Pie charts can be tricky to interpret for a variety of reasons:

  1. It’s difficult to accurately judge the size of each slice of a pie chart. It can be tricky to compare slices that are close in size.
  2. Pie charts usually have minimal labelling. Each slice can only be labelled once, so it’s challenging to add additional information about the data being represented.
  3. They can become cluttered and difficult to read.

If we were to create a pie chart using superstore data on subcategories and sales, we would receive something like this:

This chart is extremely confusing and has too much going on. We have so many subcategories, some labels are missing. It is tough to gauge the slices of the pie chart. Overall, this chart holds no value and no information.

We can use a filter to select only a couple of subcategories:

This is easier to understand, however, the chart is misleading as the “pie” does not represent 100% of the sales. We will use LODs to fix that and create a pie chart that will represent 100% of the sales and the user can select the number of subcategories they want to see.

Firstly, we have to union the data to itself. After doing that, we will see that there is a new column in our dataset named [Table Name]. We will use first the table for creating other categories and overall sales, and the second one for our selected sub-categories. Then we have to create a calculated field with an if statement:

// Sub-Category New

IF [Table Name] = “train.csv” THEN [Sub-Category]

ELSE “Other” END

This will take sub-categories from one table and will grab others from another. The next step is taking calculating the overall sales we have. Note that in this step we are dividing by 2, because we have double the sales in our dataset.

// Total Sales

{FIXED : SUM([Sales])}

The final calculated field will adjust the sales amount depending if it is “other” or a selected sub-category.

// Sales Fixed

IF ATTR([Sub-Category New]) = “Other” THEN MAX([Total Sales]) – SUM([Sales])


Now, all we have to do is drop a new sub-category onto colour and our fixed sales into an angle.

This pie chart highlights the relationship between the overall sales and selected sub-categories.

Adding an “other” category in a pie chart can be useful for combining small slices that may be difficult to interpret on their own. To do this, simply group all the small slices together and label them as “other”. It’s important to note that the “other” category should still be labelled and represented accurately to avoid misleading viewers.

Veronika Varaksina
Author: Veronika Varaksina

Meet Veronika, a dynamic and adaptable individual with a diverse background in economics, accounting, finance, and data analytics. Veronika pursued a Bachelor’s degree in Economics and gained valuable experience in financial analysis, budgeting, and forecasting while working for five years in accounting and finance. However, she soon realized her passion for data analytics and decided to pursue a postgraduate degree in Analytics at Victoria University. Throughout her academic journey, Veronika honed her skills in data visualization, statistical modeling, and machine learning. Her expertise earned her a spot in the highly competitive Data School program, where she further continues to expand her skills in data analysis.