What is a scatter plot?

A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables.

The example scatter plot above shows the diameters and heights for a sample of fictional trees. Each dot represents a single tree; each point’s horizontal position indicates that tree’s diameter (in centimeters) and the vertical position indicates that tree’s height (in meters). From the plot, we can see a generally tight positive correlation between a tree’s diameter and its height. We can also observe an outlier point, a tree that has a much larger diameter than the others. This tree appears fairly short for its girth, which might warrant further investigation.

When you should use a scatter plot

Scatter plots’ primary uses are to observe and show relationships between two numeric variables. The dots in a scatter plot not only report the values of individual data points, but also patterns when the data are taken as a whole.

Draw a scatter plot with possibility of several semantic groupings. The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets. Scatter is an attempt at making the current scattering workflow of blender more Accessible, Powerful, Extremely Fast and Enjoyable for artists. Scatter will boost your productivity and creativity like never before.

Identification of correlational relationships are common with scatter plots. In these cases, we want to know, if we were given a particular horizontal value, what a good prediction would be for the vertical value. You will often see the variable on the horizontal axis denoted an independent variable, and the variable on the vertical axis the dependent variable. Relationships between variables can be described in many ways: positive or negative, strong or weak, linear or nonlinear.

A scatter plot can also be useful for identifying other patterns in data. We can divide data points into groups based on how closely sets of points cluster together. Scatter plots can also show if there are any unexpected gaps in the data and if there are any outlier points. This can be useful if we want to segment the data into different parts, like in the development of user personas.

Example of data structure

In order to create a scatter plot, we need to select two columns from a data table, one for each dimension of the plot. Each row of the table will become a single dot in the plot with position according to the column values.

Common issues when using scatter plots

Overplotting

When we have lots of data points to plot, this can run into the issue of overplotting. Overplotting is the case where data points overlap to a degree where we have difficulty seeing relationships between points and variables. It can be difficult to tell how densely-packed data points are when many of them are in a small area.

There are a few common ways to alleviate this issue. One alternative is to sample only a subset of data points: a random selection of points should still give the general idea of the patterns in the full data. We can also change the form of the dots, adding transparency to allow for overlaps to be visible, or reducing point size so that fewer overlaps occur. As a third option, we might even choose a different chart type like the heatmap, where color indicates the number of points in each bin. Heatmaps in this use case are also known as 2-d histograms.

Interpreting correlation as causation

This is not so much an issue with creating a scatter plot as it is an issue with its interpretation. Simply because we observe a relationship between two variables in a scatter plot, it does not mean that changes in one variable are responsible for changes in the other. This gives rise to the common phrase in statistics that correlation does not imply causation. It is possible that the observed relationship is driven by some third variable that affects both of the plotted variables, that the causal link is reversed, or that the pattern is simply coincidental.

For example, it would be wrong to look at city statistics for the amount of green space they have and the number of crimes committed and conclude that one causes the other, this can ignore the fact that larger cities with more people will tend to have more of both, and that they are simply correlated through that and other factors. If a causal link needs to be established, then further analysis to control or account for other potential variables effects needs to be performed, in order to rule out other possible explanations.

Common scatter plot options

Add a trend line

When a scatter plot is used to look at a predictive or correlational relationship between variables, it is common to add a trend line to the plot showing the mathematically best fit to the data. This can provide an additional signal as to how strong the relationship between the two variables is, and if there are any unusual points that are affecting the computation of the trend line.

Categorical third variable

A common modification of the basic scatter plot is the addition of a third variable. Values of the third variable can be encoded by modifying how the points are plotted. For a third variable that indicates categorical values (like geographical region or gender), the most common encoding is through point color. Giving each point a distinct hue makes it easy to show membership of each point to a respective group.

One other option that is sometimes seen for third-variable encoding is that of shape. One potential issue with shape is that different shapes can have different sizes and surface areas, which can have an effect on how groups are perceived. However, in certain cases where color cannot be used (like in print), shape may be the best option for distinguishing between groups.

Numeric third variable

For third variables that have numeric values, a common encoding comes from changing the point size. A scatter plot with point size based on a third variable actually goes by a distinct name, the bubble chart. Larger points indicate higher values. A more detailed discussion of how bubble charts should be built can be read in its own article.

Hue can also be used to depict numeric values as another alternative. Rather than using distinct colors for points like in the categorical case, we want to use a continuous sequence of colors, so that, for example, darker colors indicate higher value. Note that, for both size and color, a legend is important for interpretation of the third variable, since our eyes are much less able to discern size and color as easily as position.

Highlight using annotations and color

If you want to use a scatter plot to present insights, it can be good to highlight particular points of interest through the use of annotations and color. Desaturating unimportant points makes the remaining points stand out, and provides a reference to compare the remaining points against.

Related plots

Scatter map

When the two variables in a scatter plot are geographical coordinates – latitude and longitude – we can overlay the points on a map to get a scatter map (aka dot map). This can be convenient when the geographic context is useful for drawing particular insights and can be combined with other third-variable encodings like point size and color.

Heatmap

As noted above, a heatmap can be a good alternative to the scatter plot when there are a lot of data points that need to be plotted and their density causes overplotting issues. However, the heatmap can also be used in a similar fashion to show relationships between variables when one or both variables are not continuous and numeric. If we try to depict discrete values with a scatter plot, all of the points of a single level will be in a straight line. Heatmaps can overcome this overplotting through their binning of values into boxes of counts.

Connected scatter plot

If the third variable we want to add to a scatter plot indicates timestamps, then one chart type we could choose is the connected scatter plot. Rather than modify the form of the points to indicate date, we use line segments to connect observations in order. This can make it easier to see how the two main variables not only relate to one another, but how that relationship changes over time. If the horizontal axis also corresponds with time, then all of the line segments will consistently connect points from left to right, and we have a basic line chart.

Visualization tools

The scatter plot is a basic chart type that should be creatable by any visualization tool or solution. Computation of a basic linear trend line is also a fairly common option, as is coloring points according to levels of a third, categorical variable. Other options, like non-linear trend lines and encoding third-variable values by shape, however, are not as commonly seen. Even without these options, however, the scatter plot can be a valuable chart type to use when you need to investigate the relationship between numeric variables in your data.

The scatter plot is one of many different chart types that can be used for visualizing data. Learn more from our articles on essential chart types, how to choose a type of data visualization, or by browsing the full collection of articles in the charts category.

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Note

These visuals can be created and viewed in both Power BI Desktop and the Power BI service. The steps and illustrations in this article are from Power BI Desktop.

A scatter chart always has two value axes to show: one set of numerical data along a horizontal axis and another set of numerical values along a vertical axis. The chart displays points at the intersection of an x and y numerical value, combining these values into single data points. Power BI may distribute these data points evenly or unevenly across the horizontal axis. It depends on the data the chart represents.

You can set the number of data points, up to a maximum of 10,000.

When to use a scatter chart, bubble chart, or a dot plot chart

Scatter and bubble charts

Scattergories

A scatter chart shows the relationship between two numerical values. A bubble chart replaces data points with bubbles, with the bubble size representing an additional third data dimension.

Scatter charts are a great choice:

• To show relationships between two numerical values.

• To plot two groups of numbers as one series of x and y coordinates.

• To use instead of a line chart when you want to change the scale of the horizontal axis.

• To turn the horizontal axis into a logarithmic scale.

• To display worksheet data that includes pairs or grouped sets of values.

  Tip

  In a scatter chart, you can adjust the independent scales of the axes to reveal more information about the grouped values.

• To show patterns in large sets of data, for example by showing linear or non-linear trends, clusters, and outliers.

• To compare large numbers of data points without regard to time. The more data that you include in a sScatter chart, the better the comparisons that you can make.

In addition to what Scatter charts can do for you, bubble charts are a great choice:

• If your data has three data series that each contains a set of values.

• To present financial data. Different bubble sizes are useful to visually emphasize specific values.

• To use with quadrants.

Dot plot charts

A dot plot chart is similar to a bubble chart and scatter chart, but is instead used to plot categorical data along the X-Axis.

Scattered Crossword Clue

They're a great choice if you want to include categorical data along the X-Axis.

Prerequisites

This tutorial uses the Retail Analysis sample PBIX file.

1. From the upper left section of the menubar, select File > Open

2. Find your copy of the Retail Analysis sample PBIX file

3. Open the Retail Analysis sample PBIX file in report view .

4. Select to add a new page.

Note

Sharing your report with a Power BI colleague requires that you both have individual Power BI Pro licenses or that the report is saved in Premium capacity.

Create a scatter chart

1. Start on a blank report page and from the Fields pane, select these fields:

   • Sales > Sales Per Sq Ft

   • Sales > Total Sales Variance %

   • District > District

2. In the Visualization pane, select to convert the cluster column chart to a scatter chart.

3. Drag District from Details to Legend.

   Power BI displays a scatter chart that plots Total Sales Variance % along the Y-Axis, and plots Sales Per Square Feet along the X-Axis. The data point colors represent districts:

Now let's add a third dimension.

Create a bubble chart

1. From the Fields pane, drag Sales > This Year Sales > Value to the Size well. The data points expand to volumes proportionate with the sales value.

2. Hover over a bubble. The size of the bubble reflects the value of This Year Sales.

3. To set the number of data points to show in your bubble chart, in the Format section of the Visualizations pane, expand General, and adjust the Data Volume.

   You can set the max data volume to any number up to 10,000. As you get into the higher numbers, we suggest testing first to ensure good performance.

   Note

   More data points can mean a longer loading time. If you do choose to publish reports with limits at the higher end of the scale, make sure to test out your reports across the web and mobile as well. You want to confirm that the performance of the chart matches your users' expectations.

4. Continue formatting the visualization colors, labels, titles, background, and more. To improve accessibility, consider adding marker shapes to each line. To select the marker shape, expand Shapes, select Marker shape, and select a shape.

   Change the marker shape to a diamond, triangle, or square. Using a different marker shape for each line makes it easier for report consumers to differentiate lines (or areas) from each other.

5. Open the Analytics pane to add additional information to your visualization.

   • Add a Median line. Select Median line > Add. By default, Power BI adds a median line for Sales per sq ft. This isn't very helpful since we can see that there are 10 data points and know that the median will be created with five data points on each side. Instead, switch the Measure to Total sales variance %.

   • Add symmetry shading to show which points have a higher value of the x-axis measure compared to the y-axis measure, and vice-versa. When you turn symmetry shading on in the Analytics pane, Power BI shows you the background of your scatter chart symmetrically based on your current axis upper and lower boundaries. This is a very quick way to identify which axis measure a data point favors, especially when you have a different axis range for your x- and y-axis.

     a. Change the Total sales variance % field to Gross margin last year %

     b. From the Analytics pane, add Symmetry shading. We can see from the shading that Hosiery (the green bubble in the pink shaded area) is the only category that favors gross margin rather than its sales per store square footage.

   • Continue exploring the Analytics pane to discover interesting insights in your data.

Create a dot plot chart

To create a dot plot chart, replace the numerical X-Axis field with a categorical field.

From the X-Axis pane, remove Sales per sq ft and replace it with District > District Manager.

Considerations and troubleshooting

Scatter Crossword Clue

Your scatter chart has only one data point

Does your scatter chart have only one data point that aggregates all the values on the X- and Y-axes? Or maybe it aggregates all the values along a single horizontal or vertical line?

Add a field to the Details well to tell Power BI how to group the values. The field must be unique for each point you want to plot. A simple row number or ID field will do.

If you don't have that in your data, create a field that concatenates your X and Y values together into something unique per point:

To create a new field, use the Power BI Desktop Query Editor to add an Index Column to your dataset. Then add this column to your visualization's Details well.

Next steps

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