A scatter plot is a data visualization that displays the values of two different variables as points. The data for each point is represented by its horizontal (x) and vertical (y) position on the visualization. Additional variables can be encoded by labels, markers, color, transparency, size (bubbles), and creating 'small multiples' of scatter plots. Scatter plots are also known as scatterplots, scatter graphs, scatter charts, scattergrams, and scatter diagrams.

Don't forget you can create a scatterplot for free using Displayr's scatterplot maker!

In the example below, each dot represents a diamond, with the horizontal position showing the carat (size) of the diamond and vertical position showing the price of the diamond.

## Required data

A scatter plot displays data for a set of variables (columns in a table), where each row of the table is represented by a point in the scatter plot. The variables can be both categorical, such as Language in the table below, and numeric, such as the various scores assigned to countries in the table below.

## Main variants of scatter plots

### Labeled scatter plots

A *labeled scatter plot *uses text to identify each point of data, as shown below.

### Bubble charts

A *bubble chart *shows additional information by the size of the circle. In th example below, the area of each circle indicates the quality of the cut (bigger circles have higher cut qualities), and color indicates the clarity of the diamond, with yellow bubbles having superior quality. As with the scatter plot at the beginning of this post, we can see that larger carat diamonds have higher prices, on average. However, we can also see that the price among diamonds of similar carats, those with higher clarity get higher prices (i.e., the yellow bubbles are generally higher than the reddish bubbles).

**Create your own Scatter Plot!**

### Small multiples, including scatter plot matrices

A *small multiple* of scatter plots is a set of related scatter plots shown in a table. Most commonly, this is a **sc**atter **pl**ot **m**atrix *(SPLOM), *where each plot shows a correlation between a pair of variables. The SPLOM below shows the numeric data from the table earlier in this article. The distribution of each of the numeric variables is shown by the histograms in the *main diagonal *of the plot. The correlations are shown above the main diagonal and the scatter plots below the main diagonal.

### Scatter plots with smoothers (lines of best fit)

Lines of best fit are often added to scatter plots to make it easier for the viewer to discern the average relationship between the *x *and *y *variables. Where these lines are permitted to be curved, they are known as *smoothers.* In the example below, a confidence band has also been shown to indicate the uncertainty regarding the estimate of the line of best fit.

## The overplotting problem

The *scatter plot *below shows age by income. This visualization exhibits the telltale sign of *overplotting*, which is that the data appears in neat rows and columns. There is no way to determine from this visualization if, say, there is only one person aged 60 with income of $50,000 or more. See "What is Overplotting" for a discussion of different causes and remedies for overplotting.

## Software for scatter plots

The scatter plot is one of the most widely used data visualizations. It can be created by almost every data visualization software package. The scatter plots in this post have all been created using Displayr.

**You can make your own scatter plots in Displayr, or check out the rest of our Beginner's Guides!**