Looker Explore Guide
An Explore has seven sections:
Explore Name: It shows the name of the current explore you are accessing to.
Search Box: You can type and search the fields you want from here. It is very helpful when there are many views and a long list of fields in the Explore.
Field Picker: Fields are grouped by views. From here you can select the fields (dimensions / measures) to query the data.
All Fields: It includes all views and fields.
In Use: It only shows the fields that you’ve selected.
Filters Pane: It is where the filters are set up and edited. Dimensions and measures can both be added as filters to help you narrow down the result. There are three types of filters in Looker and you can use any combination of these filters in your query. Refer to Filtering and Limiting Data for more info.
A. Basic Filters
Filters are added by clicking on the filter icon next to the field in the Field Picker.
Standard filter options vary by field type. For example, a time dimension lets you select a time range.
A numeric measure provides you with options such as equal to or greater than.
For text dimensions, Looker displays a list of existing data values for the field. As you type, Looker narrows the list to values that include that text.
All the filter options are pretty intuitive by name, except for the “matches” options, which are relatively “advanced” options. The standard filters will meet the basic needs most of the time.
B. Matches A User Attribute
Looker admins can configure user attributes that specify user-specific values. For example, an admin can define a user attribute for a sales region and assign the appropriate values to individual users or groups of users.
User attributes let you automatically customize a Look or dashboard for each user that views it. The Matches a user attribute provides this user-specific flexibility. For example, you can filter a sales region dimension in a Look to equal a sales region user attribute. The Look will filter for the user's specific sales region and automatically adjust to show each user the data for their own sales region.
C. Advanced Matches
Another way to filter queries is using Looker Filter Expression. In this way, there is more flexibility and it is more close to natural language. To use this filter, in the text field as shown below, enter your expression.
To enter a special character in an advanced matches filter, first add a leading carat (^). For example, to filter on Santa Cruz, CA, you would enter Santa Cruz ^, CA.
Your Looker admin can configure user-specific values called user attributes that let you automatically customize a Look for each user. To reference a user attribute in an advanced matches filter, use the syntax {{ _user_attributes['name_of_your_attribute'] }}.
D. Custom Filters
Custom filters let you write the fields, constants, functions, and operators for your desired filtering. Looker lets you build an expression that evaluates as true or false. When you run the query, Looker only returns rows for which that condition is true.
To enable custom filters, check the box on the upper right corner of the filter pane, as shown below. You can have a combination of fields, constants, functions and operators to build the filter you want. For example, the custom filter below will return all data with a start date on and before the same day last week in 2023. For more info about how to write the expression, refer to Creating Looker Expressions.
Run Button
After you configure the fields and filters, click the Run button and Looker will automatically generate a SQL query and fetch the result.
Visualization Pane
The result will be shown in both the Visualization Pane and Data Pane. In the Visualization Pane, you can choose the most effective visualization as you want. You can either quickly switch between table, column chart, bar chart, scatter plot, line graph, area graph, pie chart, maps, and single value using the icon in the red rectangle as shown below, or click on the three dots and get more visualization options.
Click Edit and you can configure the visualization option settings, such as naming and arranging chart axes, choosing the position and type of each data series, or modifying the chart color palette and so on.
For more info about creating visualizations in Looker, please refer to Creating Visualizations and Graphs.
Data Pane
Looker will also retrieve result into the Data Pane and show it as row and column format.
A. Data Limit
Looker supports up to 5,000 rows and an unlimited number of columns for un-pivoted queries. For browser performance, 50 or fewer columns is recommended. Looker supports up to 200 columns for pivoted queries, but sets a default column limit of 50 columns.
B. Pivot Data
Pivoting data is need sometimes for specific visualizations such as stacked column chart, multiple donut chart and so on.
You can pivot data by clicking on the “pivot data” icon next to the field and then it will pivot the the field to be shown as columns instead of rows. For example, the screenshot below shows that the Request Status are pivoted and each status is shown as a column in the data pane.
C. Totals
Column totals and row totals can be turned on by checking the respective boxes on the upper right corner of the data pane.
Explore Name: It shows the name of the current explore you are accessing to.
Search Box: You can type and search the fields you want from here. It is very helpful when there are many views and a long list of fields in the Explore.
Field Picker: Fields are grouped by views. From here you can select the fields (dimensions / measures) to query the data.
All Fields: It includes all views and fields.
In Use: It only shows the fields that you’ve selected.
Filters Pane: It is where the filters are set up and edited. Dimensions and measures can both be added as filters to help you narrow down the result. There are three types of filters in Looker and you can use any combination of these filters in your query. Refer to Filtering and Limiting Data for more info.
A. Basic Filters
Filters are added by clicking on the filter icon next to the field in the Field Picker.
Standard filter options vary by field type. For example, a time dimension lets you select a time range.
A numeric measure provides you with options such as equal to or greater than.
For text dimensions, Looker displays a list of existing data values for the field. As you type, Looker narrows the list to values that include that text.
All the filter options are pretty intuitive by name, except for the “matches” options, which are relatively “advanced” options. The standard filters will meet the basic needs most of the time.
B. Matches A User Attribute
Looker admins can configure user attributes that specify user-specific values. For example, an admin can define a user attribute for a sales region and assign the appropriate values to individual users or groups of users.
User attributes let you automatically customize a Look or dashboard for each user that views it. The Matches a user attribute provides this user-specific flexibility. For example, you can filter a sales region dimension in a Look to equal a sales region user attribute. The Look will filter for the user's specific sales region and automatically adjust to show each user the data for their own sales region.
C. Advanced Matches
Another way to filter queries is using Looker Filter Expression. In this way, there is more flexibility and it is more close to natural language. To use this filter, in the text field as shown below, enter your expression.
To enter a special character in an advanced matches filter, first add a leading carat (^). For example, to filter on Santa Cruz, CA, you would enter Santa Cruz ^, CA.
Your Looker admin can configure user-specific values called user attributes that let you automatically customize a Look for each user. To reference a user attribute in an advanced matches filter, use the syntax {{ _user_attributes['name_of_your_attribute'] }}.
D. Custom Filters
Custom filters let you write the fields, constants, functions, and operators for your desired filtering. Looker lets you build an expression that evaluates as true or false. When you run the query, Looker only returns rows for which that condition is true.
To enable custom filters, check the box on the upper right corner of the filter pane, as shown below. You can have a combination of fields, constants, functions and operators to build the filter you want. For example, the custom filter below will return all data with a start date on and before the same day last week in 2023. For more info about how to write the expression, refer to Creating Looker Expressions.
Run Button
After you configure the fields and filters, click the Run button and Looker will automatically generate a SQL query and fetch the result.
Visualization Pane
The result will be shown in both the Visualization Pane and Data Pane. In the Visualization Pane, you can choose the most effective visualization as you want. You can either quickly switch between table, column chart, bar chart, scatter plot, line graph, area graph, pie chart, maps, and single value using the icon in the red rectangle as shown below, or click on the three dots and get more visualization options.
Click Edit and you can configure the visualization option settings, such as naming and arranging chart axes, choosing the position and type of each data series, or modifying the chart color palette and so on.
For more info about creating visualizations in Looker, please refer to Creating Visualizations and Graphs.
Data Pane
Looker will also retrieve result into the Data Pane and show it as row and column format.
A. Data Limit
Looker supports up to 5,000 rows and an unlimited number of columns for un-pivoted queries. For browser performance, 50 or fewer columns is recommended. Looker supports up to 200 columns for pivoted queries, but sets a default column limit of 50 columns.
B. Pivot Data
Pivoting data is need sometimes for specific visualizations such as stacked column chart, multiple donut chart and so on.
You can pivot data by clicking on the “pivot data” icon next to the field and then it will pivot the the field to be shown as columns instead of rows. For example, the screenshot below shows that the Request Status are pivoted and each status is shown as a column in the data pane.
C. Totals
Column totals and row totals can be turned on by checking the respective boxes on the upper right corner of the data pane.
Updated on: 21/03/2023
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