Drop-down tree example

Drop-down trees can be defined in two ways: automated build and layered build. The key is what is your data structure? Choose the definition based on your data.

The drop-down tree is mainly in the automated construction mode. By directly converting datasets into tree datasets, the drop-down tree directly selects tree datasets to automatically construct the hierarchy.

The advantage of automated builds is that you don't need to write SQL queries, it's all done automatically by FR.

Of course, the premise is that your base data must meet the following conditions, that is, there needs to be a clear parent-child relationship, as shown below, only such data can generate tree dataset.

For details about how to define the process, see Bulid drop-down tree.

Each layer needs to write SQL to implement, and the SQL statement of each layer must be defined as a report dataset. Select the corresponding dataset in the drop-down tree data definition interface.

Filter by referencing the values of the upper layer in FR. If you need to reference the first layer, use layer1.

So when do you choose to build hierarchically? It can be said that any data structure can use a layered build, of course, if your data meets the requirements of automated build, you can use automated build, otherwise choose a layered build.

For details about how to define the process, see Drop-down tree widget layered construction example.

Index:

Drop-down tree widgets are selected all by default

Drop-down tree widget speed layer build example

Build drop-down tree

Drop-down tree widget layered build example

Multi-select drop-down trees enable queries between different levels

Drop-down tree implements dataset filtering

The fill page drop-down tree widget returns the selected level value

Dropdown tree widget FAQ