Evolution of a complex graph. Part 1. What do you want to say?

Screenshot showing two slides. The first one is titled "low within-group variability". The second one is titled "High between-group variability". The graphs in the slides is the same

From time to time, people ask me for help with non-trivial data visualization tasks. A couple of weeks ago, a friend-of-a-friend-of-a-friend showed me a set of graphs with the following note:

Each row is a different use case. Each use case was tested on three separate occasions – columns 1,2,3. We hope to show that the lines in each row behave similarly, but that there are differences between the different rows.

Before looking at the graphs, note the last sentence in the above comment. Knowing what you want to show is an essential and not trivial part of a data visualization task. Specifying what is it precisely that you want to say is the first required task in any communication attempt, technical or not.

For the obvious reasons, I cannot share the original graphs that that person gave me. I managed to re-create the spirit of those graphs using a combination of randomly generated arrays.
The original graph: A 3-by-4 panel of line charts
Notice how the X- and Y- axes are aligned between all the subplots. Such alignment is a smart move that provides a shared scale and allows faster and more natural comparison between the curves. You should always try aligning your axes. If aligning isn’t possible, make sure that it is absolutely, 100%, clear that the scales are different. Slight differences are very confusing.

There are several small things that we can do to improve this graph. First, the identical legends in every subplot are a useless waste of ink and thus, of your viewers’ processing power. Since they are identical, these legends do nothing but distracting the viewer. Moreover, while I understand how a variable name such as event_prob appeared on a graph, showing such names outside technical teams is a bad practice. People who don’t share intimate knowledge with the underlying data will find human-readable labels easier to comprehend, making your message “stickier.”
Let’s improve the signal-to-noise ratio of this plot.
An improved version of the 3-by-4 grid of line charts

According to our task, each row is a different use case. Notice that I accompanied each row with a human-readable label. I didn’t use cryptic code such as group_001, age_0_10 or the such.
Now, let’s go back to the task specification. “We hope to show that the lines in each row behave similarly, but that there are differences between the separate rows.” Remember my advice to always use conclusions as graph titles? Let’s test how such a title will look like

A hypothetical screenshot. The title says: "low intra- & high inter- group variability"

Really? Is there a better way to justify the title? I claim that there is.

Let’s experiment a little bit. What will happen if we will plot all the lines on the same graph? By doing so, we might create a stronger emphasize of the similarities and the differences.

Overlapping lines that show several repetitions in four different groups
Not bad. The separate lines create some excessive noise, and the legend isn’t the best way to label multiple lines, so let’s improve the graph even further.

Curves representing four different data groups. Shaded areas represent inter-group variability

Note that meaningful ticks on the X-axis. The 30, 180, and 365-day marks provide useful anchors.

Now, let us go back to our title. “Low intra- and high inter- group variability” is, in fact, two conclusions. If you have ever read any text about technical presentations, you should remember the “one point per slide” rule. How do we solve this problem? In cases like these, I like to use the same graph in two different slides, one for each conclusion.

Screenshot showing two slides. The first one is titled "low within-group variability". The second one is titled "High between-group variability". The graphs in the slides is the same

During a presentation, I would show this graph with the first conclusion as a title. I would talk about the implications of that conclusion. Next, I will say “wait! There is more”, will promote the slide and start talking about the second conclusion.

To sum up,

First, decide what is it that you want to say. Then ask whether your graph says what you want to say. Next, emphasize what you want to say, and finally, say what you want to say.

To be continued

The case that you see in this post is a relatively easy one because it only compares four groups. What will happen if you will need to compare six, sixteen or sixty groups? I will try answering this question in one of my next posts

Do you REALLY need the colors?

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Look at this example from the seaborn documentation site

>>> import seaborn as sns
>>> sns.set_style("whitegrid")
>>> tips = sns.load_dataset("tips")
>>> ax = sns.barplot(x="day", y="total_bill", data=tips)

Barplot example with colored bars

This example shows the default barplot and is the first barplot. Can you see how easy it is to add colors to the different columns? But WHY? What do those colors represent? It looks like the only information that is encoded by the color is the bar category. We already have this information in the form of bar location. Having this colorful image adds nothing but a distraction. It is sad that this is the default behavior that seaborn developers decided to adopt.

Look at the same example, without the colors

>>> ax = sns.barplot(x="day", y="total_bill", color='gray', data=tips)

Barplot example with gray bars

Isn’t it much better? The sad thing is that a better version requires memorizing additional arguments and more typing.

This was my because you can rant.

 

Before and after — the Hebrew holiday season chart

Graph: Tishrei is mostly a non-working month. "Before" and "after" versions.

Sometimes, when I see a graph, I think “I could draw a better version.” From time to time, I even consider writing a blog post with the “before” and “after” versions of the plot. Last time I had this desire was when I read the repost of my own post about the crazy month of Hebrew holidays. I created this graph three years ago. Since then, I have learned A LOT. So I thought it would be a good opportunity to apply my over-criticism to my own work. This is the “before” version:

Graph: Tishrei is mostly a non-working month.

There are quite a few points worth fixing in that plot. Let’s review those problems:

  • The point of the original post is to emphasize the amount of NON-working days in Tishrei. However, the largest points represent the working days. As the result, the emphasis goes to the working days, thus reversing the semantics.
  • It is not absolutely clear what point I intended to make using this graph. A short and meaningful title is an effective way to lead the audience towards the desired conclusion.
  • There are three distinct colors in my graph, representing working, half-working and non-working days. The category order is clear. The color order, on the other hand, is absolutely arbitrary. Moreover, green and red are never a good color combination due to the significantly high prevalence of impaired color vision.
  • Y label is rotated. Rotated Y labels are the default option in all the plotting tools that I know. Why is that is beyond my understanding, given the numerous studies that show that reading rotated text takes more time and is more error-prone (for example, see ref, ref, and ref.)
  • One interesting piece of information that one might expect to read from a graph is how many working days are there in year X. One can obtain this information either by counting the dots or by looking at a separate graph. It would be a good idea to make this information readily available to the observer.
  • The frame around the plot is useless.

 

OK, now that we have identified the problems, let’s fix them

  • Emphasize the right things. I will use bigger points for the non-working days and small ones for the working days. I will also use squares instead of circles. Placing several squares one next to the other creates solid areas with less white space in-between. This lack of whitespace will help further emphasizing non-working chunks. I will make to leave some whitespace between the points, to enable counting.
  • What’s your point? I will add an explanatory title. Having given some thought, I came up with “How productive can you be?”. It is short, thought-provoking, and makes the point.
  • Reduce the number of colors. My intention was to use red for non-working days, and blue for the working ones. What color should I use for the half-working (Chol haMoed) days? I don’t want to introduce another color to the improved graph. Since in my case, those days are mostly non-working, I will use a shade of red for Chol haMoed.
  • Improve label readability. One way to solve the rotated Y label problem is to remove the Y label at all! After all, most people will correctly assume that “2006”, “2010”, “2020” and other values represent the years. However, the original post mentions two different methods to count the years, using the Hebrew and Christian traditions. To make it absolutely clear that the graph talks about the Christian (common) calendar, I decided to keep the legend and format it properly.
  • Add more info. I added the total number of working days as a separate column of properly aligned gray text labels. The gray color ensures that the labels don’t compete with the graph.  I also highlighted the current year using a subtle background rectangle.
  • Data-ink ratio. I removed the box around the graph and got rid of lines for the X and Y axes. I also removed the vertical grid lines. I wasn’t sure about the horizontal ones but I decided to keep them in place.

This is the result:

tishrei_working_days_after.png

I like it very much. I’m sure though, that if I revisit it in a year or two, I will find more ways to make it even better.

You may find the code that generates this figure here.

 

Chart legends and the Muttonchops

Adding legends to a graph is easy. With matplotlib, for example, you simply call plt.legend() and voilà, you have your legends. The fact that any major or minor visualization platform makes it super easy to add a legend doesn’t mean that it should be added. At least, not in graphs that are supposed to be shared with the public.

Take a look at this interesting graph taken from Reddit:

The chart provides fascinating information. However, to “decipher” it, the viewer needs to constantly switch between the chart and the legend to the right. Moreover, having to encode eight different categories, resulted in colors that are hard to distinguish. And if you happen to be a colorblind person, your chances to get the colors right are significantly lower.

What is the solution to this problem? Let’s reduce the distance between the labels and the data by putting the labels and the data together.

Notice the multiple advantages of the “after” version. First, the viewer doesn’t need to jump back-and-forth to decide which segment represents which data series. Secondly, by moving the legends inside the graph, we freed up valuable real estate area. But that’s not all. The new version is readable by the colorblind. Plus, the slightly bigger letters make the reading easier for the visually impaired. It is also readable and understandable when printed out using a black and white printer.

“Wait a minute,” you might say, “there’s not enough space for all the labels! We’ve lost some valuable information. After all,” you might say, “we now only have four labels, not eight”. Here’s the thing. I think that losing four categories is an advantage. By imposing restrictions, we are forced to decide what is it that we want to say, what is important and what is not. By forcing ourselves to only label larger chunks, we are forced to ask questions. Is the distinction between “Moustache with Muttonchops” and “Moustache with Sideburns” THAT important? If it is, make a graph about Muttonchops and Sideburns. If it’s not, combine them into a single category. Even better, combine them with “Mustache”.

Muttonchops
Muttonchops. By Flickr user GSK

Having the ability to add a legend with any number of categories, using only one code line is super convenient and useful, especially, during data exploration. However, when shared with the public, graphs need to contain as fewer legends as practically needed. Remove the legends, place the labels close to the data. If doing so results in unreadable overlapping labels, refine the graph, rethink your message, combine categories. This may take time and cause frustration, but the result might surprise you. If none of these is possible, put the legend back. At least you tried.

Chart legends are like Muttonchops — the fact that you can have them doesn’t mean you should.

Evolution of a Plot: Better Data Visualization, One Step at a Time

My latest post on data.blog

Data for Breakfast

The goal of data visualization is to transform numbers into insights. However, default data visualization output often disappoints. Sometimes, the graph shows irrelevant data or misses important aspects; sometimes, the graph lacks context; sometimes, it’s difficult to read. Often, data practitioners “feel” that something isn’t right with the graph, but cannot pinpoint the problem.

In this post, I’ll share the process of visualizing a complex issue using a simple plot. Despite the fact that the final plot looks elementary and straightforward, it took me several hours and trial-and-error attempts to achieve the result. By sharing this process, I hope to accomplish two goals: to offer my perspectives and approaches to data visualization and to learn from other options you suggest. You’ll find the code and the data used in this post here.

Plotting power distribution in the Knesset

This post is devoted to a graph I created to explore…

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