Calculating Percentages in PowerBI


Last time I showed how you can pivot a multivalued column from a survey downloaded from SharePoint to Excel. After some transformations, I created the following report that shows the number of career responses in each of the 18 categories. While interesting in of itself, it would be more interesting to see how the career choices break down by different dimensions such as the school or grade level, the gender of the student, or perhaps even by individual school.

In my report page, I am going to reserve an area along the left side of the page to list several of the dimensions I’m interest in and to display the total number of students that took the survey along with the number of students in the filtered subset when I filter by one of the dimensions. Basically that means building something like the following:

This image actually consist of four separate visualizations. The top visualization for Total Students uses the Card visualization and initially displays the count of the ID column in the survey table that has one record for each survey. I duplicate this visualization and drag it below the first one. Then for the top visualization, I open the Format option panel in the visualization section and make the following changes:

  • I turn off the Category Label
  • I turn on the Title and then open the Title section to add the Title Text: Total Students
  • I change the font size and color to make this information stand out.

I then repeat the process for the second visualization but with the Title Text: Sampled Students because this count will represent the number of students included in the visualizations taking into account any filters applied to the page.

I then add two single column tables, one for gender and one for grade level. Because I want to use the values in these table as page filters, I change the visualization of these two tables to the Slicer.

Along with the table I created in my last blog, I can work with the slicer values to explore how the career choices change based on gender and grade level by clicking on the check boxes. When I do this, I see that the chart automatically adjusts the columns based on the changes I make to the slicers. Also the number of sampled students in the second card visualization displays the count of students included after applying the slicer. Unfortunately, the total student count also changes. This I do not want. I want the total number of students to always represent all the students in the entire survey.

I can achieve this goal with a little DAX and a custom measure back in the data page for the survey table. The custom measure needs to count the IDs for all the records in the table ignoring any filters applied to the report page. I can do this by passing the survey table name to the ALL() function. This function ignores all other page filters. Then I use the COUNTAX() function which defines the data source as the output from the ALL() function and then performs a count the number of IDs. While this may sound complicated, it is as simple as the following equation:

Notice that I name the measure Students. I must provide a unique measure name for each measure I create. However, I can then use that measure in any visualization such as the card visualization for the total students in the survey.

Back on my report page, I select the top card visualization (for Total Students) and change the field used from ID to my new measure, Students.

Now if I select any of the values in the slicer visualizations, my sampled students card displays the number students included in the filter while the total students card displays the total number of student surveys taken as shown below.

I then add on the right some additional column visualizations to display other data fields such as which subject the student finds most interesting in school or charts that display career choices by gender, by grade level or by other criteria from the survey. Each of these charts begins with a simple table visualization in which I add the columns I want to use. I then convert the visualization to a column chart.

In the image below, you see the final result of the first page of my report. Notice that I also added a vertical line shape to separate the two card visualizations and the slicers from the other column charts.

Since each student was allowed to select one or more careers from the list of possible careers, the total number of career choices is significantly larger than the number of students. In the above figure, the count of career interests, if I were to add the values in each of the columns, would total the number of career selections which is over 17,000, not the number of students. Therefore, I might decide to display the same charts as a percent of all the students rather than a count of all career selections.

Again I need another custom measure to calculate the percent based on total students. Fortunately, I already have a measure that calculates the total student count ignoring any slicer selection or page filter. Therefore, I can generate a percentage using a formula similar to the following:

With this formula, I count the number of surveys filtered by the visualizations and slicers on the page divided by the total number of students who took the survey. The maximum value of this percentage would be 100% if all the students who took the survey selected the same career, such as computers, as one of their choices. Similarly, the minimum value would be 0% if no student selected a specific career as one of their choices. Because students could select more than one career of interest, the sum of the percentages of each of the columns will not add up to 100%, but some value greater than 100%. (Lesson learned: Next time ask for their preferred career choice, then their second career choice and finally their third career choice.)

Next I take the first page of my report and duplicate it by right clicking on the page tab and selecting the option to duplicate the page. (This is a lot faster than recreating the same visualizations on a new page, isn’t it?) On the duplicated page, I modify each of the column charts to display the Percent measure just created rather than the count of ID.

Why does this work? Well, because each student can only select any specific career one time even though they may select two, three, or more careers, I can simply count the number of filtered students in each column by the total number of students in the survey. Each chart already divides the students by career choice, one career for each column. Then additional filters from the slicers may limit the gender or grade level or both. Therefore, I can count students that match all those filter criteria and divide by the total student count to get a percentage of students who have an interest in that career. In fact, this measure also correctly calculates the percent of students interested in each school subject (of which they can only select one subject each) which I can verify by summing these percentages from the chart in the upper left and getting a total of 100%.

The following figure shows the percent page of my report.

I can then proceed to add other visualizations if I want on additional pages. But I’ll suspend this example at this point for now.

C’ya next time for more exciting ways to use Power BI.

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