December 18, 2017

Special thanks to Shaelyn McCole at Hootology who suggested the topic, Ryan Gensel at CPI who came up with a wonderful enhancement to the connected dot plot, and Jeffrey Shaffer at Data Plus Science who suggested some cosmetic improvements.

## Overview

If you have filters in your dashboards you’ve probably had a thought similar to this: yes, it’s great that I can filter the results, but what did the dashboard look like before I applied the filter?  I can’t remember if the bars were smaller or larger, let alone by how much.

Consider the example below that shows the results to a multi-select survey question were we see results from all respondents that answered a set of questions.

Figure 1 — Results from all survey respondents.

Now compare this with results with women from North America.

Figure 2 — Results from female respondents who live in North America.

Unless you process information and memories differently than the vast majority of people it’s very hard to compare the two populations and harder still if you can only see one result at a time (here you can at least scroll up and down to compare).

So, is there an easy way in Tableau to display both filtered and unfiltered results in the same visualization?

The answer is a resounding yes, with a refreshingly straightforward Level-of-Detail (LoD) calculation.

## Determining the calculations

Let’s first look at the calculation we need to determine the percentage of people that checked “Yes” to a question. The “pill arrangement” in Tableau is shown below.

Figure 3 — Pill arrangement for a check-all-that-apply survey question.

Notice that we have Wording on the rows shelf. The Question Grouping filter is limiting the results to only those rows that have responses for the survey questions that interest us. Notice also the filters for Gender, Generation, and Location.

Here’s the calculation that determine the percentage of people that selected an item.

`SUM([Vales]) / SUM([Number of Records])`

As the survey has been coded so that folks that selected an item have a Value of 1 and those that did not select an item have a Value of 0, this calculation add up all the ones and divide by the number of people that answered the question.

Okay, so we have the calculation that will adjust the bars as we change the settings for the demographic filters.

What is the calculation that will keep the bars the same length even if we change the demographic filters?

All we need to do is lock the results to the Wording field using the following LoD calculation:

`{FIXED [Wording]: SUM([Value])/SUM([Number of Records])}`

This tells Tableau even though there may be filters, ignore them fix this to all the rows associated with the [Wording] field.  For those of you wondering, you could also fix this to the [Question ID] field and you would get the same results.

So, does this work?

Consider the pill configuration below where we can compare, one below the other, the results for the Filtered population and the Entire population. When all options in the demographic filers are checked, the bars are the same length.

Figure 4 — Filtered vs. Unfiltered results.  Right now, with everything selected, the bars are the same length.

Now let’s see what happens when we apply some filters (keep you eyes on Adrenaline Production for the Entire Population, currently at 76%).

Figure 5 — One set of bars changes but the other remains fixed

Notice that one set of bars change, but the bars for the Entire Population (the ones using the LoD calculation) don’t change.

## A gap chart… with a twist!

I experimented with several ways to visualize the differences and settled on a gap chart (also called a dumbbell chart, also called a connected dot plot). Let’s see how to build the chart (and how to improve it, using color coding that Ryan Gensel suggests).

Figure 6 — Dot plot using Measure Names and Measure Values

This is very similar to the bar chart we had earlier but instead of bars we are using circles and we’ve placed Measure Names on Color so the circles are different colors.

We now need to create a line that connect the two dots on each row.

First, we will duplicate the Measure Values pill that is on columns and have two identical charts, side-by-side, as shown here.

Figure 7 — Duplicating Measure Values

Next, for the second instance of Measure Values we will change the Mark type to Line and move Measure Names from Color to Path, as shown below.

Figure 8 — Making the second instance of Measure Values a Line chart, connecting the two measures by placing Measure Names on Path

Now we’ll right-click the second Measure Values pill and select Dual Axis, then we’ll right-click the secondary axis and select Synchronize Axis, then we’ll right-click the secondary axis again and de-select Show Header, giving us a chart that looks like this.

## Making the difference stand out

I was chatting with CPI’s Ryan Gensel, one of he authors of the Agency Utilization Dashboard that appears in The Big Book of Dashboards.  Ryan told me about a technique he was using that colors the line based on which dot has the larger value.

Here I apply the technique by creating a field called Line Color which is defined as follows.

```IF [Filtered -- % Check all that apply]>
SUM([Entire Population -- % Check all that apply]) THEN
"Filtered"
ELSE
"Entire Population"
END```

We can then place this field on Color for the Line chart instance of Measure Values.  We will also need to make the size of the line a little thicker by clicking the Size button and moving the slider to the right. This yields a chart that looks like this.

Figure 10 — A better gap chart.

I’ve embedded the working dashboard below.  Please feel free download and explore the dashboard as well as some alternative approaches saved in the workbook.

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November 25, 2017

Special thanks to Jeffrey Shaffer, Andy Cotgreave, and Rody Zakovich for feedback that helped improve the dashboard that appears at the end of this post.

## Overview

It seems I’m not the only person who has been thinking about stacked bar charts (see posts from Cole Nussbaumer Knaflic, Jonathan Schwabish, and Andy Cotgreave.)

My problem with these charts, and their first cousin, the area chart, is that the many people who design them don’t understand the possible pitfalls and end up creating charts that are attractive but that don’t convey a lot of useful information.

In this blog post we’ll see examples of where stacked bar and area charts work, where they fail, and what you can do to add some functionality to your dashboards so that if you do use stacked bar and area charts they will work better.

## The people who market data viz tools love these charts

Some of the chief culprits include the data visualization vendors themselves who sometimes fashion “screaming cat” visualizations like these in their marketing materials and promotions.

Figure 1 — Sample dashboard Tableau uses to showcase its extensions API.

Figure 2 — Microsoft PowerBI dashboard.

I’ll admit the last one looks particularly cool, but do you have any inkling what it’s trying to show you?

Before we get into exactly what’s wrong with the charts (and how to fix them) let’s look at a couple of examples that work very well.

## Some good examples

Here’s an example from The Big Book of Dashboards’ Complaints dashboard.

Figure 4 — A portion of the Complaints Dashboard showing open, closed, and overall complaints (Dashboard by Jeffrey Shaffer).

With this chart it’s very easy for me to see the total number of complaints (overall length of blue bars plus red bars) as well as compare the number of open complaints (red bars) because there are only two colors and the items I want to compare are open complaints (red) and total complaints (red plus blue), both of which have a common baseline.

Another example comes from Matt Chambers’ Mayweather vs. McGregor fight analysis dashboard.

Figure 5 — Stacked bar chart comparing overall punches and punches that landed between Mayweather and McGregor

You should check out the complete dashboard, but this stacked bar chart gets to the heart of why Mayweather won the fight: McGregor exerted more effort in launching 430 punches vs. Mayweather’s 320, but far fewer of McGregor’s punches landed (111 to 170).

As you consider why this chart is so effective notice that we only care about two things — the punches that landed and the total number of punches.

So, why do I like these two examples, but cite the earlier dashboards as “screaming cats”? It has to do with how many segments there are, and which segment is along the baseline.

Let’s explore a bit.

## Understanding the strengths and weaknesses of stacked bar charts

Consider the chart shown below.

Figure 6 — Typical stacked bar chart. We can make accurate comparisons of overall and of the first category (Central), but nothing else.

I can see that Phones has more sales overall (1), that Chairs is the biggest seller in the Central region (2) and the Bookcases is the lowest seller in the Central region (3).  If that’s all that is important then we may be all done here (although it is hard to see that Bookcases is in fact less wide than Machines… more on that in a moment.)

But suppose I want to know what were the three lowest selling categories in the Central region, or if I wanted to easily compare sales in the East or West? In these cases this visualization isn’t much help and *that’s* the biggest problem with stacked bar and area charts: You can only accurately compare overall values and the one region that hugs the baseline.

## Adding functionality — sorting and focus

Let’s address the “what were the three lowest sellers in the Central region?” question first. One way to do this would be to have a widget on your dashboard that allows you to sort by both total sales and by sales for a particular region. Here’s what the sort would look like for the Central region.

Figure 7 — Bars sorted by Central region. Now it’s easy to see which where the top and bottom sellers in that region.

Ah, now we can easily answer the question “what were the bottom three sellers in the Central region?” They are Accessories, Machines, and Bookcases.

This is great if all you care about is the Central region, but suppose you wanted co compare sales in the South?  With the way the chart is configured above this is very difficult, but if you add a “widget” that allows your audience to select a region to focus on, the chart can easily answer the question.

Figure 8 — Adding some functionality to the visualization so the audience can move a selected region to the baseline and sort by that region.

The “Focus on” parameter allows the user to select which region gets placed along the baseline and the “Sort Bar Cart by” parameter allows the user to sort either by the Selected region or by overall.

But, if what we’re interested in is showing how one region compares with itself and overall, why bother to have the other regions as different colored bars?  That is, why not make the two things we care about — overall and the region in question — stand out more?

## Highlighting the selected region

My fellow author Jeff Shaffer suggested I add this functionality to the visualization and I think it’s a terrific addition. Let’s see how much easier it is to focus on the two main questions (overall and the Selected region) when we mute the colors that aren’t stacked along the baseline.

Here’s the results when we sort by the selected region.

Figure 9 — Stacked bar chart with muted colors sorted by Selected region.

And here are the results when we sort by overall sales.

Figure 10 — Stacked bar chart with muted colors sorted by overall sales.

You’ll need to address the same issues with area charts as you can only make accurate comparisons for totals and for segments that hug the baseline, as shown below.

Figure 11 — Area chart showing sales over time. Note that we can compare overall sales and sales in the West as there is common baseline.

Note that because we are not including the product sub-categories the sorting feature is not needed.

## 100% stacked bar charts

Whereas the regular stacked bar chart allows you to make accurate comparisons of overall sales and one region at a time, a 100% stacked bar chart will allow you to accurately compare the two outer regions, as we can see below.

Figure 12 — 100% stacked bar chart. We can compare the outer regions (Central and West) because there is a common baseline

Buy we can’t accurately compare the inner regions:

Figure 13 — 100% stacked bar chart. We cannot compare the inner regions (East and South) because the elements are floating; there isn’t a common baseline.

## Give the dashboard a try

The dashboard below allows you to explore the functionality discussed in this post. Please note that I’m not suggesting you should include all the widgets in the dashboard. Indeed, maybe this is something you use on our own to help curate interesting findings in your data that you then highlight in a presentation or using Storypoints.

As for how to build all this functionality into Tableau, if you download and the workbook and look under the hood you’ll see there’s nothing terribly complicated going on (indeed, there isn’t one LOD calc). That said, my solution is not very robust — it’s hard-coded to only show the four known regions that are currently in the data set. I’m sure with a bit more effort one could fashion something extensible but for this blog post I wanted to prototype the functionality, not craft a robust solution.

Parting thoughts: Do make sure to check out this post where Rody Zakovich applies a different approach to looking at overall and segmented sales for individual customers.

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## Overview

Tableau’s automatic axis feature can often present problems with survey data.

In this blog post we’ll look at how the problem crops up and what you can do to both fix the problem and make your visualizations more insightful.

## The problem with auto-adjusting bars

Tableau’s automatic axis does a great job extending and contracting the length of bars so that a bar chart fills the view. In most circumstances this is a good thing but there are cases where it can inhibit your audience’s ability to correctly interpret your data.

Consider the chart below where we see the percentage of people that selected that top 2 boxes for a Likert scale question (in this case we see the percentage of people who selected Agree or Strongly Agree).  Pay attention to the length of the top bar.

Figure 1 — Percentage of respondents that selected Agree or Strongly Agree (Top 2 Boxes).  Notice the length of the top bar.

Now let’s see what happens when we change the view to show the percentage of people that only selected Strongly Agree (Top Box).  Again, pay attention to the length of the top bar.

Figure 2 — Percentage of respondents that selected Strongly Agree (Top Box).  Again, notice the length of the top bar.

There’s a HUGE difference here, but unless you look at the numbers (and the labels, as the sorting has changed) you’re going to miss it.

## Make the differences pop by comparing with 100%

Here’s the same data but this time we’re showing the gap between each bar and 100%.

First, let’s see what the Top 2 Box view looks like.

Figure 3 — Percentage of respondents that selected Agree or Strongly Agree (Top 2 Boxes) compared with 100%.

Now let’s see what happens when we change this to show only the Top Box.

Figure 4 — Percentage of respondents that selected Strongly Agree (Top Box) compared with 100%.

As chef Emeril Lagasse might say “BAM!”

I’ll walk through how to create the 100% bars in a moment but I first want to show another instance where the auto-sizing bars can present a problem.

## Showing demographics: comparing two similar charts

A similar issues comes into play with demographics dashboards.  Consider the very simple dashboard below that shows the percentage of respondents broken down by location and gender.

Figure 5 — Respondent demographics dashboard with auto-sizing bars.

Do you see the problem?  No?  Check out what happens when you hover over the North America bar and the Male bar.

Figure 6 — Two independent charts with slightly different levels of magnitude.

The top bar in both cases is the same length, but in the top chart is represents 341 respondents (40%) and in the bottom chart it represents 436 people (52%).

The solution is to normalize the chart so that both have a common axis, as shown below.

Figure 7 — Demographics dashboard with common (normalized) axes.

We will consider a couple of ways to produce this normalized axis later but first let’s tackle the 100% reference bars.

## Building the reference bars visualization

Let’s start with the view shown below which shows the percentage of folks that selected the Top N Boxes.  Note that the completed dashboards may be found embedded at the end of this blog post.

Figure 8 — Initial view

1. Create a calculated field called 100 Percent and define it as follows.

Yes, you will need to define at as “1.0” so that Tableau treats it as a float and not an integer. This will come up later when we need to create a dual axis and synchronize the charts.
2. Right-drag the newly-created field to Columns and select MAX(100 Percent) from the Drop Field dialog Box. Also make sure the mark is set to Bar and not automatic.
3. Right-click MAX(100 Percent) on columns and select Dual Axis. Your screen should look like the one shown below.
4. Right-click the axis at the top and select Move marks to back, then right-click the axis again and select Synchronize Axis. Your screen should look like this.
5. Right-click the top axis and deselect Show Header, then modify the colors so that the 100% bars is a muted gray.

## Normalizing the axis in the demographics dashboard

I’m going to recommend a different approach for the demographics dashboard as we really don’t need to show 100%; it’s very unlikely that any one segment will ever reach 100%.  Instead I propose adding a hidden reference line placed at the maximum value that any demographic element is likely to reach, say 60%

Consider the Location demographic visualization below.

There are several ways to hard-code a reference line at 60%. Here’s one way to go.

1. Right-click the axis along the bottom and select Add Reference Line.
2. When the Add Reference Line, Band, or Box dialog box appears, select Entire Table and indicate you want a constant set for .6. Make sure to set Label to None and Line to None as shown below.
3. Repeat this for all the other worksheets that comprise your demographics dashboard.

“Wait!”, you may exclaim. “Why not just fix the axis at 60%?”

That will work… most of the time.  But what happens if you fix the axis at 60% and you in fact have a bar that exceeds that value?  If you fix the axis you are out of luck, but a hidden reference line will not constrain the axis; that is, it will make sure the axis extends to at least 60%, but the axis will go beyond that if needed.

## Conclusion

Tableau’s tendency to auto-size an axis is great but can sometimes foster confusion. In this post we explored two different techniques that will help your audience make more accurate comparisons.

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May 10, 2017

## Overview

Most organizations want to wildly exceed customer expectations for all facets of all their products and services, but if your organization is like most, you’re not going to be able to do this. Therefore, how should you allocate money and resources?

First, make sure you are not putting time and attention into things that aren’t important to your customers and make sure you satisfy customers with the things that are important.

One way to do this is to create a survey that contains two parallel sets of questions that ask customers to indicate the importance of certain features / services with how satisfied they are with those products and services.  A snippet of what this might look like to a survey taker is shown in Figure 1.

Figure 1 — How the importance vs. satisfaction questions might appear to the person taking the survey.

## How to Visualize the Results

I’ve come up with a half dozen ways to show the results and will share three approaches in this blog post.  All three approaches use the concept of “Top 2 Boxes” where we compare the percentage of people who indicated Important or Very Important (the top two possible choices out of five for importance) and Satisfied or Very Satisfied (again, the top two choices for Satisfaction).

##### Bar-In-Bar Chart

Figure 2 shows a bar-in-bar chart, sorted by the items that are most important.

Figure 2 — Bar-in-bar chart

This works fine, as would having a bar and a vertical reference line.

It’s easy to see that we are disappointing our customers in everything except the least important category and that the gap between importance and satisfaction is particular pronounced in Ability to Customer UI (we’re not doing so well in Response Time, 24-7 Support, and East of Use, either.)

##### Scatterplot with 45-degree line

Figure 3 shows a scatterplot that compares the percent top 2 boxes for Importance plotted against the percent top 2 boxes for Satisfaction where each mark is a different attribute in our study.

Figure 3 — Scatterplot with 45-degree reference line

The goal is to be as close to the 45-degree line as possible in that you want to match satisfaction with importance. That is, you don’t want to underserve customers (have marks below the line) but you probably don’t want to overserve, either, as marks above the line suggest you may be putting to many resources into things that are not that important to your customers.

As with the previous example it’s easy to see the one place where we are exceeding expectations and the three places where we’re quite a bit behind.

##### Dot Plot with Line

Of the half dozen or so approaches the one I like most is the connected dot plot, shown in Figure 4.

Figure 4 — Connected dot plot. This is the viz I like the most.

(I placed “I like most” in italics because all the visualizations I’ve shown “work” and one of them might resonate more with your audience than this one.  Just because I like it doesn’t mean it will be the best for your organization so get feedback before deploying.)

In the connected dot plot the dots show the top 2 boxes for importance compared to the top 2 boxes for satisfaction.  The line between them underscores the gap.

I like this viz because it is sortable and easy to see where the gaps are most pronounced.

## But what about a Divergent Stacked Bar Chart?

Yes, this is my “go to” viz for Likert-scale things and I do in fact incorporate such a view in the drill-down dashboard found at the end of this blog post. I did in fact experiment with the view but found that while it worked for comparing one feature at a time it was difficult to understand when comparing all 10 features (See Figure 5.)

Figure 5 — Divergent stacked bar overload (too much of a good thing).

## How to Build This — Make Sure the Data is Set Up Correctly

As with everything survey related, it’s critical that the data be set up properly. In this case for each Question ID we have something that maps that ID to a human readable question / feature and groups related questions together, as shown in Figure 6.

Figure 6 — Mapping the question IDs to human readable form and grouping related questions

Having the data set up “just so” allows us to quickly build a useful, albeit hard to parse, comparison of Importance vs. Satisfaction, as shown in Figure 7.

Figure 7 — Quick and dirty comparison of importance vs. satisfaction.

Here we are just showing the questions that pertain to Importance and Satisfaction (1). Note that measure [Percentage Top 2 Boxes] that is on Columns (2) is defined as follows.

Figure 8 — Calculated field for determining the percentage of people that selected the top 2 boxes.

Why >=3?  It turns out that the Likert scale for this data went from 0 to 4, so here we just want to add up everyone who selected a 3 or a 4.

## Not Quite Ready to Rock and Roll

This calculated field will work for many of the visualizations we might want to create, but it won’t work for the scatterplot and it will give us some headaches when we attempt to add some discrete measures to the header that surrounds our chart (the % Diff text that appears to the left of the dot plot in Figure 4.) So, instead of having a single calculation I created two separate calculations to compute % top 2 boxes Importance and % top 2 boxes Satisfaction. The calculation for Importance is shown in Figure 9.

Figure 9 — Calculated field for determining the percentage of folks that selected the top two boxes for Importance.

Notice that we have all the rows associated with both the Importance questions and Satisfaction “in play”, as it were, but we’re only tabulating results for the Importance questions so we’re dividing by half of the total number of records.

We’ll need to create a similar calculated field for the Satisfaction questions.

## Ready to Rock and Roll

##### Understanding the Dot Plot

Figure 10 shows what drives the Dot Plot (we’ll add the connecting line in a moment.)

Figure 10 — Dissecting the Dot Plot.

Here we see that we have a Shape chart (1) that will display two different Measure Values (2) and that Measure Names (3) is controlling Shape and Color.

##### Creating the Connecting Line Chart

Figure 11 shows how the Line chart that connects the shapes are built.

Figure 11 — Dissecting the Line chart.

Notice that Measure Values is on Rows a second time (1) but the second instance the mark type is a Line (2) and that the end points are connected using the Measure Names on the Path (3).  Also notice that there is no longer anything controlling the Color as we want a line that is only one color.

##### Combining the Two Charts

The only thing we need to do now is combine the two charts into one by making a dual axis chart, to synchronize the secondary axis, and hide the secondary header (Figure 12.)

Figure 12 — the Completed connected Dot Plot.

## What to Look for in the Dashboard

Any chart that answers a question usually fosters more questions. Consider the really big gap in Ability to Customize UI. Did all respondents indicate this, or only some?

And if one group was considerably more pronounced than others, what were the actual responses across the board (vs. just looking at the percent top 2 boxes)?

Figure 13 — Getting the details on how one group responded

The dashboard embedded below shows how you can answer these questions.

Got another approach that you think works better?  Let me know.

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April 25, 2017

## Overview

I became a big fan of adding a marginal histogram to scatterplots when I first saw them applied in Tableau visualizations from Shine Pulikathara and Ben Jones.

For those not familiar with how these work, consider the scatterplot shown in Figure 1 that shows the relationship between salary and age.

Figure 1 — Comparing Age and Salary on a scatterplot

Some interesting things here; for example,  we can see that salaries appear to be highest between ages 50 and 55 and lowest among the youngest and older workers.

But look what happens when we add marginal histograms to the x and y axes (Figure 2.)

Figure 2 — Scatterplot with marginal histogram

Whoa! The two bar charts to the right and below the main chart add a lot of insight into the data.  We don’t just see the correlations, but now we can also see age demographics and salary distribution in the organization.

## Marginal Histograms and Jitterplots

The marginal histogram works with other visualizations as well. Consider the dot plot with jitter (jitterplot) example from Lean management tool innovator LeanKit in Figure 3.

Figure 3 — Individual and aggregate vies of important data from LeanKit

The combination of the individual data points (the jittered dots that represent Kanban cards) and the aggregated data (stacked bar charts) tells a more complete story than having only the aggregation or only the individual dots.

## Marginal Histograms and Highlight Tables

Readers of this blog know I like highlight tables and often use them as a “visualization gateway drug” to move people from cross tabs to more insightful ways of looking at their data.

But as great as they are, they do not lend themselves to accurate comparisons of the data. Consider Figure 4 where we see the percentage of sales broken down by region.

Figure 4 — Sorted highlight table showing percentage of sales by sub-category and region

Yes, I can see that Phones in the East is a lot darker than Copiers in the West, but without the numbers there’s no way to could do an exact comparison as I don’t know of anyone that can look at just the color coding and exclaim “ah, that cell is twice as blue as that other cell.”

But look what happens when we add the marginal histogram to the visualization, as shown in Figure 5.

Figure 5 — Sorted highlight table with marginal histograms. Here we see percentage of sales.

I’ll confess that the histograms don’t work quite as well if you have negative values. Here’s what it looks like if we look at percentage of profit broken down by sub-category and region.

Figure 6 — Sorted highlight table with marginal histograms. Here we see percentage of profit.

Because we have bars pointing in different directions for the histogram on the right the look isn’t quite as clean, but it certainly works.

## See for Yourself

I’ve included an embedded dashboard below where you can experiment with different metrics and different sorting choices. Feel free to download and “look under the hood.”

Note that making this type of dashboard is not very difficult; the only tricky part is getting the three elements to align properly. Ben Jones gets into those particulars in his blog post.

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More thoughts on the Markimekko chart and in particular how to build one in Tableau.

April 4, 2017

## Overview

Given my reluctance to embrace odd chart types and my conviction that I would find something better I was surprised to find myself last month writing about — and endorsing — the Marimekko chart.

If I was surprised then I’m absolutely gobsmacked to be writing about it again.

What precipitated all this was another very good example of the chart in the wild. After admiring it I couldn’t help but “look under the hood” (hey, we are talking about Tableau Public and people sharing this stuff freely) and I thought that the dashboard designer was working harder than he needed to build the visualization.

So, if people are going to use these things I thought I would share an alternative, and I think easier, technique for building them.

## The Great Example from Neil Richards

Here’s the terrific Makeover Monday dashboard from Neil Richards where we see the likelihood of certain jobs being replaced by automation.

Neil does a great job highlighting some of the more interesting findings, but if you want to know more than what Neil highlights you’ll need to explore the dashboard on your own.

Notice that in both this case and in Emma Whyte’s we are dealing with only two data segments; e.g., male vs. female and at-risk vs. not at-risk jobs. Having only two colors is one of the main reasons why the chart works well.

Okay! Uncle! I agree that under the right conditions this is a useful chart and I can see what you may want to make one.

But is there an easier way to make one?

## An Easier Way to Create a Markimekko Chart in Tableau

It turns out the same technique Joe Mako showed me six years ago for building a divergent stacked bar chart works great for fashioning a Markimekko.  Let’s see how to do this using Superstore data with fields similar to what was available in both Emma and Neil’s dashboards.

Let’s say I want to compare the magnitude of sales with the profitability of items by region.  Figure 2 shows the overall magnitude of sales but makes comparing profitability difficult.

Figure 2 — Overall sales is easy to see but comparing profitability across regions is difficult.

Here’s another attempt using a 100% stacked bar chart.

Figure 3 — Showing profitability with a 100% stacked bar chart.

Yes, this does a much better job allowing us to compare the profitability of each region, but there’s no way to easily glean that Sales in the West is almost double sales in the South (which is easy to do in Figure 2.)

So, how can we make the regions that have large sales be wide and the regions that have small sales be  narrow?

## Understanding the Fields

Before going much further let’s make sure we understand the following three fields:

• Percentage Profitable Sales
• Percentage Unprofitable Sales
• Sales Percentage of
##### [Percentage Profitable Sales]

This is defined as

`SUM(IF [Profit]>=0 THEN [Sales] END)/SUM(Sales)`

… and translates as “if the profit for an item within a partition is profitable, add it up, then divide by the total sales within the partition.”

This is the field that gives us the 90%, 77%, 76%, and 72% results shown in Figure 3.

##### [Percentage Unprofitable Sales]

This is defined as

`1 - [Percentage of Profitable Sales]`

… and gives us the 10%, 23%, 24%, ad 28% shown in Figure 3.

##### [Sales Percentage of]

This is defined as

`SUM([Sales]) /TOTAL(SUM([Sales]))`

… and we will use it to compute the percentage of sales across the four regions (i.e., show me the sales for one region divided by the sales for all the regions). Here’s how we might use it in a visualization.

Figure 4 — Using the calculation to figure out how wide each region should be.

So, in Figure 4 we can see that the West segment is a lot thicker than the South segment.

How can we apply this additional depth to what we had in Figure 3?

## Make it Easy to See if the Math is Correct

At this point it will be helpful to see the interplay of the various measures and dimensions using a cross tab like the one shown in Figure 5.

Figure 5 — Cross tab showing the relationship among the different measures and dimensions.

The first four columns are easy to interpret:

“I see that sales in the West is \$725,458 of which 10% is unprofitable and 90% is profitable.  That \$725,458 represents 31.6% of the total sales.”

But how is the field called [Start at] defined and how are we going to use it?

### Understanding [Start at]

[Start at] is defined as

`PREVIOUS_VALUE(0)+ZN(LOOKUP([Sales Percentage of],-1))`

This is the calculation that figures out where the mark should start while [Sales Percentage of] will later determine how thick the mark should be.  Let’s see how this all works together.

Figure 6 — How [Start at] and [Sales Percentage of] will work together.  Note that “Compute Using” for the two table calculations is set to [Region].

For the West region we want to start at 0% and have a bar that is 31.6% units side. The function

`PREVIOUS_VALUE(0)`

Tells Tableau to look at whatever is the value for [Sales at] for the row above and if there is no row above make the value 0 (see Item 1 in Figure 6, above.)

Add to this the value for [Sales Percentage of] in the previous row (Item 2 which is also not present) and you get 0 + 0 (Item 3).

For the East region we want to start wherever West left off (Item 3 plus Item 4, which gives us item 5) and make the mark 29.5% wide (item 6).

For the Central region we want to start wherever the previous region left off (Item 5 plus item 6, which gives us item 7) and make the mark 21.8% wide (Item 8).

Let’s see how this all fits together into the Marimekko visualization in Figure 7.

Figure 7 — Using [Start at ] and [Sales Percentage of] to make the Marimekko work.

There are three things to keep in mind.

1. [Start at] is on columns and determines the starting point (how far to the right) for each of the regions.
2. [Sales Percentage of] is on Size and determines how thick the bars should be.
3. Size is set to Fixed width, left aligned, where Fixed means the measure on the Size shelf is determining the thickness.

Figure 8 — Size must be fixed and left-aligned.

## Some Interesting Findings

I built a parameter-driven version of the Marimekko (embedded at the end of this blog post) that allows the viewer to select different dimensions and different ways to sort. Here’s what happens when we look at Sub-Category sorted by Profitability.

Figure 9 — Profitability by Sub-Category.

Okay, not a big surprise here given how many visualizations we’ve all seen showing that Tables are problematic.

That said, I was in for a surprise when I broke this down by state and sorted by the magnitude of sales, as shown below.

Figure 10 — Profitability by state, sorted by Sales.

Wow, after 11 years of living with this data set I never realized that 60% of the items sold in Texas were unprofitable.  Who knew?

To be honest I’m not convinced we need a Marimekko to see this clearly.  A simple sorted bar chart will do the trick, as shown in Figure 11.

Figure 11 — Sorted bar chart.

Indeed, I think this very simple view is better than the Marimekko in many respects.

I guess it depends what you’re trying to get across.

## See for Yourself

I’ve included an embedded workbook that has the Superstore example as well as versions of the visualizations Emma Whyte and Neil Richards built, but using this alternative technique.

I encourage you to think long and hard before deploying a Marimekko.  But if you do decide to build one I hope the techniques I explored here will prove useful.

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Or

How I stopped worrying and learned to love appreciate the Marimekko

March 19, 2017

## Overview

Readers of my blog know that I suffer from what Maarten Lambrechts calls xenographphobia, the fear of unusual graphics.  I’ll encounter a chart type that I’ve not seen before, purse my lips, and think (smugly) that there is undoubtedly a better way to show the data than in this novel and, to me, unusual chart.

That was certainly my reaction to “Marimekko Mania” when Tableau 10.0 was first released. I didn’t see a solid use case for this chart. There were some wonderful blog posts from Jonathan Drummey and Bridget Cogley on the subject, but I just wasn’t buying the need for the chart type.

Note: It turns that for many situations you can make a perfectly fine Marimekko just using table calculations. I’ll weigh in on this later.

## Enter Emma Whyte and Workout Wednesday

My “I’ll never need to use that” arrogance was disrupted a few weeks ago when I read this blog post from Emma Whyte.  The backstory is that Emma reviewed a Junk Charts makeover of a Wall Street Journal graphic, really liked the makeover, and decided to recreate it in Tableau.

Here’s the Wall Street Journal graphic.

Figure 1 — Source of inspiration for Junk Charts  and Emma Whyte. From a 2016 survey by LeanIn.org and McKinsey & Co.

There are two important things the data is trying to tell us:

1. The percentage of women decreases, a lot, the higher up you go in the corporate hierarchy; and,
2. There are far more entry-level positions than there are managers than there are VPs, etc.

The chart does a good job on the first point but only uses text to covey the second point.

Contrast this with Emmy Whyte’s visualization:

Figure 2 — Emma Whyte’s makeover.

Whoa.

I immediately “grokked” this.  There are way more men than women among VPs, Senior VPs, and in the C-Suite, but look how much narrower those bars are!  True, I cannot easily compare how much wider the Entry Level column is than the VP column, but is that really important?

Is the Marimekko in fact the “right” way to show this?

Being a little bit stubborn I was not ready to declare a Marimekko victory so I decided to see if I could build something that worked as well, if not better, using more common chart types.

## Anything You Can Do, I Can Do…

I won’t go through all ten iterations I came up with but I will show some of my attempts to convey the data accurately and with the visceral wallop I get from Emma’s makeover.

##### 100% Stacked Bar with Marginal Histogram

Putting a histogram in the margin has become a “go to” technique when I’m dealing with highlight tables and scatterplots so I thought that might work in this situation. Here’s a 100% stacked bar chart combined with a histogram.

Figure 3 — 100% stacked bar with marginal histogram.

I was so convinced this would just smoke the Marimekko. I mean just look how easy it is to make accurate comparisons!

That may be true, but I think the Marimekko in question does a better job.

##### Connected Dot Plot

Here’s another attempt using a connected dot plot.

Figure 4 — Connected dot plot where the size of the circles reflects the percentage of the workforce.

Here the lines separating the circles show the gender gap and the size of the circles reflects the percentage of the workforce.

OK, I think the gap is well represented but the spacing between job levels is a fixed width.  In my pursuit of accuracy I needed to find a way spread the circles based on percentage of the workforce.

##### Diverging Lines with Bands

Figure 5 shows two diverging lines with circles and bands that are proportionate to the percentage of the workforce (Entry level is 52 units wide, Manager is 28 units wide, and so on).

Figure 5 — Diverging lines with dots and correctly-sized circles and bands

But why are the lines sloping?  Shouldn’t the lines be flat for each job level?

##### Flat Lines

Here’s a similar approach but where the lines stay flat for each job level.

Figure 6 — Flat lines and accurate circles and bands.

## More Approaches and the Graphic from the Actual Report

All told I made ten attempts.  The calculation I came up with for Figure 5 also made it possible to create a Markimekko just using a simple table calculation.

Note: I asked Jonathan Drummey to have a look at the Marimekko-with-table-calc approach and he points out that in both my example and Emma Whyte’s example the data isn’t “dense” so you can break the visualization simply by right-clicking a mark and selecting Exclude. That said, the technique is fine for static images and dashboards where you disable the Exclude functionality.

I also reviewed the full Women in the Workplace report and saw they used an interesting pipeline chart to relate the data.

Figure 7 — “Pipeline” chart from Women in Workplace report (LeanIn.Org and McKinsey & Co.)

I applaud the creativity but have a lot of problems with the inaccurate proportions. Notice that this chart also has a sloping line suggesting a continuous decrease as you go from one level to another.

## And The Winner is…

For me, Emma Whyte’s Marimekko does the best job of showing the data in a compelling and accurate format and I thank Emma for presenting such a worthwhile example.

Will I use this chart type in my practice?

It depends.

If the situation calls for it, I would try it along with other approaches and see what works best for the intended audience.

Here’s a link to the Tableau workbook that contains a copy of Emma Whyte’s original approach and many of my attempts to improve upon it. If you come up with an alternative approach that you think works well, please let me know.

## Postscript

Big Book of Dashboards co-author Jeff Shaffer encouraged me to make some more attempts. Here’s a work in progress using jittering.

I think this looks promising.

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## Overview

Imagine a terrific introductory college course presented by a terrific professor.

That’s the feeling I had in reading The Truthful Art, Alberto Cairo’s follow up to his first book The Functional Art.

Whereas his first book took a “look at what you can and should do” approach to help people see and understand data, The Truthful Art is more of a “here’s what you need to know” if you want to be a data journalist — and there’s a lot of things you need to know if you are going to do a proper job.

I’m reluctant to use the term “data journalism” as Cairo’s book is for anyone that that is tasked with helping people make sense of data. The difference is that the data journalist’s work is likely to be public and yours may only be seen by people working in your organization. But while you may not have to make a dashboard that is as polished as an infographic from the New York Times, both you and the data journalist need to adhere to a particular doctrine and have sufficient skills across a wide range of topics if you are going to build functional, truthful, and meaningful visualizations.

## First, Be Truthful

If the credo for doctors is to “first, do no harm” Cairo might argue that the credo for data journalists is to “first, be truthful.” Cairo makes the case that a good visualization must be

• Truthful
• Functional
• Beautiful
• Insightful
• Enlightening

And it must be these things in this order of priority. That is, the visualization must first be “relevant, factual, and accurate” and only then should it be “accessible and engaging.” Cairo further states that “honesty, clarity, and depth come first.” Indeed, this is why he bristles with outrage over deceitful graphics like this one.

So, how, exactly, does one create something that is truthful, functional, beautiful, insightful, and enlightening?

By achieving a sufficient level of competence in a LOT of different areas.

And just what are those areas?

## The Data Journalism Landscape

In reading The Truthful Art you may feel like you are in a helicopter several thousand feet above the data visualization landscape. In each section Cairo, as expert guide, will gently descend to several hundred feet above a particular area and allow you to examine varied topics including design, statistics, color, storytelling, psychology, and ethics. While the book never gets deep into any of these subjects Cairo does provide excellent resources for anyone interested in exploring a particular topic in depth as every chapter of the book ends with a section titled “To Learn More.”

While Cairo’s writing is disarmingly warm and engaging he takes the responsibility of data storytelling very seriously. By the end of the book you will have an excellent understanding of the investment needed to make a worthwhile contribution to your company, society, or both.

## Conclusion

Whether you are new to the field or have been practicing for years, I’m confident you’ll find The Truthful Art, like its predecessor, to be fun, elucidating, and inspiring.

The Truthful Art

Paperback: 400 pages

Publisher: New Riders; 1 edition (February 28, 2016)

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## Overview

As readers of this blog know, I have my problems with donut charts.

That said, I acknowledge that they can be cool and, under certain circumstances, enormously useful.

On a recent flight I was struck by how much I liked the animated “estimated time to arrival” donut chart that appeared on my personal TV screen. An example of such a chart is shown in Figure 1.

Figure 1 — Donut chart showing progress towards completion of a flight.

I find this image very attractive and very easy to understand — I can see that I’m almost three-quarters of the way to my destination and that there are only 49 minutes left to the flight.

So, given how clear and cool this is, why not use them on a dashboard?  And if one is good, why not use lots of them?

It’s the “more than one” situation that may lead to problems.

## Trying to make comparisons with donut charts

The flight status chart works because it shows only one thing only: a single item’s progress towards a goal.

Let’s see what happens when we want to compare more than one item.

Consider the chart in Figure 2 that shows the placement rates for Fremontia Academy.

Figure 2 — Donut chart showing placement percentage.

A 95% placement percentage is really impressive.  Is that better than other institutions?  If so, how much better is it?

Figure 3 shows a comparison among three different institutions using three different donut charts.

Figure 3  — Three donut charts displaying placement percentages for three different institutions.

Before digging deeper let’s replace the three separate donuts with a donut-within-a-donut-within-a donut chart (Figure 4.)

Figure 4  — A concentric donut chart (also called a “radial bar chart” or a “pie gauge.”)

“What’s the problem?” you may ask, “these comparisons are easy.”

While you may be able to make the comparisons you are in fact working consierably harder than you need to be.

Really.  Let me prove it to you.

Let’s suppose you wanted to compare the heights of three famous buildings: One World Trade Center, The Empire State Building, and The Chrysler Building (Figure 5).

Figure 5  — Comparing the size (in feet) of three large buildings.

Now that’s an easy comparison. With virtually no effort we can see that One World Trade Center (blue) is almost twice as tall as The Chrysler Building (red).

Now let’s see how easy the comparison is with donuts (Figure 6.)

Figure 6  — Three large buildings twisted into semi-circles.

Here are the same buildings rendered using a concentric donut chart (Figure 7).

Figure 7  — Three skyscrapers spooning.

Yikes.

So, with this somewhat contrived but hopefully memorable  example we took something that was simple to compare (the silhouettes of buildings) and contorted them into difficult-to-compare semi-circles.

With this in mind, let’s revisit the Placement example we saw in Figure 3.

Here is the same data rendered using a bar chart.

Figure 8 — Placement percentage comparison using a bar chart.

The comparison is much easier with the bars than with the donuts / semi-circles. You can tell with practically no effort that the blue bar is approximately twice as long as the red bar, even without looking at the numbers.

Indeed, that’s a really good test of how clear your visualization is: can you compare magnitude if the numbers are hidden?

Pop quiz — how much larger is the orange segment compared to the red segment?

Figure 9 — Trying to compare the length of donut segments is difficult.

Now try to answer the same question with a “boring” bar chart.

Figure 10 — Comparing the length of bars is easy.

With the circle segments you are squinting and guessing while with the bars you know immediately: the orange bar is twice as large as the red bar.

## More downsides for donuts

In addition to comparisons being difficult, how would you handle a situation where you exceeded a goal?  For example, how do you show a salesperson beating his / her quota?  With a bar chart you can show the bar going beyond the goal line (Figure 11).

Figure 11 — With a bar chart it’s easy to show more than 100% of goal.

How do you show this with a donut chart?

Rhetorical question.  You can’t.

## Conclusion

If you only have to show progress towards a single goal and don’t need to make a comparison then it’s fine to use a donut chart. If you need anything more complex you should use a bar chart as it will be much easier for you and your users to understand the data.

Special thanks to Eric Kim for creating the building images.

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