Bullet Chart — Tableau

Tableau is one of the popular and most widely used data visualization tool. It is a business intelligence, data analytics and data infrastructure platform that has been created to simplify data-driven decision making.

Using Tableau, we can create a variety of charts and interpret the data to arrive at useful conclusions. In this blog, I’ll explain about the bullet charts and how to create them in Tableau.

What is bullet chart?

Bullet chart is a variation of bar chart. It has been developed by Stephen Few. According to Stephen’s original specification, “The bullet graph was developed to replace the meters and gauges that are often used on dashboards. Its linear and no-frills design provides a rich display of data in a small space, which is essential on a dashboard.”

What does a bullet chart represent?

Bullet chart encodes three different data elements:

• Feature Measure
• Comparative Measure
• Range of values

For example, let’s use the Superstores dataset. If we create a bullet chart to compare the sales against the profit, the bar will indicate the sales happened and the marker identifies the profit.

This figure shows the sales(the bar) against the profit(the marker) for the category ‘Office Supplies’ during the year 2018. The background color represents the range of values.

How to create bullet chart in Tableau?

Step-1: Connect to the dataset. Here I’m using the Superstores dataset from: www.kaggle.com.

Step-2: Open new worksheet.

Step-3: Select the measures ‘Profit’ and ‘Sales’.

Step-4: Drop the measures into the ‘Rows’. Two separate bar graphs will be created.

Step-5: Click on ‘Show Me’ option at the top right corner and select the bullet chart. It is the last but one chart from the list.

Bullet chart will be created with ‘Profit’ as feature measure and ‘Sales’ as comparative measure.

Step-6: We can change the ‘Sales’ as feature measure and ‘Profit’ as comparative measure. To do so: Right click on x-axis (‘Profit’) in the chart and select the option ‘Swap Reference Line Fields’.

Now, the measures get swapped, the bar represents the ‘Sales’ and the marker, the ‘Profit’.

Step-7: Add some dimensions to get details of further break down. Here I have added the ‘Category’ and ‘Order Date’ dimensions.

There are three categories as ‘Furniture’, ‘Office Supplies’, and ‘Technology’. Also, the dataset contains the sales data for 4 years.

From this chart, we can infer that the ‘Furniture’ sales increased gradually year after year, whereas the profit has the variation of raising and falling. But for the other categories, the profit is increasing every year irrespective of a decline in sales during the year 2019.

The background color variation represents the range of profit. By default, this range will be 60%,80%. This range and also the color can be changed by: ‘right click on the x-axis (sales)→ Edit Reference Line → 60% 80% of Average Profit’ option.

Conclusion

Bullet charts are more powerful when we have to compare different measure. Let us assume that our Superstores dataset has a target-sales column that specifies the number of units of each and every product to be sold. When we compare, this measure with the actual ‘Sales’ measure by using the bullet chart, the visual will provide a clear picture of the % of target met by each product category. Based on the ‘marker’, that is the target-sales, we can arrive at conclusions like: setting the target-sales for the upcoming year.