Power BI - Card and Slicer Visualization



Power BI is an awesome tool for creating interactive reports and modeling data. Various built-in visuals are exclusive in the Visualization pane where measures, aggregation functions, and so on can be applied to construct the specific model. A card visual is one of the crucial visuals where a computed solitary value like average salary, maximum salary, or total production in a specific year is to be highlighted on a card.

Calculating Average Monthly Salary via Card Visualization

Step 1 − You can click on the "Power BI Desktop" icon, navigate to the "Home" tab select the "Get data" tile, and click on the "Excel workbook" to load the table named Employee.xls.

Calculating Average Monthly Salary

Step 2 − Users need to choose the specified file name from the location of the system and then click on the "Open" button.

Calculating Average Monthly Salary 1

Step 3 − The preview of the Employee table is shown in the Navigator window box. Now, click on the "Sheet1" checkbox and press the "Load" button as given below −

Calculating Average Monthly Salary 2

Step 4 − Moreover, the employee table is inserted in the Power BI desktop. All the tables are shown under the "Data" pane.

Calculating Average Monthly Salary 3

Step 5 − You may click on the Report view, expand Sheet1, and select the "Salary" column as highlighted below image −

Calculating Average Monthly Salary 4

Step 6 − After switching to the Report Editor, by default, the sum of the salary column chart graph is automatically displayed on the window. Now, select the Card visual icon.

Calculating Average Monthly Salary 5

Calculating Average Monthly Salary 6

Step 7 − Click on the lower arrow adjacent to the "Sum of Salary" under the Fields section and select the Average option from the drop-down menu as given below −

Calculating Average Monthly Salary 7

Step 8 − Therefore, the average employee's salary that is 68.86K is showcased through a Card visual icon as shown below −

Calculating Average Monthly Salary 8

Slicer Visual

You may insert a Slicer (new) to filter employee data according to their job roles. The Visual format option may be chosen to develop a more interactive slicer where boundaries, font size, and font color can also be edited.

Slicer is an alternative way of filtering. Numeric Value, Relative Date and time, and Hierarchy are the primary types of slicers. The benefits of slicer(new) include easy access to filtered data, fast retrieval of employee-related information, column-based data filtering, vibrant reports, and compatibility with other charts.

Filtering Employee Names with Slicer Based on New Constraints

Step 1 − Consider the sample dataset "Employee" comprises six columns as given below −

Slicer Based on New Constraints

Step 2 − Two slicer options are available in the Visualization pane. To insert the slicer into the editor, the user may select the "Slicer(New)" visual under the Visualizations pane. The empty slicer would be generated. Users may expand the slicer's size for clear visibility.

Slicer Based on New Constraints 1

Step 3 − You may drag the "Name" column of the Employee table and drop it into the "Field" textbox. Once you have selected this column, all employees' names will display on the Slicer as given below −

Slicer Based on New Constraints 2

Step 4 − You can drag the "Job Role" column and drop it into the "Drill through fields". Select the "Used as category" option. All the employees are listed in the category form along with a number. The numbers 1,2,..10 indicate the number of employees working in a specific job role.

Slicer Based on New Constraints 3

Step 5 − Let's click on the "Cyber Security Analyst" checkbox, the employee's name "Jennifer" is displayed on the slicer which means only one employee is currently working on this role. Similarly, we can choose other job roles and filter the employee's name accordingly.

Slicer Based on New Constraints 4

Sometimes, beginners encounter difficulty in evaluating the complex datasets in Power BI. As they are not aware of the crucial functionality of this tool, encounter difficulty in understanding DAX measures and the usage of the calculated column. However, with the assistance of Card Visual, they can seamlessly view the result without any interruption. Users may practice more on numerous visuals to improve their data analytics skills.

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