It will look different if you are using a PC, but the four boxes that are important should be the same: Name, X values, Y values, and Sizes (or Size of Bubble).
Then either leave the Table highlighted, or just click on any non-blank cell again. Tables make things easier to work with (a blog post about tables is coming next!). Step 1:Ĭlick on any non-blank cell in the dataset. If you want to understand PivotTables better, check out my blog post “PivotTable Basics”. If you aren’t familiar with PivotTables, you should still be able to follow these instructions to produce one. But since I want to aggregate the information by manufacturer, a PivotTable makes sense here. You don’t have to make a PivotTable in order to make a Bubble Plot (in fact, later in this post we’ll make a Bubble Plot without making a PivotTable). Let’s start by making a Table, then a PivotTable. For this first example, we’re going to have the bubble color represent a unique manufacturer of cereals. You can get a FOURTH variable into a bubble plot believe it or not! If you want to add a categorical variable (think of that as a text column in Excel) to the three quantitative columns, you can do it by making the color of the bubbles represent values from the categorical variable. We’re going to go with average sugar content for our bubble size.
However, generally, it’s a good idea to pick a variable that has a lot of variation if you can so that your bubbles aren’t all the same size and are actually showing something interesting. The size of the bubble can also be any quantitative variable, and it should be one that will be relevant when compared to the x and y variables. So, we’re going with average fat content for our y axis. Same as the x axis, any quantitative variable will do, but you’ll get more out of your visualization by picking a variable that you think is being influenced by the variable you chose for the x axis. So, I made average number of calories my x axis. In this case, I hypothesized that the higher the average number of calories, the higher the average fat content would be for each brand of cereal. Any quantitative variable will do, but you can get more out of your visualization by picking a variable that you think is influencing the y variable.
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