Several problems with stacked bar charts, as demonstrated by a Delta chart designer

In the Trifecta Checkup (link), I like to see the Question and the Visual work well together. Sometimes, you have a nice message but you just pick the wrong Visual.

An example is the following stacked column chart, used in an investor presentation by Delta.

From what I can tell, the five types of aircraft are divided into RJ (regional jet) and others (perhaps, larger jets). With each of those types, there are two or three subtypes. The primary message here is the reduction in the RJ fleet and the expansion of Small/Medium/Large.

One problem with a stacked column chart with five types is that it takes too much effort to understand the trends of the middle types.

The two types on the edges are not immune to confusion either. As shown below, both the dark blue (Large) type and the dark red (50-seat RJ) type are associated with downward sloping lines except that the former type is growing rapidly while the latter is vanishing from the mix!

 In this case, the slopegraph (Bumps-type chart) can overcome some of the limitations.

***

This example was used in my new dataviz workshop, launched in St. Louis yesterday. Thank you to the participants for making it a lively session!

Doing my duty on Pi Day #onelesspie

Xan Gregg and I started a #onelesspie campaign a few years ago. On Pi Day each year, we find a pie chart, and remake it. On Wikipedia, you can find all manners of pie chart. Try this search, and see for yourself.

Here's one found on the Wiki page about the city of Ogema, in Canada:

This chart has 20 age groups, each given a different color. That's way too much!

I was able to find data on 10-year age groups, not five. But the "shape" of the distribution is much easily seen on a column chart (a histogram).

Only a single color is needed.

The reason why I gravitated to this chart was the highly unusual age distribution... this town has almost uniform distribution of age groups, with each of the 10-year ranges accounting for about 11% of the population. Given that there are 9 groups, a perfectly even distribution would be 11% for each column. (Well, the last group of 80+ is cheating a bit as it has more than 10 years.)

I don't know about Ogema. Maybe a reader can explain this unusual age distribution!

 

 

 

Steel tariffs, and my new dataviz seminar

I am developing a new seminar aimed at business professionals who want to improve their ability to communicate using charts. I want any guidance to be tool-agnostic, so that attendees can implement them using Excel if that’s their main charting software. Over the 12+ years that I’ve been blogging, certain ideas keep popping up; and I have collected these motifs and organized them for the seminar. This post is about a recent chart that brings up a few of these motifs.

This chart has been making the rounds in articles about the steel tariffs.

The chart shows the Top 10 nations that sell steel to the U.S., which together account for 78% of all imports. 

The chart shows a few signs of design. These things caught my eye:

1. the pie chart on the left delivers the top-line message that 10 countries account for almost 80% of all U.S. steel imports
2. the callout gives further information about which 10 countries and how much each nation sells to the U.S. This is a nice use of layering
3. on the right side, progressive tints of blue indicate the respective volumes of imports

On the negative side of the ledger, the chart is marred by three small problems. Each of these problems concerns inconsistency, which creates confusion for readers.

1. Inconsistent use of color: on the left side, the darker blue indicates lower volume while on the right side, the darker blue indicates higher volume
2. Inconsistent coding of pie slices: on the right side, the percentages add up to 78% while the total area of the pie is 100%
3. Inconsistent scales: the left chart carrying the top-line message is notably smaller than the right chart depicting the secondary message. Readers’ first impression is drawn to the right chart.

Easy fixes lead to the following chart:

***

The central idea of the new dataviz seminar is that there are many easy fixes that are often missed by the vast majority of people making Excel charts. I will present a stack of these motifs. If you're in the St. Louis area, you get to experience the seminar first. Register for a spot here.

Send this message to your friends and coworkers in the area. Also, contact me if you'd like to bring this seminar to your area.

***

I also tried the following design, which brings out some other interesting tidbits, such as that Canada and Brazil together sell the U.S. about 30% of its imported steel, the top 4 importers account for about 50% of all steel imports, etc. Color is introduced on the chart via a stylized flag coloring.

 

 

 

 

 

The tech world in which everyone is below average

Laura pointed me to an infographic about tech worker salaries in major tech hubs (link).

What's wrong with this map?

The box "Global average" is doubly false. It is not global, and it is not the average!

The only non-American cities included in this survey are Toronto, Paris and London.

The only city with average salary above the "Global average" is San Francisco Bay Area. Since the Bay Area does not outweigh all other cities combined in the number of tech workers, it is impossible to get an average of $135,000.

***

Here is the second chart.

What's wrong with these lines?

This chart frustrates the reader's expectations. The reader interprets it as a simple line chart, based on three strong hints:

• time along the horizontal axis
• data labels show dollar units
• lines linking time

Each line seems to show the trend of average tech worker salary, in dollar units.

However, that isn't the designer's intention. Let's zoom in on Chicago and Denver:

The number $112,000 (Denver) sits below the number $107,000 (Chicago). It appears that each chart has its own scale. But that's not the case either.

For a small-multiples setup, we expect all charts should use the same scale. Even though the data labels are absolute dollar amounts, the vertical axis is on a relative scale (percent change). To make things even more complicated, the percent change is computed relative to the minimum of the three annual values, no matter which year it occurs.

That's why $106,000 (Chicago) is at the same level as $112,000 (Denver). Those are the minimum values in the respective time series. As shown above, these line charts are easier to understand if the axis is displayed in its true units of percent change.

The choice of using the minimum value as the reference level interferes with comparing one city to the next. For Chicago, the line chart tells us 2015 is about 2 percent above 2016 while 2017 is 6 percent above. For Denver, the line chart tells us that 2016 is about 2 percent above the 2015 and 2017 values. Now what's the message again?

Here I index all lines to the earliest year.

 

In a Trifecta Checkup analysis (link), I'd be suspicious of the data. Did tech salaries in London really drop by 15-20 percent in the last three years?