Working with filters

What filters are and how they behave

A filter is a control element that limits the data shown in dashboard components (such as charts and tables).

When a filter is applied:

• It affects all connected charts in the dashboard at once

• It updates results dynamically based on the selected value(s)

• It only works if the underlying data supports the filter field

Example

A dashboard shows property listings across Europe.

• Filter: “City”

• Selected value: “Lisbon”

Result:

• All charts and lists update to show only properties located in Lisbon

Key behavior to understand

• Filters do not change the data source itself, only the visible results

• Multiple filters can be combined (e.g. city + year)

• Filters are usually applied instantly after selection

Adding filters to a dashboard

Filters are added at the dashboard level and can then be connected to one or more datasets.

Steps

1. Open your dashboard in edit mode

2. Add a new filter component

3. Enter a title for the filter

4. Add the Google Sheets URL in the Data Source URL field

5. Click the Sync button to load the data

6. Select the column (control field) that should be used for filtering

7. Choose the filter type:

   • Single select

   • Multi select

Configuring filter options

Each filter type includes configuration settings that define how the filter behaves and appears.

Common settings (all filter types)

Placeholder

Defines the text shown when no value is selected

• Custom text can be entered

• If left empty, the default text “Please select” is used

Preselected options

Allows you to define default selected values so the dashboard is already filtered when users open the page.

Important

• The value must exactly match the value in the data source

• This includes spelling, spacing, and full naming

Example

• Data source value: “Frankfurt am Main”

• Preselected option: “Frankfurt”

Result:

• The filter will not be applied because the values do not match

Best practices

• Always copy the value directly from the data source when possible

• Avoid abbreviations or shortened versions of values

• Double-check for formatting differences (e.g. spaces, capitalization)

Multi select filter settings

Multi select filters allow users to choose multiple values at once.

Additional option:

Max. limit

Limits how many values users can select in a multi select filter.

• Default value: 10

• If left empty, users can select an unlimited number of options

How to use it correctly

• Adjust the value based on the number of available options

• Do not set a limit higher than the actual number of options

Example

• Filter has 5 cities available

• Max. limit is set to 10

Result:

• The limit has no practical effect

Why it matters

• Helps prevent charts from becoming too crowded

• Improves readability when working with large datasets (e.g. many cities)

Best practices

• Use a limit when the filter contains many options

• Keep the number low enough to maintain clear visualizations

• Leave the field empty only if unlimited selection is intentional

Single select filter settings

Single select filters allow only one value to be selected.

Typical use case

• Selecting a single year (e.g. “2024”)

• Choosing one property type at a time

Best practices

• Use single select when only one value should be active at a time

• Use multi select for broader exploration (e.g. multiple cities)

• Always define a clear and user-friendly placeholder

• Consider using preselected options to guide users to meaningful default views

Linking filters to datasets

For a filter to have an effect, it must be linked to one or more datasets used in the dashboard.

How linking works

• Each filter references a specific field (e.g. “city”)

• The same field must exist in the dataset(s) you want to control

• Once linked, the filter will apply to all connected components

Example

You have:

• A table showing property listings

• A chart showing average price per city

If both use a dataset with a “city” field:

• A “City” filter can control both components at the same time

Important

• If a component uses a different dataset without the same field, the filter will not affect it

Requirements for filters to work

Filters depend on the structure and quality of the underlying data.

Minimum requirements

• The dataset must contain the field used in the filter

• Field values must be consistent (e.g. “Frankfurt am Main” vs “Frankfurt a. M.” are treated differently)

Limitations and expected behavior

Understanding limitations helps avoid confusion when filters do not behave as expected.

Common limitations

• Filters only affect linked datasets, not the entire dashboard automatically

• Fields with missing or inconsistent values may lead to incomplete results

• Some filters may not support multi-selection, depending on configuration

• Performance may decrease with very large datasets

Expected behavior

• Components update immediately after a filter is applied

• If no data matches the filter, components may show the message "No data av

• Filters remain active until cleared or changed

Good examples

• Using a “Property type” filter to switch between apartments and houses

• Combining “City” and “Price range” to narrow down listings

• Linking one filter to multiple components for consistent results

Bad examples

• Adding a filter for a field that does not exist in the dataset

• Using unclear labels like “Category” instead of “Property type”

• Expecting a filter to affect components that are not linked

Working with multiple datasets

When a filter is linked to multiple datasets, it combines (consolidates) all available values from those datasets into a single list of options.

How it works

• The filter reads the selected column (e.g. “City”) from all connected datasets

• It merges all unique values into one combined list

• Duplicate values are automatically removed

Example

You have two datasets:

• Dataset 1 (City column)

  • Berlin

  • Munich

  • Hamburg

• Dataset 2 (City column)

  • Berlin

  • Frankfurt

  • Hamburg

Filter result:

• Berlin

• Munich

• Frankfurt

• Hamburg

Expected behavior when selecting values

When users select a value that exists in only some datasets, not all components will display data.

Example

• The user selects “Frankfurt”

• Only Dataset 2 contains this value

Result:

• Charts connected to Dataset 2 will update and show data

• Charts connected only to Dataset 1 will show no data (empty state)

Important considerations

• Filters do not guarantee that all components will always display results

• Empty charts are expected if the selected value is not available in a dataset

• This behavior is correct and does not indicate an error

Best practices

• Ensure consistent data across datasets when possible (e.g. same cities)

• Avoid linking too many unrelated datasets to the same filter

• Clearly structure dashboards so users understand why some components may be empty

Summary

Filters are a powerful way to make dashboards interactive and relevant. To use them effectively:

• Choose meaningful fields

• Ensure your data supports the filter

• Link filters correctly to datasets

• Keep the user experience simple and clear

When set up properly, filters help users quickly explore real estate data and focus on what matters most.