Building Custom Data Visualizations

Exploratory and Expository

Shirley Wu

Shirley Wu

Data Sketches
Building Custom Data Visualizations

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The "Exploratory and Expository" Lesson is part of the full, Building Custom Data Visualizations course featured in this preview video. Here's what you'd learn in this lesson:

Shirley discusses the two significant categories of custom data visualizations: exploratory and expository. It's explained why we will be using expository examples today, but that they are easily transferrable to exploratory data visualizations.


Transcript from the "Exploratory and Expository" Lesson

>> Shirley Wu: To. When I think of custom data visualization, usually we put them into two very broad categories. And so those two broad categories, we think of them as expository versus exploratory. Of course, there's steps in between, but these are the kind of opposite sides of the spectrum that I want to talk about briefly today.

So the way that I define expository is that it's usually with a static data set. And as we're going through and trying to build the data visualization, we'll explore the data for a story we want to tell. So we're usually trying to find some sort of insights or patterns or something to wrap into a narrative that's compelling.

And then we want to communicate that story to the audience. So that is our main goal, to communicate a story to the audience. And great examples of these are New York Times, The Pudding, The Washington Post, all the visual storytelling that they do. I've been a fan of what Pudding has been putting out just because I think they have a little bit more breathing room than a lot of the news organizations.

Like the news organizations a lot of times, they have to put out stories really quickly. And so their visualizations might be, they're really good but they might be concise and a one pager. Whereas the Pudding, they've been putting a lot of effort into visual storytelling. And so this one I really recommend as a great example of expository visualizations.

And so, let's see,
>> Shirley Wu: This is one of the ones that they did where they looked up film dialogue. And it says 2,000 screenplays broken down by gender and age. And so this is, I think, one of their earliest works. And they'll do these kinds of like, as the story goes along, the visualization matches that story.

So you can follow between the visual and the story at the same time. So there was another really great one I saw recently. Well they had did a very great one on the, I don't know if [LAUGH] it's appropriate to show or not, but they did really great one on her, Ally Wong.

And they did a visualization that kind of broke down the structure of her stand up. I feel like a lot of visual storytelling tends to be the scrolly, which I love, because you can see the story being reflected in the visualizations and vice versa. As the person scrolls, and it's so common because especially on mobile are the most basic of our interactions, are scrolling.

So it works really well that way. But this one, [LAUGH] PS, there's explicit content and audio. I don't actually know. So this one, though, is one of the click through ones. But, I just want to show you actually, that's probably better, because well, there is video. [LAUGH] But do you see how they're talking about those circles that have shown up.

Those are them visualizing the laughter. [LAUGH] So the size of the circles are the size of the audience's laughter. And I love this because if you notice it's kind of an animation that it's like timed and you just takes you through the story. And if you get impatient you can like kind of trigger it to go faster.

But it's not a form of visual storytelling I've really seen done anywhere else. And I think this was one of their experimentations, cuz they do a lot of the scrolly telling, but this was one of their, I think, let's see if we can tell a story a different way.

And I really love that. And then, and also they're just really great at this whole visual storytelling thing. I showed this at a Meet Up in Japan, and I had forgotten how rude and vulgar our content is. And then she started twerking, she started, and I was like, gosh no, I'm sorry.

But yeah, so I
>> Shirley Wu: Yeah, I wanted to use them as an example of expository visualizations. And so if you ever are in need of inspiration or just want to keep up with them, I highly recommend them. And [LAUGH] on the other side is the exploratory visualizations. And these are often times going to be a dynamic dataset that you're probably pulling out of a database.

And a lot of times, it's you probably have stakeholders, who are trying to use this visualization to potentially answer business questions. So it's usually a visual tool that you're building for the stakeholders to explore the data themselves. So instead of guiding your readers through a story of that already has the things that you found.

You're building a visual tool to guide the stakeholders and exploring the data and finding the insights themselves. And so, a lot of these, I think of as like scientific and academic research, visualizations, internal business tools at some of those giant, like Netflix, Uber, Airbnb, etc. And unfortunately I don't have examples to show you for those cuz a lot of them are like locked behind NDA.

And but it's really, really interesting how similar their use cases are to what I do a lot in my freelance with expository. But then they also, I think it kind of also builds on top of the skills sets you need to do the static data set. I have a section at the very end that talks about kind of the process for exploratory, and the advice for building exploratory visual tools.

But today we're going to concentrate on doing expository visualizations. And then I think what you can learn and take away from this, you can build on top of that to do the exploratory, internal business analytics tools. So yeah, so today we're going to be looking at a very specific data set.

And I really I wondered for a whole shower what kind of data set I should give you where everybody will know the data set. It's not so specific that it alienates people. And hopefully the top ten blockbuster movies every year for the last two decades, hopefully that's pretty, I guess cross cultural.

But these are I only made it at the top US movies so that we stick within one one culture and hopefully we'll all be able to get something out of this data set. And the goal for this is to come up with a design and implement together. So you might notice that you have some markers in front of you and that is because there's going to be some exercises in the middle in the design section where you're going to draw things.

And hopefully, we'll be able to talk about them, share about them. And maybe give feedback so that people that are watching this either at home or later on can kind of learn from what we do here. So, that's the hope, so yay participation! [LAUGH] And along that line, if there's ever anything that you're confused about, please feel free to kinda ask the questions.

Cuz that will make the workshop better for everybody watching later also. And I'm just to show you this is the raw data. It's a little bit hard to read cause it's just JSon but it's basically the movies and there's a lot of minute data on each of the movies.

But this is where you can get the raw data if you're ever interested.

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