Triple Bar Graph
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Triple Bar Graph
As the number of chart types and approaches continue to grow, things get worse, and sometimes even top experts get confused with identifying the purposes of one chart type or another. Stacked bar charts are often worthwhile and should be considered when the occasion calls for it. This article explains why you should be careful when and where you use them.
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Data visualization has become an important part of our daily lives, allowing us to quickly assess information. And with so many chart types to choose from, it should be possible to effectively solve almost any task, whether it’s exploratory (that is, examining and analyzing data to better understand it yourself) or explanatory (that is, reporting and communicating data to the end user).
However, variety can also cause confusion, making it difficult to clearly understand the purpose of any form of data visualization. As a result, if an inappropriate type of diagram is applied to data, the user can not only be confused by the information, but, more importantly, can make bad decisions based on such a presentation.
Today we’re going to talk about stacked bar charts, because – to my surprise – I recently learned that these chart types can be real problems.
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A vivid example is Robert Kosara, senior research scientist at Tableau Software and former associate professor of computer science. In his blog post “Stacked Bar Charts Are the Worst” he calls stacked bar charts inconvenient, useless and even harmful. But is that really fair?
I would argue that stacked bar charts are undeservedly demonized in his article. The author seems to have simply fallen victim to a common misunderstanding of their true purpose. In fact, stacked bar charts are used to compare totals in different categories and at the same time identify which series is “to blame” for making one total larger or perhaps smaller than another.
However, Kosara says they are the worst because they are useless for comparing series sizes in a given category. Well, I agree: stacked bar charts are really bad at that, and a regular multiseries bar chart always seems to be a better choice. But they should not be used for that. It’s like using a hammer to change a light bulb – you’ll just end up with a mess!
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In this article I will try to explain the real purposes of visualizing data in regular and stacked bar charts and what exactly they should be used for. To begin, let’s look at a conventional bar chart.
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The main purpose of a bar chart is to compare individual data points with each other. It’s easy. Let’s look at regular vertical bar (also called column) charts.
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This multiseries bar chart shows the sales of each product within each sales strategy and helps us answer the following questions:
Of course, if these are the questions, we need to compare values within each series (sales of a given product across all strategies) and in each category (sales of each product within a given strategy). With that said, let’s look at this example.
We can now clearly see that product D was the most successful in strategy 4 and the least successful in strategy 5.
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We also see that the largest sales within strategy 1 are attributed to product C, while product D is finished last, and products A and B are almost identical.
With this example, we can quickly analyze all the differences at the product level, and that’s what we use such bar charts for.
Now, what if we just want to see the total sales for each strategy and then compare them to each other? We know that some products performed better or worse than others in each strategy, but which strategy resulted in the greatest overall sales overall, regardless of individual product performance?
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To analyze only the differences between category totals, we simply add the product values and represent them as columns in a single series bar chart: c
This diagram fully answers the question we asked. It is clear that strategy 2 was generally the most effective and strategy 5 the least.
However, we can go even further and explore the relationships between what we noticed in the first (multi-series) and the second (single-series) graphs to understand more deeply what actually happened. To that end, we need to plot both category totals and product-specific data on one stage, and that’s where a stacked bar chart starts to shine.
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We can also ask, “Why did strategy X undermine the sales of a product?” Or, “What was the performance of a product within one strategy relative to its sales within other strategies?” Great questions, but remember that stacked bar charts don’t answer these or similar questions with such detail. Sorry, no superheroes here.
Stacked bar charts are designed to help you compare concurrent totals and see sharp item-level changes that are likely to have the most impact on movements in category totals.
If you look at our stacked bar chart, for example, we can clearly see that strategy 5 was the least effective overall, and this is mainly because the sales of product D were so much lower compared to the other strategies.
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Of course, if you need deeper, more comprehensive, or just different insights, you should choose a different chart type, one that helps you answer your specific questions about your specific data.
I showed the first draft of this article to some of my colleagues. One of them wondered:
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“If you want to show both category totals and item-specific data at the same time, why not use a double-axis chart with separate bars for each product and a line series for totals? Wouldn’t that be better than a stacked bar chart, om’ t it helps you understand all the differences, even at the product level?
While each category brings totals into a separate line series with its own axis and then regular bars take care of the rest sounds maybe appropriate, I don’t think so. Not to mention that I’m not a big fan of dual axis charts, at least when I have data and questions like this. But let’s look at it and you decide.
In this case, we can see the total number at once and compare data points within a category. But the main problem is that this combined diagram is actually more difficult to read, because the display is overloaded with columns, each of which attracts a lot of attention, not to mention the row series with the second one as that is not the display is cluttered.
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And things only get worse if we have a few more strategies, in this case just three more:
Large datasets are not uncommon, and while this one is far from large, I think you’ll agree that the eye gets lost in the presentation. In fact, in both cases, we have to spend too much time reading the combination of bar charts and line graphs. So, if we visualize large amounts of data, we would definitely be better off switching to another view, rather than piling everything on a map.
Now you have seen it with your own eyes. The stacked bar charts shown in this article allow us to see not only the category totals first, but also a rough but useful understanding of the item level in each category. As a result, they answered the questions clearly:
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I hope you will also agree that our stacked bar charts have proven to be accurate and easy to read.
Please consider that this chart type is very specific. Use it carefully and only if your questions and data fully match the purposes they serve. For example, to compare item values more precisely and comprehensively, choose a conventional bar chart instead, because that’s the goal. However, I am sure you now share my feeling and experience: stacked bar (column) charts are far from the “worst”, much less they are useless.
I’ll repeat myself and say that the main, strategic purpose of stacked bar charts is to help you assess the big picture at a glance, focusing on category totals
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