How to make dynamic waffle charts in Excel

Waffle charts can add visual appeal to a report. They seem to be popping up everywhere lately! I recently used them in a report and I wanted to share my process in order to show you how easy they are.


Once you break these charts down you can see that they are just 10×10 grids. And we all know that Excel, er, excels at working with grids.

First off, type out your data in two columns. You can see that I have values for Group A, Group B, and Group C. Then you are going to make some 10×10 grids (since I have three groups, I made three grids). These grids will contain the values 1-100%.


Next we are going to resize the grids. Highlight all of the columns in your grid and drag your mouse to resize. The default line height in Excel is 20 pixels so I like to make my cells 20 pixels wide so that things are perfectly square.


At this point I make my number font really tiny (as in size 6) so that it fits into the cells.

Change your background and text color on the grids to whatever you want your default color to be. I chose a light grey.


Now we are going to change the color of the borders. I find white too harsh of a contrast so I like using a very light grey. Changing border color is a bit quirky. First highlight the columns with your grids. Go to the border button and go down to line color and select the color that you would like.


Your cursor will turn into a little pencil and you will see little black dots in your grids. Higlight the columns that contain the grids again and press the border button. At this point the border colors should be changed.


Next we will use the magic of conditional formatting to fill in our grids.  Highlight the first grid and go to Conditional Formatting and click New Rule.


Next select “Format only cells that contain” and select “less than or equal to” and then select the cell that contains the actual value for Group A. This is telling Excel that you want to change the color of every cell in the grid that is less than or equal to the actual score.


Go to “Format” and change the background fill color AND the font color to whatever color you would like for your group.

Repeat these steps for all of your grids. When you are finished you will have something like this:


Alright, now we’re getting somewhere.

Next we’re going to do some extra Excel kung fu to make pictures from these grids that we can paste anywhere in our workbook (such as a front sheet that you are using to summarize your results).  Not only this, the picture will automatically update if you change your data.

Highlight all of the cells in your first grid and copy. Right click wherever you want your waffle chart to be. Right click, go to paste special, and then go to the little picture with a link on it. This creates something called a linked picture.


You can easily move this picture around on the worksheet and resize it. If you change your data in the previous worksheet, where we set up our grid, the picture will automatically update. Neat, right?

Once you have created linked pictures for all of your waffle charts let’s add some labels so that we can easily tell what scores the charts are representing.

Go to the first waffle chart and insert a text box. Change the fill and outline of the text box to ‘none’. Make sure the (empty) text box is selected. Go up to the formula bar, type = and then navigate to where the score for that group is stored, click that specific cell, and hit enter. Your text box should now contain that group’s score.


Like the linked picture, if you change the group’s score, the text box will automatically update to reflect this.

Repeat the steps above to label your other waffle charts. Tweak the formatting to make the text boxes easy to read. Add a headline that tells the reader the main insight from the charts and there you go, you’re done!


Before you get too excited there is an important downside to waffle charts: They take up a lot of space. Each waffle grid is essentially showing one data point. That is a lot of real estate for one data point!

Let me know your thoughts on waffle charts – Love them? Sick of them? Other thoughts?

Non-linear relationships: The importance of examining distributions

Recently I was analyzing some data to help answer the question “what are the demographic differences between program graduates and program drop outs?” I did some modelling and found a few predictors, one of which was age.

I compared the average age between the groups and saw that the drop outs had a lower average age (42 years) than graduates (44 years). Simple enough. But this simplistic explanation didn’t jive with anecdotal information the program staff had given me. I wondered if the relationship between age and program completion was linear (i.e., does a change in age always produce a chance in the likelihood of graduating).

As I mentioned in my last post, I’ve been playing around with R. I recently came across something called a violin plot and I wanted to try it out. A violin plot is kind of like a box plot, except that instead of a plain old box it shows you the distribution of your data.

Here is an example of a box plot:


The main thing that I immediately see from this chart is that on average, the drop outs were younger than the graduates.

Here is an example of a violin plot:


I get a different takeaway from this plot. You can see from the violin plot that the distribution of age for the drop outs looks a lot different than the distribution of age for the graduates. The bottom of the drop out violin is wider, indicating that the drop outs skew a lot younger than the graduates. This indicates that we should be exploring the relationship between age and graduation more closely.

But what if you don’t use R and can’t create a violin plot? Histograms are standard tools to show distributions and are much more common. A histogram is essentially a column chart that show the frequency of values in your distribution (so for this example, it would show how many participants were 20 years old, 21 years old, 22 years old, you get the idea). Excel actually has a built in feature to create histograms (click here for instructions). The tool bugs me a lot and it isn’t super intuitive to use, but it gets the job done.

Here is the distribution for age for both the drop outs and graduates. Yes, yes, I know that my x-axes aren’t labelled and that my y-axes use different scales but these choices were intentional because I want you to focus on the shape of the distributions, not the content.


Again, you can see that the age of the drop outs skews to the left (meaning that there is a higher proportion of younger participants than older). The histogram for the graduated group looks quite different.

All of this evidence points to a non-linear relationship, meaning that age has an effect on whether or not a participant graduates for participants in different age groups.

To take a closer look at this relationship, I calculated the drop out rate for different age groupings and put them on a line chart. Aha! If the relationship between age and program completion was linear, we would expect this line to be straight. But it’s not. You can see that the drop-out rate declines with age until we hit age 40 or so. After that it’s more or less flat until age 70, and then goes down again.


This is an important piece of knowledge for program staff to target retention efforts and something that we wouldn’t have uncovered if we simply had stopped at comparing the average age between the drop-outs and the graduates.

Showing two main points on one chart

It’s (usually) fairly straightforward to choose a chart type when you know what the main point you are trying to get across is. Is your message that there has been a change over time? Do you want to show a difference between groups? There are all kinds of online chart choosers to help you do this (here is one of my favourites). But what about when you have two main points to make?

I was recently working on a chart where I wanted to make the following two points:

  1. 2016 was the only year that participants had a statistically signifcant increase in health ratings; and
  2. participants had lower health ratings pre-program in 2016 vs. other years

I started with the chart below. Here the different color used in 2016 really highlights that something different happend that year (half of point #2), but it is difficult to see the change over time (point #1, half of point #2):


Alright then, let’s change to a line graph. It is much easier to see the change over time. However, the statistical change in pre- and post-test scores was important to the program and they wanted to highlight that. That piece of information isn’t easy to see here.


I added a transparent rectangle to highlight the difference between pre- and post-test scores and this is the result:


I think that this chart nicely conveys the two main points that I wanted to make and is a vast improvement over the first chart. It also goes to show that it’s worthwhile to play around with different chart types while working on reporting!

Note: I have changed the results to fictional data to keep things anonymous