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Scatter charts use small dots to represent values of two different numeric values, such as height and weight, width and length, or similar metrics. This chart is great at showing the relationship between the two variables, allowing users to easily understand patterns and correlations in the data.
You can go one step farther, configuring a Group By property that applies color to the displayed dot. This is fantastic for quickly identifying which groups appear in particular locations across the range.
When should I use a Scatter Chart?
Scatter charts are an ideal choice for comparing large numbers of data points without regard for time. Typical uses of this powerful chart type involve highlighting the relationship between to variables (an X and a Y) - for example, a person's height versus weight, price versus square footage of homes, and similar sets of related data.
See the section Scatter Chart Examples below to see it in action. They highlight situations where Scatter may be a good choice to display your large data sets.
Avoid using scatter charts when you're examining values that add up to a fixed number, as Pie charts really shine for that type of work. You may also consider other chart types if you are not comparing a relatively large number of data points - sparsely populated scatter charts do not give viewers a good sense for correlation in the data. If your data has a periodic component you might want to investigate Bar, Column or Line charts as well.
Configuring Scatter Chart Saved Views
Configuring a Scatter chart is easy with the SmartSuite Chart Setting panel. Just follow these easy steps:
Add a new Chart Saved View (see this article to learn how)
Select Scatter Chart Type
Specify the chart's Values
Pick a Group By field
Scatter Chart Type
Select scatter chart under Chart Settings to get started with your configuration.
Specifying Scatter Chart Values
The Values configuration section allows you to pick which field or fields contain the data you want to display on your chart. Scatter charts have two configurable Values, one for the X-Axis (horizontal values) and one for the Y-Axis (vertical values). All of the numeric type fields in your app (Number, Currency, formulas that output a number, etc.) are available for selection.
Group By has a special function in Scatter charts - it lets you color code your displayed data points! Simply select the field you want to group by (list-type fields like single select, date fields, and more are available) and your data will be identified by the selected value, like this:
Configuring Scatter Chart Widgets
Scatter charts are great on their own, but even better on a SmartSuite Dashboard!
All of the configurations aren the same as we've described above, with a handful of minor differences:
Widget Name: Name the Column Chart - this text will be displayed in the widget's border to let people know what data they're looking at.
App: Saved Views are configured in the context of a specific App, but widgets live at the Solution level. Start your configuration by picking which App contains the data you want on your chart.
Once you have configured your chart parameters to your liking, click Add Widget to place it on your Dashboard. That's all there is to it!
Scatter Chart Use Cases
Here are a few charting use cases to get you thinking about Scatter chart and when it can be the best chart type choice.
Use Case: Large Number of Values
Scatter charts excel at making a large number of data points tell a story. You can see the correlation between the X and Y axis values at a glance. We've taken the height versus weight dataset we were looking at before and added several hundred additional data points - you can really see how the groupings become distinctive with this volume of data:
Use Case: Identifying Outliers
In statistics an outlier is a data point that differs significantly from other observations. These anomalous data points can indicate a variety of things - problems in sampling, unique circumstances that created a different outcome, or they might point at something that is interesting and merits investigation. Scatter plots are particularly good at highlighting outliers - the chart below makes it clear that the core data set results in a grouping, with 3 anomalies that might need investigation: