Tableau is the most popular interactive data visualization tool, nowadays. It provides a wide variety of charts to explore your data easily and effectively. This series of guides - Tableau Playbook - will introduce all kinds of common charts in Tableau. And this guide will focus on the Dot Plot.
In this guide, we will learn about the dot plot in the following steps:
Here is a dot plot example from Eurostat. The following example shows the distribution of unemployment within each country. Each point represents a region. Its color and x-axis offset represent the level of unemployment rates.
The dot plot is a powerful chart with the ability to include several different data fields into a meaningful visualization.
Specifically, in Tableau, the dot plot is called circle views. In most cases, if we want to compare categorical data with one measure, we usually choose a highlight table which displays in colors. With two measures, we consider a heat map using size and colors. With three measures, the best practice is the dot plot. It compares categorical data using position, colors, and size.
By visualizing with a dot plot, we are able to identify patterns or correlation much quicker than looking at the raw data of the table. It also has high scalability, which means displaying plenty of data in a single chart. Besides, It is space-efficient and a good way to show individual values from the entire distribution.
On the other hand, similar to highlight table, the dot plot limits the number of dimensions. And it is hard to distinguish small differences in amounts. It has the potential risks of data points overlap. Overplotting makes it difficult to dig out useful patterns and conclusions.
This dataset contains employment data by industry for 2011 and 2014 by city for Great Britain. The 1-digit sheet has data aggregated at the industry level.
We will analyze the distributions of jobs group by industry and city. We will also focus on the changes in jobs from 2011 to 2014 under the influence of these factors.
Let's draw a basic dot plot step by step:
Click on Show Me and see the request for the circle views.
For circle views, try 1 or more Dimensions, 1 or more Measures.
But we won't be satisfied with the default template, so we choose to build a dot plot manually:
Transform to diverging stepped colors as what we did in highlight table and heat map:
In the last step, let's polish this chart:
A basic dot plot is completed. But we can see there are still many defects in this basic dot plot. We will optimize it with advanced features.
Let's go ahead in optimizing the basic dot plot:
Add a reference line to show the distribution more clearly: Navigate to Analytics -> drag Median with Quartiles into Table.
From the previous dot plot, we can see that even when we optimize the axis, the points still overlap into clusters. For further optimization, we could use the jitter technique. Jittering makes the data points more visible by using the second axis to randomly position.
random(). It will be aggregated as "SUM(random())" automatically.
Add one more measure as size. In this example, we continue to use "% Change".
Since we use jitter technique in the y-axis, we should add a caption to let users know to clear their confusion.
This is the final chart:
From the above dot plot, we can see all the job distributions in a single chart, which come from different cities and industries. And, with many advanced features, it visualizes the data more intuitively.
We enhanced our chart by x-axis truncating and scaling and y-axis offset with jitter technique; we reduced the overlap of points and see the distribution more clearly.
Aided by the position, size, and diverging stepped colors, this dot pot indicates the amount and trend of jobs intuitively. For example, we can find out the Mining And Quarrying industry has a small number of jobs (below quartile in reference line) but a high growth rate.
We can focus on a particular city with the help of highlighter. Such as London has the most significant job amount and a high growth rate.
In this guide, we have learned about a variation of the text table in Tableau - the Dot Plot.
First, we introduced the concept and characteristics of a dot plot. Then we learned the basic process to create a dot plot. Next, we enhanced it with many advanced features, such as axis truncating and scaling, jittering, and the reference line. In the end, we compared it with other text table variations.
You can download this example workbook Text Table and Variations from Tableau Public.
In conclusion, I have drawn a mind map to help you organize and review the knowledge in this guide.
I hope you enjoyed it. If you have any questions, you're welcome to contact me [email protected]
If you want to dive deeper into the topic or learn more comprehensively, there are many professional Tableau Training Classes on Pluralsight, such as Tableau Desktop Playbook: Building Common Chart Types.
I made a complete list of common Tableau charts serial guides, in case you are interested:
|Categories||Guides and Links|
|Bar Chart||Bar Chart, Stacked Bar Chart, Side-by-side Bar Chart, Histogram, Diverging Bar Chart|
|Text Table||Text Table, Highlight Table, Heat Map, Dot Plot|
|Line Charts||Line Chart, Dual Axis Line Chart, Area Chart, Sparklines, Step Lines and Jump Lines|
|Statistical Charts||Scatter Plot, Box and Whisker Plot, Bullet Chart, Pie Chart, Packed Bubble Chart|
|Advanced Charts||Tree Map, Gannt Chart, Slope Chart, Pareto Chart, Map, Time Series, Burndown Chart, Dual Axis Combination Chart|
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