One of the biggest challenges in data cleaning is the identification and treatment of outliers. In simple terms, outliers are observations that are significantly different from other data points. Even the best of algorithms will under perform if outliers are not cleaned from the data because they can adversely affect the training process of a machine learning algorithm, resulting in a loss of accuracy. Therefore, it becomes extremely important to detect outliers. The most prominent visualization tool to detect outliers is the box-and-whisker plot.
In this guide, you will learn how to build a box-and-whisker plot in Tableau.
The chart below depicts a box-and-whisker plot:
This chart type contains the following components:
Lower Outlier Points < 1st quartile (Q1) value – 1.5 times the interquartile (IQR) value, and
Upper Outlier Points < 3rd quartile (Q3) value + 1.5 times the interquartile (IQR) value,
We’ll be using the
Sample – Superstore data, available on the Tableau repository, to create a box-and-whisker plot. The goal is to visualize the continuous measure,
Sales, against the dimension
Category) and measure of interest (
Sales). To do that, we’ll drag the category dimension in the Columns shelf, and sales in the Rows section. Also drag the dimension Region to the Detail Marks Card. The output is shown in the chart below:
The other option for plotting a box-and-whisker chart is to right-click on the axis of the measure and click on the add reference line. A new window will pop up and the option will be there to draw a box plot. This will create the box plot, and we can see that the tool tip gives us information about the spread of the data, such as the median, upper and lower whiskers, and upper and lower hinges.
If the data points are not properly visible, we can increase the circle size and reduce the opacity of the Color fill in the Marks tab.
In this window, we can change the whiskers extension to the maximum extent of data, or data within 1.5 times of the IQR value, with the latter being the default option. It is also possible to make other formatting changes. The final box plot is shown below.
If the data is normally distributed, the median will be displayed in the middle of the box plot. However, if the data is skewed, the box-and-whisker plot will also be skewed accordingly.
In this guide, you learned about building a box-and-whisker plot in Tableau. You also learned the components of the box plot, which will help you understand the data distribution of variables and draw meaningful insights from it.