabling you to derive more value from your data than ever before.