small multiples

Communicating data effectively with data visualization – Part 17 (Multivariate Dimensions)


Data visualizations can improve how we see complex data, in particular, where multiple dimensions are involved. For instance, in an X-Y plan, we can have the months on the X-axis and the number of patients on the Y-axis (Figure 1). Let’s imagine that number of patients represents some outcome you are interested in (e.g., number of patients who has 5+ prescription medications). Time and the number of patients are dimensions on this two dimensional plan.


Figure 1. Two-dimensional X-Y axes figure.

Figure 1.png

As a rule, whenever you want to display multiple dimensions, each dimension needs to be represented onto a single figure, which is challenging given that a figure is normally on a two-dimensional plane. What if we wanted to have a figure with more than two dimensions? What if we wanted to have a figure with months on the X-axis, number of patients on the Y-axis, and include a third dimension denoting different genders? How would we go about doing that? Figure 2 illustrates how we can do this by adding lines and labeling them using different colors.


Figure 2. Figure with three dimensions.

Figure 2.png

Figure 2 is able to capture three dimensions of data into a single two dimensional figure. The number of patients is captured in the Y-axis and the time in months is captured in the X-axis. Gender is represented by the colored lines that show the difference in the relationship between number of patients and time associated with males and females.

Alternatively, we use the color blue for the following conditions:

E[Y | male]


We use the color red for the following conditions:

E[Y | female]


The legend providers additional clarification that the different line colors denote the gender types. It is critical to include clear and intuitive legends so that your readers will immediately recognize their reference and label. Without a legend, your audience will have to guess what color belongs to what gender type.

How about adding another dimension such as age? This would increase the number of dimensions on this figure from three to four. For example, what if we wanted to see how being older (80+ years) impacted the relationship between the number of patients and time across genders? Well, this can be accomplished by using different types of lines (e.g., dotted lines and dashed lines).

Figure 3 illustrates how using different types of lines (dotted for the 80+ year old patient and dashed for the <80 year old patient) can provide a visual accounting of the differences across genders and across age in terms of the number of patients and months. The legend provides additional clarification as to the age groups associated with the different line types.


Figure 3. Figure with four dimensions.

Figure 3.png

Alternatively, we continue to use the color blue for the following conditions but add different line types for the age groups:

E[Y | 80+years & male] (dotted lines)

E[Y | <80 years & male] (dashed lines)


Similarly, we continue to use the color red for the following conditions but add different line types for the age groups:

E[Y | 80+years & female] (dotted lines)

E[Y | <80 years & female] (dashed lines)

Using colors and line types allow us to capture multiple dimensions onto a two dimensional figure. We essentially are showing a stratified descriptive analysis of the age groups nested within each gender and their relationships between the number of patients and time (months).

How about adding a fifth dimension? How could one do that?

A simple way to introduce a fifth dimension is to use the concept of small multiples by Edward Tufte.[1] Tufte uses small multiples to include additional dimensions. Figure 4 illustrates how we can leverage small multiples to look at the differences in the relationships between number of patient and time for different genders and age groups across different states.


Figure 4. Small multiples of states with differing patterns of number of patients and time for different genders and age groups.

Figure 4.png

Using small multiples allow us to compare the differences in the association between number of patients and months across different states stratified by gender and age groups. The number of patients increased across time for all gender and age groups in California. Similar patterns are observed in Virginia, but the rate of increase in the number of patients across time is lower in the female group and age cohorts. In Ohio, different patterns are observed compared to California and Virginia. Males and their associated age groups have a decreasing number of patients across time. Conversely, females and their age cohorts have a positive correlation between the number of patients and time.



Adding dimensions can improve the figure you design by incorporating complex relationships across different data characteristics. In our example, we demonstrate how we can integrate dive dimensions of data to a two-dimensional figure that tell us information about the association between the outcomes (number of patients) with time (months) across states stratified by gender and age groups. Be creative with how you integrate multiple dimensions into a figure. Ask yourself if this is something that will help improve the story the figure is conveying. There are times when a simple figure will do. But when you have a lot of data and want to tell a story, consider adding dimensions to the figure to get a narrative that will excite and capture your audience’s attention.



  1. Tufte ER. The Visual Display of Quantitative Information. Second. Cheshire, CT: Graphics Press, LLC.; 2001.

Communicating data effectively with data visualizations - Part 7 (Using Small Multiples or Panel Charts in Excel)


It can be challenging when you’re trying to visualize many different groups using the same metric repeatedly. Initially, you may want to do this with a single figure, but this is too crowded and prevents you from seeing the differences across the groups. Alternatively, you can separate and visualize the groups individually without compromising the space or size of the figure. In this article, we will discuss the use of small multiples or panel charts in Excel. This will make it easier for the eyes to see the differences while presenting a large number of data.



I have created a data set that contains the average temperature (degrees F) across four quarters for eight states in the United States. These temperatures were generated using a random number generator.

The data has the following structure:

Figure 1.png

Using this data, we can generate a single plot that contains all the states and their average temperature for each quarter.

Figure 2.png

There are several issues with this plot. First, there is so much clutter, it is difficult to discern which states are increasing or decreasing over time. Second, the colors, despite being different, seem to mix in too much like spaghetti. In fact, this is commonly called a “spaghetti” plot.[1]

To avoid this cluster of tangled lines, it is best to view these lines separately in small multiples. We can apply the principle of small multiples into an 8 by 1 matrix (8 rows and 1 column of data). 

Figure 3.png

By separating each state into its own separate line, we can identify which states had temperatures that increased across time. From this figure, we can see that Alabama, Alaska, Arizona, and Kansas have temperatures that increased from Q1 to Q4. However, the magnitude in the change for Kansas looks similar to Alaska (absolute difference of 37 degrees F and 76 degrees F, respectively). Despite using the same Y-axis, the compressed scale gives the illusion that all the changes were similar in magnitude, which they were not.

We can improve upon this figure by furthering the use of small multiples. Tufte argues that small multiples can present a large amount of comparisons through repeated panels.[2] With this in mind, we can take the above figure and separate each state into a 2 by 4 matrix.

Figure 4.png

This is vastly improved. We can see trajectory for each state across the quarters. For example, we can clearly see that Alabama, Alaska, Arizona, and Kansas have temperatures that were increasing steadily across time. However, large fluctuations in temperatures were observed for New Jersey, New York, and Oklahoma. Only California appears to have a steady trend in the temperature across time.

The panel chart has equal sized boxes with temperature scales that are identical. This allowed our eyes to make quick comparisons between the states. Moreover, we can also identify the trends much more easily that in the “spaghetti” plot.



To generate the above panel chart, we will use the randomly generate temperature dataset, which can be downloaded from here.

An important element of the dataset is the column called “strata.” This column will allow us to generate a staggered line plot separated by the strata.

The following figure illustrates the different variable names including the strata column.

Figure 5.png

Notice how the strata column alternates between 1 and 2? This will be used to separate the temperatures of each state into clusters. In other words, we will cluster the temperature from Q1, Q2, Q3, and Q4 for each state.

Once you’ve reviewed the data, select all the data and then use the pivot table feature to insert the new table onto a different worksheet.

Figure 6.png

Excel will automatically create a new worksheet with a blank pivot table work space. You can use this work space to generate different tables using the powerful pivot table features.

Figure 7.png

We will use the pivot table builder to generate the table we need for the panel chart. After you insert the pivot table into a new worksheet, move the State variable into the Row box followed by the Quarter variable. (This is denoted by A.) Move the Temperature variable into the Values box. (This is denoted by B.) Then move the Strata variable into the Columns box. (This is denoted by C.) Review your work with the following figure below.

Figure 8.png

Once you set up you pivot table, you should notice that the temperature values are staggered between the states. This is important for when you construct the line graphs for the panel charts.

Next, we will remove the Grand Total column. This isn’t important for us, so let’s right click the cell that contains the column header Grand Total and select Hide.

Figure 9.png

Then click on the Design tab on the ribbon and select Report Layout > Show in Tabular Form. This will change the design of the default pivot table.

Figure 10.png

Next, we are going to remove the total rows for each state. Right click on the first state (Alabama) and select Hide. You should notice that the total row for each state is now hidden. This updated pivot table design will make it easier to create panel charts.

Figure 11.png

You pivot table should look like the following:

Figure 12.png

Copy and paste this table and its values onto a blank section of the worksheet. Then highlight the data for the first four states and insert a line chart.

Figure 13.png

You will see the trend lines for the four states, which will be separated by the different time periods.

Figure 14.png

There are two colors for the alternating states. Change this to a single color (e.g. Blue). Then remove the gridlines, chart title, and the legend. The chart should look like the following:

Figure 15.png

The next part will be to include line partitions between each state, which will allow the eye to distinguish each line as separate trends for the states.

We will need to create a new dataset for the line partitions. To do that, count the number of time periods for one of the states (the time periods should be equivalent for all the states. In our case, there are four quarters for each state). In between Q4 and Q1, there is a gap. If you take all the quarters from Q1 and Q4 for Alabama to California, there are a total of 16 intervals. In between interval 4 and 5 is where we want to put first line partition. Interval 8 to 9 is where we want to put the second line partition, etc. For this example, the dataset should look like the following:

Figure 16.png

Select the chart and click on the Chart Design tab and click on Select Data.

Figure 17.png

The Select Data Source window will appear and list the previous data already used to generate the trend lines for each state. We will add a new dataset (A) and select the Y values from the dataset that was generated for the partitions (B). 

Figure 18.png

This will create a line at the bottom of the chart area.

Figure 19.png

Right click on the line (A) and select Change Chart Type > X Y (Scatter) > Scatter (B).

Figure 20.png

Right click on the scatter and go to Format Data Series > Series Options > Plot Series On > Secondary Axis.

Figure 21.png

Select the Y-axis on the right side of the chart (A).

Figure 22.png

Then go to Format Axis > Axis Options and set the Maximum to 1.0.

Figure 23.png

Next, we will align the scatter points into the correct partition. Right click on the chart area and click on Select Data. Select Series 3 and go to the X values box. In the X values box, select the data in the Partition column.

Figure 24.png

The scatter points appear to be in the correct location. The next steps will involve including error bars for the partition lines and formatting.

Figure 25.png

Click on scatter points and select Chart Design in the Ribbon. Click on the Add Chart Element and select Error Bars > More Error Bars Options….

Figure 26.png

Format the error bars and remove the cap. Depending on where the scatter points are, you want to choose error bars that will cover the empty region. In our example, the scatter points are at the top, so having the “Minus” or “Both” direction options will work for us. We will leave it at “Both” for this example.

Figure 27.png

Click on the horizontal error bars and delete them.

Figure 28.png

Click on the scatter point and go to the Format Data Series. Go to the Fill & Line > Marker Options > None.

Figure 29.png

The chart should look like the following:

Figure 30.png

The remainder of this tutorial will be to make this aesthetically appealing.

We want to hide the secondary Y-axis, which can be done by hiding the line and change the font color to match the background. Then we want to change the size of the chart using the Chart Options.

Figure 31.png

We also changed the font to Helvetica (native to Apple products), resized the chart boxes, included a Vertical Y-title bar for the temperature axis, added a mean Temperature band using 60% transparency, change the color of the vertical error bars to a lighter gray, changed font size on the Y-axis, and moved the X-axis labels to the top. We repeated these steps for the final four states and carefully placed it below the first four states, using the grid to assist with alignment.

The final panel chart:

Figure 32.png



When you encounter a data set that can be separated into smaller graphics, consider using the small multiple principle or panel charts approach. It requires a little more work, but the results can provide a narrative that is easy to visualize and interpret. In our example, we start to notice different trends using different approaches to the small multiple principle. It’s best to experiment which ones work best for your needs. Here are some examples of small multiples that may inspire you: link1 and link2.



1. Knaflic CN. Strategies for avoiding the spaghetti graph. Storytelling with data. Published March 14, 2013. Accessed May 16, 2018.

2. Tufte ER. The Visual Display of Quantitative Information. Second. Cheshire, CT: Graphics Press, LLC.; 2001.