Summary Modes for Charts

Application Scenario

Summarized data used in chart creation can simplify the complex raw data, making charts clearer and easier to understand. For example, summarized data can be used to track and display key performance indicators of business or projects, such as total or average sales volume, facilitating performance change evaluation and goal achievement.

Function Description

The functionality and logic of chart summary modes are consistent with those of table summary modes. This document provides a brief introduction. For details, see Summary Modes for Tables.

Items in Summary Mode include Sum, Average, Median, Maximum, Minimum, Standard Deviation, and Variance. You can click the  icon next to an indicator field, select Summary Mode from the drop-down list, and select an item to switch to the corresponding mode, as shown in the following figure.

1. Sum is the default summary mode. Each column represents the total sales of each product type, as shown in the following figure.

2. You can switch the summary mode to Average.

Click the  icon next to the field Sales, select Summary Mode from the drop-down list, and select Average. At this time, each column represents the average sales of each product type.

3. Besides, you can calculate the Median, Maximum, Minimum, Standard Deviation, and Variance of the Sales value of each product type by selecting corresponding items. For details, see Summary Modes for Tables.

If you want to display data details, you can drag the Text/Date field (the data in which has the finest granularity in a data table) into Dimension/Fine-grained. For example, in a Sales Detail table, you can drag the field Order Code into Dimension to display indicator details according to fields such as Order Information Record, Order Code, Product, Sales, and Profit.

When creating a Scatter Chart, you can drag fields into Fine-grained after dragging fields into Horizontal/Vertical Axis. The data will be aggregated based on the dragged dimensions in Fine-grained, and the more dimensions there are, the finer the data will be.