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12. Scatter and Bubble Plots
Scatter and bubble plots can be useful when you want to study the relationship between two or three numeric variables. We’ll learn how to create them in this lesson.
- Scatter charts are used to analyze the relationship between two numeric variables
- A scatter plot consists of a series of dots
- Trends or patterns in these dots can indicate the nature of the relationship between the two fields
- Bubble charts are used for the same purpose as scatter charts
- With a bubble chart, each dot can vary in size according to the value of another variable
- This allows three numeric fields to be analyzed on a single plot
In this lesson, we'll look at how scatter and bubble plots can help users see the relationships between variables.
In our data set, we may want to know how the relationship between revenue and number of users varies between sales people.
Are some sales people bringing in considerably more revenue per user than others? A scatter plot makes it easy to find out. We'll start by creating an empty scatter plot.
We can see that the available wells are slightly different than what we've used so far.
To construct the plot, we'll start by selecting variables for the X and Y axes.
We'll put revenue on the X axis and users on the Y axis.
This produces a single dot.
To see a dot for each sales person, we'll drag sales person to the details well.
As with the previous lesson, we'll exclude the two lower performing sales people, Barcus and Stafani, by creating a page level filter.
We can now see the remaining sales people much more clearly.
The linear pattern to these dots suggest that revenue and users are fairly strongly correlated and there are no sales people getting a particularly high or low price per user.
Let's take a moment to format this chart and make it a bit easier to read.
We'll select the format icon and start by insuring that fill point is turned on.
This makes the points solid so that they're easy to see.
We'll also turn on category labels so we can see which sales person is represented by each bubble.
On a graph with more data points, this can create a lot of clutter, but we have few enough data points here that it looks reasonable.
We'll also turn on color by category so each sales person gets their own color.
What we currently have here is a scatter plot.
In a scatter plot, each dot is the same size.
If we select the fields icon again, we can see the size well. The size well is specific to the scatter chart type and allows each dot to be a different size, activating this well changes this scatter plot into a bubble plot.
Let's drag company name to the size well.
Each bubble is sized according to the number of companies its sales person has sold to.
The size variable is not particularly useful here as the bubbles are all of a similar size. We can reasonably conclude that each sales person has sold to a similar number of companies. However, with the right data, a bubble plot like this can provide an appealing way to view the relationship between up to three variables.
If we look at the other available wells in the visualization pane, we can see one labeled play axis.
Let's drag our date field to this well.
A play button now appears at the bottom left of the chart. When we click play, we can see an animated plot for each date in our data set.
We can even click on an individual bubble to track its progress over time.
It doesn't always makes sense to include a play axis.
In this case, it's not ideal as our data is collected on a daily basis and not every sales person makes a sale every day.
As a result, bubbles constantly appear and disappear.
However, in the right circumstances, this can be an interesting way of tracking a variable over time.
This marks the end of our first course on visualizations in Power BI.
In the next course, we'll look at the remaining visual types including maps, waterfall charts, and carts. We'll also look at more options for modifying visuals and the data used to create them, including methods of aggregation, quick measures, and drilling down into visualizations.
For now, you should have a good grasp of the most common visuals featured in Power BI and how to use them in your reports.