Build a chart from Table Nodes

Acho app builder provides a seamless workflow to for creating charts using Table Nodes with no-code. This enables users to efficiently transform their data into meaningful Business Intelligence or Data Science visualizations.

Prepare your data

In this tutorial, we will use digital_ads_performance data from sample Postgre dataset. You can also prepare your own data and add it to resource to proceed the following steps.

Upload csv data to resource

Create an app

Create an app if you don't have one, or go to the app that you want to create a chart.

Drag a chart element onto page

To add a table to your page, go to Elements -> Chart at left tool bar and drag it onto the page.

Drag a chart

Select Data source of the chart

Select digital_ads_performance in Table Node -> sample-postgre as the Data source of your chart.

Select Chart Properties

Select Chart type

Choose a chart type to load a template. For example, in the video, we use Pie chart and Line chart to visualize the digital_ads_performance data.

Select Dimension and Metric

• Dimensions: These represent qualitative data and serve as categories or labels for your data. They provide the contextual aspect—answering the 'what'—to help make sense of the corresponding metrics.

• Metrics: These are quantitative data points. They offer measurable insights to questions such as "How much?", providing a context in which the numbers (metrics) can be understood.

For example, choose 'Pie chart' as the chart type, 'channel' as the Dimension, and 'impressions' as the Metric, and you will get a chart like this:

Aggregate data with Dimension and Metric

Furthermore, you can modify how the raw data is grouped and aggregated before generating the chart, all without needing any coding.

For enhanced visualization, charts aggregate Table node metrics, grouped by the dimension you selected. You can choose measures of aggregation, such as sum or average for numeric metrics, and count or count distinct for non-numerical metrics. The table node data will be grouped and ordered by the dimension, and the metrics will be aggregated in the backend according to your chosen measures. Subsequently, the chart renders this aggregated data to provide an optimal visualization.