Create a Custom Visualization

With GoodData.UI, you can create a new, customized visual components to address your specific analytics needs.

Your component code must be wrapped within the Execute component. This component lets you conveniently specify the data to render and then access the results:

import { Execute } from "@gooddata/sdk-ui";

function CustomVisualization() {
    return (
        <Execute
            seriesBy={measuresAndAttributes}
            slicesBy={attributes}
            onLoadingChanged={e=>{}}
            onError={e=>{}}>
            {
                (execution) => {
                    const { isLoading, error, result } = execution;
                    if (isLoading) {
                        return (<div>Loading data...</div>);
                    } else if (error) {
                        return (<div>There was an error</div>);
                    }

                    // access result by slices (rows);
                    const slices = result.data().slices().toArray();

                    return (
                        <div>
                            {slices.map((slice, idx) => {
                                // for each slice (row), print the header and then the actual formatted data points
                                return (
                                    <div key={idx}>
                                        {slice.sliceTitles().join(">")} - {slice.dataPoints().map(dp => dp.formattedValue())}
                                    </div>)
                                })}
                        </div>
                    );
                }
            }
        </Execute>
    )
}

Data series and data slices

The concept of data series and data slices used by the Execute component is best explained on a couple of real-life examples.

Tabular data

Imagine that you want to create a custom table component. This component should show one row for each value of the attribute A1. In each row, there should be two columns, one for the measure M1 and one for the measure M2.

In this scenario, the data series are the two measures M1 and M2, and the slices are defined from the attribute A1.

Now, imagine that this typical table must become more dynamic. For each value of th eattribute A2, the table must include two columns: one for each measure, M1 and M2.

In this scenario, the data series are measures M1 and M2, scoped to values of the attribute A2. And on top of it, these columns are sliced by values of the attribute A1.

Scalars

Imagine that you want to create a custom KPI component. This component should show a couple of key performance indicators, each calculated from a different measures: M1, M2, and M3.

In this scenario, the data series are the measures M1, M2, and M3, and there are no data slices at all.

Working with the results

Once the Execute component reads the results from the Analytical Backend, it will pass the result to your custom function. The instance of the result contains several methods for convenient data access.

• You can access the result by data series by calling the result.data().series().
• You can access the result by data slices by calling the result.data().slices().
• These methods return a collection of series and slices respectivelly.
• You can either iterate the items from the collection using the for-of loop or transform it to an array and then use the typical array mapping and manipulation functions of JavaScript.
• For each series or slice item, you can then iterate the available data points.
  • Iterating data points for a series gives you one data point per slice.
  • Iterating data points for a slice gives you one data point per series.
• Each data point contains both data and all available metadata (series descriptor, slice descriptor). You can access either the raw data or formatted data.

NOTE: While the result instance exposes the raw results from the backend, we strongly discourage you from accessing the raw data.