Person: LookML provides advanced logic options that you can use when you define dimensions and measures. Let's review some of these options so that you can further customize dimensions and measures to curate data experiences for your business users. As you may know, in LookML you can use the syntax, ${fieldname} to reference existing dimensions and measures within the current view file. Additionally, you can also reference dimensions and measures from other view files by fully identifying them just as you would in SQL. To do this, you simply write the view name and a period before the field name, all within curly braces following a dollar sign, such as ${inventory_items.cost}. Keep in mind though, that in order for this syntax to work correctly the two views involved need to be joined together in an Explore. In this example, the current view containing the sale_price dimension would need to be joined with the inventory_items view in an Explore. Now, just as you can reference existing dimensions when creating new measures, you can also reference previously modeled measures when defining a new measure. For example, your new measure can include multiple measures interacting with each other, such as calculating a percentage of users located in a specific country out of the total number of users. Please remember, though, that when you want to include other measures in the SQL parameter of a new measure, you need to use the measure type called "number." Last, a useful parameter for defining measures is the filters parameter, which you can use to create measures based on specific dimension values. In this example, the filter is based on the value "USA" and the country dimension. The count is completed using the primary key of the view, which in this case is user ID for the users view. The resulting measure is a count of user IDs with a country value equal to USA. Take note, though, that although the filters parameter does not use the familiar substitution syntax of ${fieldname}, the field names actually still refer to existing LookML dimensions, not literal database column names. However, in the second example, the filter is based on an existing yesno dimension name, "is_new_user". Only rows that return a value of yes for this dimension are summed across the sales price dimension for a summary of sales to only new users. Both of these examples would generate a case statement within the aggregate function of the SQL query. In summary, LookML provides several options for advanced logic when defining custom dimensions and measures, as demonstrated through these examples, you can reference fields in other views, use measures to define other measures, and use dimensions to filter measures. You should find these advanced logic options useful as you gain more experience you curating dimensions and measures for your business users.
