- rds team

# Tableau charts: Box plot

Updated: Jul 14, 2020

The series, Tableau charts has always focused on one thing, helping Tableau users learn how to create different charts and graphs hence equipping them with different techniques of telling each data story.

Therefore, inspired by the mission of this series, this post will help you learn how to build a box plot in Tableau. But first, what is a box plot? According to Wikipedia, box plot is a method of depicting groups of numerical data through their quartiles. Box plot may also have lines extending vertically from the boxes (whiskers) indicating the variability outside the upper and lower quartiles hence terms box and whisker plots. Outliers (Observation that is distant from other observations) may be plotted as individual points. A sample of box plot diagram below.

Using Superstores data set pre-packaged with Tableau app, we’ll seek to depict the distribution of **Sales** for Product **Sub-Category**.

**Step 1: Build a simple bar chart**

Drag dimension

**Sub-Category**to the column shelf.Drag measure

**Sales**to the row shelf. (Note,**Sales**is aggregated by**SUM()**).

Executing this, results to a simple bar chart.

**Step 2: Make the box plot**

Under

**Show Me**tab, choose*box-and-whisker-plots*as shown above.

Note, moving the cursor over the box displays a summary statistic as described in the box plot diagram at the beginning of this article.

Looking carefully at the resulting chart, it shows distribution of **Sales** at **Sub-Category** summary level. Suppose, my interest is to see how **Sales** is distributed within the individual **Sub-Category’s** while still being able to compare distribution across other **Sub-Category’s**.

To effect this;

Drag dimension

**Sub-Category**to the row shelf.Under

**Analysis menu**uncheck ‘**Aggregate measures**’.

See below.

And there we’ve our box plot.

Using this box and whisker plot. Simple insights like distribution of **Sales** for different products **Sub-Category’s** can be spotted easily. For instance, using interquartile range, one can tell that the Sub-Category **Machines** and **Copiers** have the highest distribution of **Sales**. Outliers can also be spotted easily for different **Sub-Category’s**.

I hope this article adds to your resources of some of the best techniques you can use to transform how you communicate your data insights.

Thanks for reading.