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Exploring data in Looker

update

Update to Looker 21.14 for the ability to create ad hoc custom bins for numeric type dimensions with the Custom Fields Labs feature without needing to use logical functions in Looker expressions or needing to develop type: tier LookML fields.

update

Update to Looker version 21.14 for the ability to quickly forecast data in new or existing Explore queries to help users predict and monitor specific data points.

update

Update to Looker 21.12 for the ability to create ad hoc custom groups for dimensions with the Custom Fields Labs feature without needing to use logical functions in Looker expressions or needing to develop CASE WHEN logic in sql parameters or type: case fields and, additionally, for the ability to use Quick Calculations to perform common calculations on numeric fields that are in an Explore’s data table without needing to use Looker functions and operators.

This page introduces you to data exploration with Looker. After you read this page, you should understand where to start pulling data in Looker, how to modify a report to see more detail, and how to drill down to gain deeper insights.

Explores are the starting point for exploration

For this example, let’s imagine we operate an e-commerce store. The Explore page presents a number of Explores for looking at our e-commerce store data. An Explore is a starting point for a query, designed to explore a particular subject area. To access the Explore page, use the Explore menu to select an Explore:

In our e-commerce store model we have Explores for Order Items (the products associated with an order), Orders (purchase events), Products, and Users (individuals associated with purchase events). For example, when we have questions about items associated with an order, we probably want to start exploring from the Order Items Explore.

The data shown in an Explore is determined by the dimensions and measures you select from the field picker at the left. A dimension can be thought of as a group or bucket of data. A measure is information about that bucket of data. In Looker, dimensions appear as blue columns and measures appear as orange columns in your data table.

The query shown below displays the number of orders per day by querying the Order Items Explore and displaying one dimension (ORDERS Created Date) and one measure (ORDERS Count).

In this case, all order records have been grouped together by day (the dimension). Then we’ve asked for a count of orders (the measure) for each of those days.

If an Explore contains modeled queries, you can use Quick Start analyses to populate fields. The next section provides an in-depth overview of Quick Start analyses and how to use them as a starting point for exploring data.

Quick Start analyses

Starting in Looker 7.20, the Upgraded Explore Field Picker Labs feature is enabled by default, and modeled queries will automatically display as Quick Start analyses in blank Explores.

Modeled queries are available to users as Quick Start analysis options in Explores. Quick Start analyses provide a helpful starting point for users to quickly run and build analyses:

Each Quick Start analysis option will display the name of the analysis and, when available, a description.

For more information on how developers can model prebuilt analyses for users, see the query parameter documentation page.

Choosing a Quick Start option from a blank Explore

To run a Quick Start analysis, click the analysis that you want to see. The analysis will automatically run and display results, including the visualization.

You can modify a Quick Start analysis once it has run by adding or removing fields from the All Fields tab, from Search results, or from the In Use tab in the field picker.

Choosing a Quick Start option once an Explore has run

Once an Explore has finished running, you can select a new Quick Start analysis by clicking the lightning bolt icon next to the Explore name:

This launches the Quick Start menu:

Selecting a Quick Start analysis from the menu runs the analysis and replaces all the previous Explore results except the existing filters.

Quick Start filter behavior

Filters are additive. This means that Quick Start analyses will include any existing Explore filters when run. If a selected Quick Start analysis has a filter value that conflicts with an existing Explore filter, the user will be prompted to select which filter value to use in the analysis.

In the example below, an Explore includes the filters Orders Created Date is in the year “2019”, Orders Status is equal to “complete”, and Users State is equal to “Washington”:

The user clicks the lightning bolt icon to launch the Quick Start modal and selects a new Quick Start analysis to run:

The selected Quick Start analysis has a conflicting filter value for the Users State filter, and the user is prompted to resolve the conflict:

To resolve the conflict:

  1. Choose an option.
    • Select Keep current filters to run the new analysis with the existing filter value (Users State is equal to “Washington” in this case).
    • Select Replace with new filters to run the new analysis with its prebuilt filter condition (Users State is equal to “California” in this case).
  2. Click Apply to confirm the selection and run the analysis.

The Explore runs with the updated Users State is equal to “California” filter condition, and includes any existing non-conflicting filters (Orders Created Date is in the year “2019” and Orders Status is equal to “complete”):

Adding more dimensions for more detail

Whether you added fields to your Explore manually or by selecting a Quick Start option, you can add more dimensions to learn more about your data.

To add a field:

  1. Click a field from the field picker to add it to the query.
  2. Click Run to re-run the query.

Adding the Status dimension splits the counts between order statuses, and displays how many orders are complete, pending, or canceled.

Sorting data

update

Update to Looker 21.6 for the ability to more easily determine the sort order of data tables in Explores.

Data on the Explore page is sorted by default according to the following prioritization:

  1. The first date dimension, descending
  2. If no date dimension exists, the first measure, descending
  3. If no measure exists, the first added dimension, ascending

You may want to sort data differently than the default order.

For example, let’s see which date has the most orders from returning customers (in other words, customers not making first purchases). Clicking on the Orders Order Count column header will sort from highest to lowest.

You can sort by multiple columns by holding down the Shift key and then clicking on the column headers in the order that you want them sorted.

Note that if you reach a row limit, you will not be able to sort row totals or table calculations.

Pivoting dimensions

Multiple dimensions are often easier to look at when you pivot one of the dimensions horizontally. Each value in the dimension will become a column in your Look. This makes the information easier to consume visually, and reduces the need to scroll down to find data. Looker supports up to 200 pivoted values.

To pivot Explore results by a dimension:

  1. Click Pivot for that dimension.
  2. Click Run to re-run the query.
  3. To unpivot results, click the field’s gear icon and select the Unpivot option.

Pivots and nulls

A row of data whose value would not appear in a column is indicated with the null value symbol, a zero with a slash across it. For example, on July 23rd we shipped one accessory but no active wear or blazers:

Pivots and sorting

You can also sort pivoted dimensions by clicking the title of the dimension. To sort by multiple pivoted dimensions, hold down the Shift key and then click on the dimension titles in the order that you want them sorted. When you’re sorting a pivoted measure, any rows with values in that column are sorted first, followed by rows without data in that column (indicated by the null value symbol).

Reordering columns

You can reorder columns in the Data section by clicking on a column header and moving the column to its desired position. The Explore’s visualization will reflect the new column order after you click Run.

Columns are organized in the Data section by field type:

  1. Dimensions
  2. Dimension table calculations
  3. Measures
  4. Measure table calculations
  5. Row totals

For the most part, columns can be reordered within each field type but cannot be moved out of their field type section.

For example, dimension table calculations can be rearranged among themselves, but you cannot place a dimension table calculation in between two measures.

One exception, however, is that you can use the arrow next to the row totals checkbox on the Data tab to move the row total column from the far right of the data table to just after the dimension table calculations.

Columns under a pivoted dimension can be reordered, but the order of pivoted dimensions can be changed only by changing the sort order, not by manual reordering.

Removing fields

To remove a field from an Explore:

  1. Click the selected field in the field picker or click Remove from the column’s gear menu.
  2. Click Run to re-run the query.

You can also remove all fields in an Explore using the keyboard shortcuts Command-K (Mac) or Ctrl+K (Windows).

Upgraded Explore field picker

update

Update to Looker 21.12 for the upgraded Explore field picker to be the default unless an admin has enabled the legacy field picker.

update

Update to Looker 21.6 for the ability to filter and more quickly navigate the fields in the field picker by entering a search term in the search bar.

update

Update to Looker 21.10 for the ability to perform more general field searches in the field picker using search modifiers in the search bar.

Starting in Looker 7.20, the Upgraded Explore Field Picker Labs feature is enabled by default.

When the Upgraded Explore Field Picker feature is enabled, Explores will display an upgraded field picker with a smarter search capability, easy view and management of selected fields, Quick Start analyses for Explores with modeled queries, and user experience improvements.

The new field picker includes the following elements:

  1. Explore name — Displays the name of the current Explore. A lightning bolt icon will also appear for Explores that have modeled queries, allowing users to access Quick Start analysis options after an Explore has run.
  2. Search bar — Displays the search bar.
  3. All Fields tab — Displays all available fields for an Explore.
  4. In Use tab — Displays all Explore fields that are currently in use.
  5. View level summary — Displays the total number of selected fields from a view. This number is shown when the view is collapsed, and when it is expanded.
  6. Field-specific information and actions — Displays a field’s current and potential functions in an Explore, as well as more details about a field.

  7. Explore summary — Displays the total number of fields in an Explore (including custom fields and table calculations when permissions allow) in the bottom left corner, and the Go to LookML link in the bottom right. Go to LookML directs users to the explore definition in its LookML project. This link is visible only to users with the see_lookml permission.

Field-specific information and actions

The icons next to each field provide more information about the field and indicate the options available for that field. The icons are visible when a field is active in an Explore, or when you hover a cursor over a field that is not active in an Explore:

These icons appear on the All Fields and In Use tabs, and in the results of a search.

Click an icon to filter or pivot by the field, to provide more field information, or — when permissions allow — to create a custom field that is based on the field:

  1. Pivot icon — Click this icon to pivot or unpivot a field in an Explore. This icon will appear gray when a field is not pivoted and blue when a field is pivoted.
  2. Filter icon — Click this icon to add a field as an Explore filter or to remove a field as a filter. This icon will appear gray when a field is not a filter and blue when it is an active filter.

  1. Information icon — Click this icon to see more detail about a field:
    • All users will see the field’s data type, description (when available), and LookML field name (in view_name.field_name syntax):
    • Users with the see_lookml permission will see the definition of the LookML field’s sql parameter, as well as an option to navigate to that field in the LookML project:

  1. Three-dot Options menu — The three-dot Options menu is available only if the Custom Fields Labs feature is enabled, and to users with the create_table_calculations permission. Users with the create_table_calculations permission can click the three-dot Options menu to quickly create custom fields depending on a field’s type. For example, the three-dot Options menu below shows the custom field options available for the Profit dimension, which is a number data type:

All Fields tab

When you open an existing Explore, the All Fields tab is displayed by default. This tab is the starting place for building an Explore and displays all the available fields that you can select for a query. Similarly to the classic Explore field picker, fields are organized alphanumerically by type (dimensions, followed by measures) under the name of the view or view label in which they are defined. Each field will show field-specific information and actions, such as a field’s current and potential functions in an Explore:

Selected fields will appear highlighted in gray, and corresponding field icons (pivot, filter) will appear blue when active. For example, the field Profit in the above field picker is highlighted in gray, indicting that it is selected. You can tell that this field is not pivoted or filtered because all corresponding field icons are gray.

Click a field from the All Fields tab to add it to or remove it from an Explore query. Additionally, you can click the appropriate icon to filter, pivot, or perform other field-specific actions from the All Fields tab.

If the Custom Fields Labs feature is enabled, custom fields and table calculations are listed under Custom Fields; and users with the create_table_calculations permission can create, select, and view these custom fields and table calculations by clicking the blue Add button or by choosing a custom field option from a field’s three-dot Options menu. Users must have the create_table_calculations permission to see the Custom Fields view label or the three-dot Options menu.

In Use tab

The In Use Tab shows all fields that are currently active in an Explore, organized alphanumerically by view or view label, and whether they are dimensions or measures:

The In Use tab also displays an updated Explore summary at the bottom of the tab. The bottom left corner displays the total number of active fields in an Explore. A Go to LookML link is available in the bottom right to users with the see_lookml permission. Go to LookML directs users to the explore definition in its LookML project. The example above shows that there are currently four total active fields in the Explore.

Removing fields from the In Use tab

When a field is in use, you can remove it from an Explore by clicking the field’s name.

You can also remove all selected fields (including custom fields and table calculations) by clicking Clear all, or you can remove all fields (including custom fields and table calculations), except those that are active filters, by clicking Clear fields, keep filters. Neither of these options will remove Custom filters; to remove a custom filter you need to manually click the checkbox next to the filter.

Alternatively, you can choose to filter, pivot, or perform other field-specific actions from the In Use tab by clicking the appropriate field icon.

In Use tab field-specific icons and actions

The icons next to each field indicate the field’s current and potential functions in an Explore query. For example, the field Created Year is currently filtered, as indicated by the active blue Filters icon.

If the Custom Fields Labs feature is enabled, custom fields and table calculations are listed under Custom Fields when used in an Explore; and users with the create_table_calculations permission can create, select, and view these by clicking the blue Add button or by choosing a custom field option from a field’s three-dot Options menu. Users must have the create_table_calculations permission to see the Custom Fields view label or the three-dot Options menu.

When a dimension group is active in an Explore’s data table, users can use the three-dot Options menu to replace a selected timeframe with another, if available, without having to manually deselect one field and select another field:

When you select a new timeframe from the Switch To list, the Explore automatically reruns with updated results. When you’re using the Switch To function, only timeframes in the Explore data table, not filtered timeframes, will be replaced.

Search bar

The new field picker search bar returns results weighted by popularity and by whether field names or descriptions match a search string.

Clicking in a blank search bar displays a list of Popular Fields to choose from. Popular Fields are fields that are most commonly selected by users in an Explore and can be helpful when you’re creating an Explore from scratch:

To perform a search:

  1. Enter a search term in the search bar. The search term can be a full or partial field, view, or label name.
  2. The search results display the information, underlining search term matches for each matching field, including:
  3. Hovering over a field in the search results reveals icons that indicate the field’s current and potential Explore functions. When a field is currently active in one of the ways shown here, the corresponding icon will appear blue. The icon will appear gray when the field is inactive. For example, the selected field in the search results above, Profit, is inactive in the Explore — for this reason, all three icons appear gray.
    • Clicking the + icon adds a field to the Explore results table. If a field is already in an Explore results table, the gray inactive + will be replaced by an active blue x. Click the active blue x to remove the field from the Explore.
    • Clicking the double-arrow icon adds a field to an Explore table as a pivot. If a field is currently pivoted, the field can be unpivoted by clicking the active blue double-arrow icon, or by clicking the active blue x icon to remove the field from the Explore table entirely.
    • Clicking the funnel icon adds a field as an Explore filter. If a field is an active filter, you can remove it by clicking the active blue Filters icon.

Active Explore fields that are surfaced in search results are marked with an inline blue dot to the left of the field name. For example, in the previous screenshot, the field ID is currently active in the Explore, as indicated by the inline blue dot. Remove active Explore fields by hovering over the field name and clicking the active blue x.

The search function empowers users to quickly select the specific fields they need to build Explores. There are two ways to select fields from a search:

  1. By selecting a field from the Popular Fields drop-down
  2. By entering a search term in the search bar

  3. Displaying totals

    Sometimes a summary of your data is useful. You can add column totals to your report by clicking the Totals checkbox in the upper right and then running the report:

    If your report contains more than one dimension, you can choose to include Subtotals in your table visualization:

    You can also add row totals to your report, but only if you’ve added a pivot to your report:

    If you’ve added row totals, and your query exceeds any row limit that you’ve set, you will not be able to sort the Row Totals column (although you can sort dimension and measure columns as normal). This is because you might be missing rows in your data that should be included in your totals. If you run into this issue, you can try increasing your row limit (up to 5,000 rows).

    When totals aren’t available

    There are some cases when totals won’t be available:

    • Column totals are available only for measures and table calculations that exclusively reference measures, not for dimensions or table calculations that reference dimensions.
    • Row totals are available only for measures, not for table calculations that are based on dimensions or dimensions.
    • Certain types of columns won’t be totaled, because of database limitations or because the value would not make sense as a total. For example, you can’t add together a list of words.

    Things to consider with totals

    Additionally, there are some things to keep in mind about how totals work in certain situations:

    • Columns that count unique items might not add up as you expect, since the same item might show up in several categories but be counted as only one unique item in the totals.
    • Some table calculations that perform aggregations, such as calculations using percentile or median, might not add up as you expect. This is because table calculations calculate totals using the values in the total row, not using the values in the data column.
    • If you’ve filtered your report by a measure, totals may appear to be too high. However, in actuality, what you’re seeing is a total for your data before the measure filter is applied. In other words, the measure filter may be hiding some data from your report, even though that data is included in the total.
    • If you’ve used totals with merged results, Looker calculates totals on each of the component queries and uses those totals in the merged result. Therefore, totals may appear too high, because what you are seeing are totals calculated before the results were merged. One way to avoid this is to align the filters on each query.
    • Similarly, if you’ve placed row or column limits on your report, and your report exceeds that limit, totals may also appear to be too high. However, what you’re seeing is a total for your data before the limits are applied. In other words, the limits may be hiding some data from your report, even though that data is included in the total.

    In the situations described in the third and fourth bullets, above, it is possible to calculate totals only for the data you can see. To do so, you’ll need to use a table calculation, explained later on this page. For a column total, use sum(${view_name.field_name}). For a row total, use sum(pivot_row(${view_name.field_name})).

    For information about displaying subtotals in table visualizations, see the Table chart options documentation page.

    Drilling down into the data

    Every query result is the starting point for another query. Clicking on any data point will drill down, creating another query refined by the data point you clicked. In the example below, we see that December 21, 2019 has had 39 orders. Clicking on the count of 39 takes us to details about those specific records.

    Drilling deeper …

    In the drill overlay, we can see all the orders placed on August 2, 2017. From here, we can:

    • Click the Explore from Here button to open an Explore that uses the fields in the drill overlay as a starting point.
    • Click the Download Results button to download the data using the same options as shown on the Downloading content documentation page.
    • Click on the drillable Order Items field for an individual customer, William Durocher, to see all the items in their order.

    And deeper still …

    If we click on the Order Items field for William Durocher, we see a list of all the order items in William Durocher’s order.

    Of course, this isn’t the end of the road. Like any query in Looker, the results are linked so you can keep drilling, exploring, and arriving at new insights.

    Drilling into dashboards

    If your Looker admin has enabled the Dashboards in Drill Menus Labs feature, dashboards will appear in the drill menu if they have a filter on the field you are drilling into.

    For example, the State field in this Explore is used as a filter for one dashboard:

    When you click on a dashboard in the drill menu, Looker displays that dashboard while applying the value you chose as the filter value for the dashboard:

    For more information about how filters on dashboards are assigned to fields, see the Adding and editing dashboard filters documentation page.

    Copying values

    Looker makes it easy to copy all the data from a table column. To do so, hover over a column label, click the gear icon, and then choose Copy Values:

    This data can then be pasted into a document or a tool like Excel.

    Using links and actions

    The presence of either links or actions is indicated by an ellipsis (…) following the data in a field.

    Using links

    Your Looker developers may have added clickable links to your data:

    When you click on the data in the field, Looker provides an option to open the destination of the link. In the example above, the developers added a link to the Brand column. When you click on a brand listing, Looker provides an option to perform a Google search for that brand name.

    Using data actions

    Your Looker developers may have added data actions to the dimensions or measures in your data. With data actions, you can perform tasks with other tools directly from Looker, such as sending an email or setting values in other applications. These data actions appear in the drill menu under an Actions heading:

    In the example above, the Phone field has a link to the Twilio service. When you click the phone number and select the Twilio action, Twilio prompts you to enter a message. Twilio then sends that message to the phone number.

    Features for developers

    Depending on your permissions, you may see several features designed for Looker developers:

    1. The Looker logo next to the Explore lets Looker developers see the Explore LookML.
    2. The gear drop-down menu next to each field in the field picker provides a Go to LookML link that lets Looker developers see the field’s LookML.
    3. The SQL tab in the Data section lets Looker developers see the query that Looker sends to the database to get the data.

    The Get LookML option in the Explore gear menu allows developers to copy LookML for the Explore’s query that can then be used to add a tile to LookML dashboards, to improve query performance with aggregate tables, or to define native derived tables.

    Conclusion

    Now that you know how powerful the Looker Explore page is for building queries, displaying results, and discovering insights through iterative searches, you might want to limit your results to just the data you’re interested in.

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