How to set up data ranking - Quick BI - Alibaba Cloud Documentation Center

Data ranking sorts data by size, making it easier for users to compare values. In data analytics, you can view the ranking of measures to help you make informed decisions. This topic describes how to set up data ranking.

Prerequisites

You have created a dashboard. For more information, see Create a Dashboard.
A dataset is selected and chart fields are configured. For more information, see Configure Chart Fields.

Limits

This analysis method is based on the returned result set calculation. The current limit in the public cloud is 10,000. If the total returned data exceeds 10,000, the calculation results may not meet expectations.

The charts that support data ranking are shown below.

Data ranking method	Description	Supported chart types
Overall ranking	Overall ranking refers to the ranking of a data point among all data points.	Line/Area charts: line chart, area chart, stacked area chart, 100% stacked area chart, combination chart. Column/Bar charts: column chart, stacked bar chart, 100% stacked bar chart, circular column chart, leaderboard, bar chart, stacked bar chart, 100% stacked bar chart, dynamic bar chart, waterfall chart. Bubble/Scatter charts: bubble chart, scatter chart, facet scatter chart. Table charts: cross table, detail table, heat map. Pie/Ring charts: pie chart, rose chart, radar chart, treemap chart. Geographic charts: color map, bubble map, heat map, flow map, symbol map. Metric charts: metric card, metric trend chart. Funnel/Conversion charts: funnel chart, comparison funnel chart. Other charts: word cloud.
Group ranking	Group ranking refers to the ranking of a data point within its group.	Table charts: cross table.

Configuration Entry

In the Fields panel of the chart, find the measure field that you want to rank, click the icon on the right, and select Advanced Calculation > Ranking from the dropdown list. Then set the ranking type according to your business scenario.

The ranking calculation types and ranking methods available for each chart are described below.

Ranking calculation types

Chart type	Ranking calculation type		Limitations	Calculation logic
Cross table	Ascending		/	Ranks the overall data from smallest to largest based on metric values.
	Descending		/	Ranks the overall data from largest to smallest based on metric values.
	Ascending within group		/	Groups data by the finest granularity and ranks data points from smallest to largest within each group. For example, if there are three dimensions: region, province, and city, cities are grouped by their respective provinces and regions, and then ranked in ascending order.
	Descending within group		/	Groups data by the finest granularity and ranks data points from largest to smallest within each group.
	Custom	Rank by column	Dimension fields in the Row area	Treats each column as an independent analysis unit and calculates the ranking of a data point within the total number of data points in a column.
		Rank within column group	Two or more dimension fields in the Row area	Calculates the ranking of the current data point within its column group. The grouping logic is as follows: starting from the first dimension in the row area up to the selected grouping dimension, all dimensions in between are treated as a whole for grouping. For example, in the following figure, the Region and Province fields are matched for grouping, and ranking is performed at the Province dimension.
		Rank by row	Dimension fields in the Column area	Treats each row as an independent analysis unit and calculates the ranking of a data point within the total number of data points in a row.
		Rank within row group	Two or more dimension fields in the Column area	Calculates the ranking of the current data point within its row group. The grouping logic is as follows: starting from the first dimension in the column area up to the selected grouping dimension, all dimensions in between are treated as a whole for grouping. For example, in the following figure, grouping is performed on the Shipping Mode field, and ranking is done within this dimension.
Other charts	Ascending		/	Ranks the overall data from smallest to largest based on metric values.
	Descending		/	Ranks the overall data from largest to smallest based on metric values.
	Custom		/	Customizes the ranking order and ranking method.

Ranking method: When a chart uses the Custom ranking calculation type, you can configure the Ranking order and Ranking method as needed. The ranking method determines how to handle duplicate values in rankings. You can choose from the following three ranking methods.

Ranking method	Description	Example
Rank	Duplicate values have the same rank and occupy positions.	1, 2, 2, 4
Dense rank	Duplicate values have the same rank but do not occupy positions.	1, 2, 2, 3
Sequential rank	Duplicate values have different ranks.	1, 2, 3, 4

Application example 2: Setting up ranking in other visualization charts

Take the metric card ranking as an example.

Log on to the Quick BI console.
Follow the steps shown in the figure below to enter the dashboard editing page.
In the top menu bar, click Add Chart, find Metric Card, and click or drag it to the dashboard area.
In the Data Panel's Fields tab, configure the metric card data.
The target dataset is company_sales_record.
1. Select the required dimension and measure fields, double-click or drag them to the target area.
  - In the Dimensions list, find Region, double-click or drag it to the Row area.
  - In the Measures list, find Order Amount, double-click or drag it to the Column area.
2. Click Update.
  The system automatically creates a metric card for you, as shown in the following figure.
Click the icon to the right of Order amount, select Advanced Calculation -> Ranking, and follow the steps shown in the figure below to enter the ranking settings.
Visualization charts support Ascending and Descending ranking.
Click Update.
The Order amount ranking in the metric card is shown in the figure below.
- When Ascending ranking is selected for Order amount, the largest Order amount is ranked last. In this example, the South China region has the largest Order amount and is ranked No.7.
- When Descending ranking is selected for Order amount, the largest Order amount is ranked first. In this example, the South China region has the largest Order amount and is ranked No.1.

Application Examples

The following examples help you better understand data ranking calculations.

Example 1: Metric Card

Take the metric card ranking as an example.

Log on to the Quick BI console.
Follow the steps shown in the figure below to enter the dashboard editing page.
In the top menu bar, click Add Chart, find Metric Card, and click or drag it to the dashboard area.
In the Data Panel's Fields tab, configure the metric card data.
Select the required dimension and measure fields, double-click or drag them to the target area.
- In the Dimensions list, find Region, double-click or drag it to the Row area.
- In the Measures list, find Order amount, double-click or drag it twice to the Column area.
  Note
  Of the two Order amount fields in the Column area, one displays the raw data and the other is set for cumulative calculation.
  When there are two duplicate fields placed on measures, you will see a "Duplicate items exist in measures" prompt. Do not update the data yet. Update after the configuration is complete.

Click the icon next to the second Order amount field, select Advanced Calculation > Ranking. You can set different ranking methods for the current chart as needed. Specific examples are shown below.

Ranking calculation type		Description	Example effect
Ascending		Overall data is ranked from smallest to largest, so the largest Order amount is ranked last. In this example, the Southwest region has the largest Order amount and is ranked No.7.
Descending		Overall data is ranked from largest to smallest, so the largest Order amount is ranked first. In this example, the Southwest region has the largest Order amount and is ranked No.1.
Custom In this example, Descending order is selected	Rank	When duplicate values exist, they have the same rank and occupy positions. In this example, Northeast and East China have the same Order amount, both ranked No.4, and they occupy a position, meaning No.5 is skipped.
	Dense rank	When duplicate values exist, they have the same rank but do not occupy positions. In this example, Northeast and East China have the same Order amount, both ranked No.4, and they do not occupy a position, meaning No.5 still exists.
	Sequential rank	When duplicate values exist, they have different ranks. In this example, Northeast and East China have the same Order amount, but they have different ranks: Northeast is No.4 and East China is No.5.

Example 2: Cross Table

Cross tables have different ranking calculation methods than other charts. Specific examples are shown below.

Ranking calculation type		Configuration description	Example effect
Ascending		Overall data is ranked from smallest to largest, so the largest Order amount is ranked last. In this example, the South China region has the largest Order amount and is ranked No.7.
Descending		Overall data is ranked from largest to smallest, so the largest Order amount is ranked first. In this example, the South China region has the largest Order amount and is ranked No.1.
Ascending within group		In this example, provinces are grouped by the region dimension, and Order amounts are ranked from smallest to largest within each region. For example, in the Northeast region, Heilongjiang Province has the smallest Order amount and is ranked No.1.
Descending within group		In this example, provinces are grouped by the region dimension, and Order amounts are ranked from largest to smallest within each region. For example, in the Northeast region, Heilongjiang Province has the smallest Order amount and is ranked No.3.
Custom In this example, Descending and Rank are set	Rank by column	In this example, the overall data is ranked in descending order by individual columns. For example, in the Truck shipping mode, South China has the highest Order amount and is ranked No.1.
	Rank within column group	In this example, both the first dimension field and the grouping dimension in the Row area are Region, so grouping and descending ranking are based on the Region dimension. For example, in the Northeast region, Heilongjiang Province has the lowest Order amount transported by truck and is ranked No.3.
	Rank by row	In this example, the overall data is ranked in descending order by individual rows. For example, in Jilin Province, the Train shipping mode has the highest Order amount and is ranked No.1.
	Rank within row group	In this example, both the first dimension field and the grouping dimension in the Column area are Product Type, so grouping and descending ranking are based on the Product Type dimension. For example, for office supplies orders in Jilin Province, the Train shipping mode has the highest Order amount and is ranked No.1.