Course Diary #1: Basic Charts

by Enrico on February 10, 2014

in Course Diary

Starting from this week and during the rest of the semester I will be writing a new series called “Course Diary” where I report about my experience while teaching Information Visualization to my students at NYU. Teaching to them is a lot of fun. They often challenge me with questions and comments which force me to think more deeply about visualization. Here I’ll report about some of my experiences and reflections on the course.

Lecture slides for this class: http://bit.ly/infovis14-l2

In the second lecture of my course (the first was a broad introduction to infovis) I introduced basic charts: bar charts, line charts, scatter plots, and some of their variants. These basic charts give me the opportunity to talk about two important concepts: the relationship between data type and graph type (even though in a somewhat primitive way) and graphical perception.

In order to let students absorb graphical perception I spend a lot of time playing graphical trick rather than talking about theory (I’ll do that later on extensively). For instance, I show the “barless bar chart” a bar chart with dots in place of bars:

barless-barchart

But I don’t limit myself to showing these are sub-optimal charts, I invite the students to think about why, and I’ve found this very nicely and naturally introduces broader and more relevant concepts. Let me explain with an example: a line chart without lines.

time-series-dot-plot

It’s easy to argue this does not work well. Especially when you show it paired with a proper line chart. But then you ask: why? Why it does not work as well as the version with lines? I’ve found that students have to stretch their mind and think much more deeply about the issue. Heck I have to think much more deeply myself!

For instance, I realized while discussing this example in class, that a line chart without lines is a very good example of why and when visualization works best: when data understanding is supported by perceptual rather than cognitive processes. A line chart without lines forces us to trace a line between the points. We desperately need that line! It’s not that we don’t use that line at all, it’s more than we draw it in our head rather than seeing it with our eyes. We can still judge the slope and detect patterns of course but it’s much much harder (slower/less accurate)! This simple concept can be applied everywhere in visualization. You get it here, with a simple time line, and you can re-apply it in a thousand different new cases.

Another example I have shown which spurred some interesting discussion is the “colorless divided bar chart”.

colorless-divided-barchart

Once again, this one forces you to think more deeply about graphical perception. A divided bar chart with color is clearly better right? But why? Why is it better? Most students said there reason is because it’s easier to detect which bar is which: red to red, blue to blue, etc. And then I say: yes ok, but why? Why is color helping you here? After all each bar has its own position, and position is a pretty strong visual primitive to encode data (hint: it’s actually the best one). And then I explain that position here is overloaded, that is, it’s used to encode two things at the same time: the groups and the categories within each group and they get mixed up more easily without color. That’s when everyone nodded in class.

At some point I showed four different ways to display a time series (there are many more of course):

time-series-designs

When I showed that, a couple of students raised some interesting questions. One was about the line chart vs. the area chart. The area chart looks pretty good, when is a good idea to use it? One students suggested the area chart has more contrast than the line chart and this made me think that area charts are probably very good when we have lots of them in a small multiple fashion as they they create a closed shape and as such are easier to compare.

By using this technique of starting from a basic chart and stripping it down of some fundamental design elements I have found I can teach a lot. I almost stumbled into this technique by chance but I think it’s very effective and I will use it over and over again in my course.

Besides dissecting charts, another recurring question I get in class is: how do we judge if a visualization is better than another? That’s a super hard question and I am glad I get it all the time. There’s not enough space here to articulate the answer but there is one thing I stress a lot in class: you cannot judge visualization without specifying a purpose.

I think everyone in this field has a tendency to judge visualizations in absolute terms, without considering their context (I have done that multiple times too). Too many believe data visualization is only about “data + visualization” (hence the name right?), forgetting that visualization with a purpose attached is impossible to judge. And again, basic charts, in all their simplicity, offer several opportunities to expose this concept: divided or stacked bar charts? area or line chart? multiple superimposed time lines or small multiples? There’s no absolute best here.

I have only one last comment from this lecture: Tableau is awesome. Coming up with examples and quickly tweaking them by adding and removing graphical properties saved me hours and hours of time. I was initially tempted to draw these examples on my whiteboard and take pictures, then I tried Tableau and it made me smile. A big smile. This makes me also think that Tableau other than being a great analytic and presentation tool can also be used as an excellent didactic tool.

That’s all for this week. Wish me luck for my next class!

  • Krishna Mohan

    Do you have videos of the class?

    • FILWD

      Maybe later on. Cannot promise.

  • mushon

    Thanks Enrico,
    These sure are some insightful findings. I love your perceptual approach and I might steal it to teach my students in the future. I happen to be an NYU (ITP) alum myself and will be visiting ITP next month to give a talk following my Disinformation Visualization post: https://visualisingadvocacy.org/blog/disinformation-visualization-how-lie-datavis
    It might be interesting for your students to join as well.
    Take care and keep up the good work,
    Mushon

    • FILWD

      Hi! Thanks a lot for your comments. Funny enough I was reading your article last night and I found it really really interesting. I am working on similar topics and I’d love to meet/chat sometime. Please send me the details of your talk. You can write to me at enrico dot bertini at nyu … Take care!

  • http://vallandingham.me Jim Vallandingham

    Just wanted to say thanks for posting this – and I can’t wait for future course diary entries.

    A great insight into a successful way to both introduce the basics of data visualization while simultaneously encouraging deeper understanding and _actual_ learning. Plus, you get lots of hooks to dive further into the details in future lessons.

    For determining the ‘success’ of a particular visualization – I completely agree that you need the context – the purpose – of the piece in order to gauge its effectiveness.

    I wonder if this concept is taken a step further: You cannot judge a visualization without a specific purpose. And, you cannot know a purpose without a specific audience.

    Not a new idea, by any means, but I tend to view the “purpose” of a visualization as a combination of the visualizations audience and that audience’s interactions with the visualization.

    This could then be formalized a bit more by using ‘personas’ when building a visualization to enumerate and provide details of canonical examples of the intended audience.

    • FILWD

      Thanks, it’s great to hear you find it useful! I think it’s a tricky question: can you judge a visualization without a purpose? I think to some extent you can “syntactically” judge a chart but I think many people go overboard with this. I also think that judgment becomes harder and harder as complexity and scope increase. I think that having a purpose and and especially an audience in mind is crucial.

  • Jörgen Abrahamsson

    Nice course. Some simplifications I think, but that is ok.
    Maybe advanced course to follow. Just one thing I think must be commented:
    “Line chart with categorical data. Wrong!” This is conventionally held to be true, but I disagree. The logic is that lines imply that the data is (or can be interpolated to be) continuous and with categorical data this is not the case.
    Well, lines can also just be used to aid visual understanding, to create a shape. The real problem is that categorical data is undordered (alphabetical is just random, if the dataset is not the alphabet itself). This means that the shape is meaningless unless you order it by something else. Might be the quantative values or some other ordered or quantative realted data. Then you can (and I think should have) a meaningful shape from the lines (or area if you prefer). Difference between linechart and areachart being that you must use base zero with area.

    • FILWD

      Thanks! I am curious to hear: do you have any practical examples where your suggestion maybe be of use? Are you talking about some sort of glyph or what?

      • http://www.synbarligen.se Jörgen Abrahamsson

        Sure, If you have any dataset with one categorical component and one quantative or ordered component. Say, GDP of countries or some rank of companies maybe.
        The categorical component is qualatative and lack its own ordering. To show it in alphabetic order makes sense if you only are interested in one value at a a time. What is the GDP of Sweden? You can do that just as well with a data table.
        If you order by value (GDP or rank) you get a picture of the distribution of all values as well as classes within that distribution, outliers etc.
        This is clearer if you make it a closed shape as with a line or area chart. Bar or point charts are less easy to perceive as one shape. If you are very concerned with the issue of understanding line charts as continuous data try very close together bars or points instead.
        You can say the idea is to make a glyph, that is right.
        Please dont make it a spider chart because they have big problems. Never use circular diagrams unless your data is circular and probably not even then .

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