Episode 34: How to Harness the Power & Beauty of a Box Plot - Featured Data Visualization by Eric William Lin

Welcome to episode 34 of Data Viz Today. When's the last time you saw a box plot? How about the last time you created one?! It's been a long time for me, but this week's featured data visualization by Eric William Lin has convinced me to reconsider using this often clinical chart type as a beautiful and powerful way to tell a story. In this episode, we'll hear how Eric built his Kantar IIB Shortlisted viz, plus a few suggestions for how and when you could try a box plot!

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is all about the classic chart type - the box plot! Also known as the box and whisker plot. We’ll talk about the visualization that inspired me to reconsider the beauty and functionality of a box plot, how it was built, plus a few suggestions how and when YOU could try a box plot!

  • 01:02 - Today’s featured data viz is called Casting Shakespeare: How age, gender, and race affect casting by Eric William Lin

  • 01:10 - Eric is a musician-turned-software engineer based in New York City. He occasionally teaches classes in programming and has recently become obsessed with designing data visualization, which has led to this featured visualization showing up on the shortlist for the Kantar Information is Beautiful Awards! Public voting is open til the 19th so vote for this viz!

  • 01:40 - The spark that led to this shortlisted viz was actually from Shirley Wu - she was featured in episode 4 How to Find Answers in Survey Results. And last year, she gave a talk at a Javascript meetup in Brooklyn about her beautiful visualization of all the words in Hamilton the musical for The Pudding. Turns out that Eric was in the audience and having been a high school theater kid and music major, the thought of visualizing theater seemed like a really exciting way to combine his two loves - coding and theater.

  • 02:15 - He began brainstorming about which plays to focus on and what would be an interesting angle, which can be a big struggle like we talked about in the last episode.

  • 02:25 - But the first piece in the puzzle for Eric was that he remembered that the New York Philharmonic had open-sourced their performance history data, so while looking through the dataset, he decided to focus on Shakespeare plays, but with a twist - instead of focusing on the lines of text, he would focus on the characteristics of actors who have acted in those Shakespeare plays at over time. Like the age, gender and race of the actors.

  • 02:55 - So he began gathering data for that, and said this turned out to be the most difficult part of the project. All that information was scattered around on different sites, in different formats, or not available at all. He had to scrape a lot of data from production websites using python, and deduce some actors ages from an old article that referenced their age and compare it to the production date of the play.

  • 03:20 - But once he got everything that he needed, Eric was able to move onto the fun, creative part - visualizing the data. His first instinct was to create 2-dimensional scatter plot. The x-axis would the year of the production, and the y-axis would be the age of the actor at the time of production. Then do this same scatter plot for each character, and present it as a series of small multiples. But he quickly realized that showing the actors like this would make it hard to visually tell a story or a narrative about interesting patterns in the data…

  • 04:00 - His breakthrough moment was realizing that he could frame the story around the actor’s perspective. What if instead of looking at each character one-at-a-time and looking at how they were cast historically year-by-year, he could ask: As an actor, what roles are available to me at my current age? What roles should I audition for, and what characters would directors cast me in based on past data.

  • 04:40 - This led him to the box plot - he could show the distribution of ages for each character side-by-side, and another innovative benefit of using the boxplot - he could slowly reveal the boxplot to tell a story of an aging actor - like now you’re 30 years old, you’re probably not going to be cast as Romeo or Juliet because 75% of actors who played those roles were under 30.

  • 05:15 -Final visualization was built with JavaScript, D3.js, Aliza Aufrichtig’s Coordinator, and Susie Lu’s d3-annotations - you can hear more about that in episode 7 How to Annotate Like a Boss with Susie Lu!

  • 05:25 - Experience the viz here!

  • 07:40 - Eric showed us that box plots can be beautiful and aid in storytelling, but let’s get a quick refresher on what a box plot is, and then we’ll talk about some pros/cons, and some variations.

  • 07:53 - A boxplot is a standardized way of showing the distribution of data. It gives you a quick way to see how your data points are spread out. If someone told you the median of a dataset, you don’t know if most of the points are clumped around that value, or if they’re spread out.

  • 08:13 - In a box plot, there’s a specific mark to show the median, the lower and upper quartiles, the upper and lower fences, and any outliers. Listen for a more detailed description of how to build one. Check out Nathan Yau’s extremely helpful blog post about how to read a box plot:

  • 09:55 - Pros: you can garner a lot of information about the distribution by these couple of marks, and they don’t take up a lot of room, so if you try show distribution with a histogram or a density plot, then it’s harder to put them all side by side and compare. But it’s easy to stack up box plots into one chart and compare distributions among various groups.

  • 10:25 - Cons: The benefit of something like a histogram, is that you can see more detail. The box plot is using summary statistics, so you don’t have any control over the granularity, like you would with a histogram by varying the bin size. It also hides the sample size, so you might compare groups with separate box plots, but it could be a little misleading if your sample size for each group varies widely. You could annotate it, or I like what Eric did by actually showing the points with slightly transparent dots behind the box plot. The box plot is also less intuitive for some people, but you could mitigate that by doing what Eric did and show a How to Read chart beforehand.

  • 11:25 - Check out box plot variations from Data Viz Catalogue!

  • 12:04 - Box plots in the wild:

    • New York Times - they showed projected career earnings for college graduates, and they had a box plot for each major and you could see the median and the spread of the projected earnings in dollars for each one.

    • FiveThirtyEight showed the median and spread of yelp reviews for restaurants with different Michelin Stars. I liked that they have an inset box that explains what the box plot shows.

  • 12:50 - Tools that make box plots: Tableau, RAWGraphs, Excel...

  • 13:20 - My final takeaway is that next time you’re visualizing the distribution of points and also want to compare distributions across many groups, consider using a box plot. It’s a clean way to show distributions, and you can experiment with different variations to show more detail, and even use it as a storytelling tool like Eric did! Just make sure your audience understands how to read it because it could’ve been a minute since they learned about box plots in math class.

  • 13:30 - Listen for Eric’s amazing advice to designers just starting out!

  • 14:55 - You can follow him on Twitter, and check out his website.

  • 15:05 - I'm sharing my essential Adobe Illustrator tips in my new course! Check if it's right for you HERE!

Allison Torbanlin, boxplot
Episode 33: [Mini] How to Discover Relevant Stories in Your Data by Taking an Editor’s Perspective

Welcome to episode 33 of Data Viz Today. How can you consistently generate interesting visual story ideas from your data set? I’ve been on a quest to find a process for this, and I recently found guidance in a book for authors who are trying to get their non-fiction short stories published by editors. I used what I learned to create a worksheet that brings me from a basic stat to eight story ideas! In this episode, you’ll learn about the reasoning behind it and hear it in action. DOWNLOAD THE WORKSHEET

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 01:34 - In this episode, I’ll share the 3 things I learned that editors look for when they publish non-fiction short stories, how that led me to creating a data viz workflow diagram that takes me from one basic stat to eight relevant story angles, and of course I’ll show it in action with an example!

  • 01:53 - If you’re ready to create fully customizable charts in Adobe Illustrator, check out my new course → Design Your First Visualization in Adobe Illustrator in Under 30 Minutes

  • 02:43 - The book I was reading is called The Byline Bible by the writing professor Susan Shapiro. It’s a guide for authors of non-fiction short stories on how to get published in magazines and newspapers. I saw so many parallels between her advice to writing relevant short stories that will get an editor to publish you, to creating a visualization out of a data set that’s going to mean something to someone.

  • 03:20 - So from the book, there were three pieces of advice for getting published that I thought were super relevant to finding a story in data.

1. Avoid the obvious. You want to focus on drama, conflict, and tension. Susan Shapiro says in her book: “confront unresolved emotional issues about something that’s bothering you.” What’s the use in visualizing something that everyone already knows?

2. Make it timely. You need to compel your reader with a fresh angle or a reason why now is the time to take notice.

3. Clarify your emotional arc. Susan says “start in delight, end in wisdom” - you want to start strong, introduce conflict, and have a resolution. For data viz, the start strong part I think is wrapped up in the visuals - to varying degrees you’ll use your design to catch someone’s eye, then your angle on your data will introduce the conflict and possibly resolution, depending on whether it’s exploratory or explanatory.

  • 04:25 - Ok, then I took these three elements and made a workflow diagram out of it to use before I do any analysis to get me warmed up and ideally take me from one statistic or fact, and turn it into 8 possible interesting angles to pursue.

  • 04:51 - First, write your stat or fact at the top. Then we move into conflict: If that stat is true, then what’s the consequence? Who is affected? Then tackle the timeliness of each consequence: Why is this important now? If it’s not, what can I compare it to that is important now? Then think about possible resolutions: What can help? What action can we take?

  • 06:10 - Listen for my example using the Makeover Monday dataset on avocados!

  • 08:44 - My final takeaway is that you can take an editor’s viewpoint, and squeeze interesting angles out of your dataset so that your visualizations are telling a compelling story. So try out this workflow, and let me know if it’s helpful to you! Remember you’re looking for Conflict, Timeliness, Resolution.

Allison Torbanmini, editor
Episode 32: How to Add Impact & Inform Your Reader by Handing Over the Power - Featured Data Visualization by Ludovic Tavernier

Welcome to episode 32 of Data Viz Today. How can you add interaction to make your story more impactful and memorable? In this episode, host Alli Torban explores specific interaction techniques that you can try in your visualizations to more effectively inform your reader. Featured data visualization project by Ludovic Tavernier perfectly shows how handing over the power to your reader can create an engaging experience.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:21 - Today’s episode is all about giving your reader ways to interact with your visualizations so that your story is more impactful and memorable.

  • 00:30 - In this episode, we’ll talk about the visualization that inspired this, how it was built, and 3 specific interaction techniques that you can try in your visualizations to more effectively inform and teach your reader.

  • 00:45 - First I have a quick announcement - many of you have asked me for resources on how to get started making visualizations in Adobe Illustrator, so I finally made my own resource! I just launched a new course called Design Your First Visualization in Adobe Illustrator in under 30 minutes, which has 6 short videos that walk you through everything you need to know to start creating and customizing charts in there. This is basically the course that I would have wanted to take a year ago when I started learning - after hearing almost everyone that I interviewed say they used Illustrator for design and annotations. So I’m excited to provide this shortcut to you now. you can enroll at dataviztoday.com/courses and use the coupon code PODCAST for 25% off until October 10th.

  • 01:35 - Today’s featured data viz is called “The Amazing Letter E” by Ludovic Tavernier. Ludovic is the Data Visualization Lead of Valoway, a french consultancy firm that specializes in the data sector. He recently won the first feeder competition of the Tableau Iron Viz Contest with this visualization that I’m featuring today.

  • 01:55 - This is actually the second featured viz that came out of an Iron Viz competition  - the first one was in episode 25.

  • 02:50 - He studied past winners and saw that he needed to really focus on 3 elements that are really important to the judges: analysis, storytelling, design.

  • 03:13 - He finally settled on telling the story about the letter E.

  • 03:30 - Then he began sketching how he’d lay out this story in his viz. He also wanted to add a mix of simple and complex charts. Some basic charts to reassure most readers, and some complex ones to engage the experienced readers.

  • 04:04 - When he had sketched out the sections of his viz and chose his chart types, he started creating it in Tableau. The data he used was text from various books around the web. I was surprised to learn that his data table has 10 rows and 4 columns (title, author, year and excerpts). Then he let Tableau to do all the hard work with formulas. For example, he took the excerpt from the book and had tableau divide it into letters and then added calculations to add up the frequencies of the letter E in each one.  

  • 04:52 - And the beautiful thing about Tableau Public and the Tableau community, is that most people make their dashboards downloadable, which Ludovic did, so you can download the actual Tableau file and dig into how he did it so you can recreate it too.

  • 08:09 - So the Tableau wizardy aside, I think the true genius in Ludovic’s viz was that he told the story of the letter E by giving us, the reader, the power to control the story, which to me, has 3 big benefits.

  • 08:25 - The first instance, where Ludovic lets you slide the bar to reveal the line chart so you can see the letter E’s frequency stabilize around 33%, that gave me the power to reveal the chart at my own pace and it almost made me feel like I was drawing the chart, which makes it more memorable.

  • 08:53 - The second example where Ludovic lets you choose a book from his bookshelf to explore or when he lets you enter your own text at the end, that gives you the power to choose what you’re most curious about. And when you let someone explore, guided by their own curiosity, they’re more engaged and have a more memorable experience, like we talked about in Episode 6.

  • 09:20 - The third example is when Ludovic lets you walk through building his complex circular connection yarn-ball viz, word by word. He took a complex viz and gave me the control to build it slowly so I could understand it at my own pace. Everyone learns at a different pace, and letting me walk through it allows me to truly understand what’s going on and appreciate the viz that much more.

  • 10:00 - My final takeaway is that you can really engage your reader and teach them something they’ll remember by giving them control over the narrative.

    • You can do this by allowing them to reveal or draw a chart themselves,

    • give them a choice of the example they explore, or

    • allow them to walk through and build up a complex viz at their own pace.

  • 10:18 - Benjamin Franklin said “Tell me and I forget. Teach me and I remember. Involve me and I learn.”

  • 10:27 - Think about how you can add interaction so you can add impact & inform your reader.

  • 10:35 - Ludovic’s advice to designers just starting out: “Just practice, don’t be afraid to show your work. We all start from somewhere!”

  • 10:46 - You can follow him on twitter @ltavernier7

  • 10:55 - And don’t forget to use coupon code PODCAST by October 10th in order to get 25% off of my Adobe Illustrator course!

Episode 31: How to Decide If Your Visualization Should Be 3D - Featured Data Visualization by Ryan Baumann

Welcome to episode 31 of Data Viz Today. When is it beneficial to visualize your data in 3D? We know that people love the “cool” factor of 3D, but I think most people know to avoid it now. But in which cases could it be useful in understanding your data? In this episode, host Alli Torban explores the pros and cons of 3D viz. Featured data visualization project by Ryan Baumann shows a beautiful way to make custom 3D visualizations of spatial athletic data.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:28 - Today’s episode is about how to decide if your visualization should be in 3D. When does 3D benefit your viewer?

  • 01:00 - In this episode, we’ll talk about the visualization that sparked my curiosity about the third dimension, how it was built, and what to consider when you’re tempted to make your dataset 3D.

  • 01:14 - Today’s featured data viz project is a book called “Athlete Data Viz” by Ryan Baumann. He’s a former pro-cyclist and an engineer at Mapbox where he collaborates with customers to develop new products. His side project is a data visualization tool for athletes called Athlete Data Viz, which is a website that Ryan created where anyone can visualize their athletics or training in a custom way on a map.

  • 03:03 - So how did Ryan get into this cool little side hustle? As an athlete, he’s always loved tracking and visualizing his performance on his bike rides. One day in 2014, a post on the Strava subreddit caught his eye… someone named Kevin was trying to build a heatmap of his Dad’s rides for Father’s Day. Ryan loved this idea and built out a proof of concept of how it could be done and reached out to Kevin to ask if he could help him.

  • 03:45 - How? He uses the Strava API along with Mapbox data and custom styles to gather the data for the app. A user logs into their Strava account and can pull their latest ride to visualize over different map styles. Listen for more of the technical aspects explained!

  • 05:30 - He really wanted to make something that felt more like art than business intelligence, and give people the joy of commemorating a great race or experience by creating something beautiful and unique.

  • 05:45 - Tools used: Ryan used Mapbox GL JS, javascript, Postgres + PostGIS extension, Python Flask for the backend API, and Heroku for the platform.

  • 06:08 - If you want to try this out and make a beautiful map of your athletic data, you can head over to athletedataviz.com.

  • 06:16 - I liked Ryan’s idea of making paths 3D especially when the height of the path is determined by elevation. That feels like a natural and helpful use of 3D to me.

  • 06:30 - But then I began to wonder what is it exactly that makes 3D visualizations a bad choice in most situations?

  • 06:50 - I’m currently reading Tamara Munzner’s Visualization Analysis and Design and she has a section is called “No Unjustified 3D”, which I found refreshing because it wasn’t just “Never 3D”, but a carefully laid out section about the pros and cons. I like the idea of keeping 3D in my toolbox as long as I also have the knowledge of its strengths and limitations.

  • 07:20 - My two big takeaways from Tamara’s analysis is that 3 dimensional visualizations

    • 1 - have the problem of occlusion and

    • 2 - have the problem of perspective distortion.

  • 07:30 - For #1, occlusion means to obstruct, so when you show data in 3D, some data might be obstructed by other data, which can be helped by making the visualization interactive, but this comes at the cost of the viewer’s time and cognitive load, since the viewer is having to construct a mental model by remembering past views, which is especially hard with abstract data.

  • 08:00 - And for #2, the perspective distortion problem, when you plot data in 3 dimensions, the objects that are further away appear smaller, which is a problem when you’re trying to compare the size or length of objects.

  • 08:15 - So you want to keep your data in 2D when relative position is important - when you need to precisely judge the distance or angle between objects.

  • 08:25 - A time when 3D is easier to justify is when the viewer’s task involves shape understanding of inherently 3D structures, which is usually spatial data.

  • 08:45 - This made me curious about what people are using 3D models for in the real world, so I asked my sisters, who are both engineers, whether they ever visualize 3 dimensional data, and one said she does because she runs simulations of air or water flow around an object so if she graphed it in 2D, she’d miss out on what’s going on on the other side, especially when the object isn’t symmetrical.

  • 09:15 - It was a similar case for my other sister who said she uses 3D models to see how atoms interact with magnetic fields and lasers, so seeing the full picture in all 3 dimensions is essential.

  • 09:27 - This is actually similar to the example that Tamara used in her book to show a well-justified use of 3D. She showed a visualization of streamlines of fluid flow through a volume. In these cases, you’re trying to understand the shape of data in a 3D space.

Example of 3D visualization of abstract data that works well. From Tamara Munzner’s book Visualization Analysis & Design.

  • 09:44 - When you try to apply 3D to abstract data though, it gets a lot trickier to justify.

  • 09:55 - Tamara had one great example in her book where she shows a visualization of voltage readings over time, but in the 2D view, all the readings are graphed over each other so it looks like all the readings are about the same, but this interactive viz allows you to kind of pull it out like a drawer and expand all the readings out like an accordian, revealing this 3D view that shows that half the readings actually look different than in the 2D view, but since so many readings were on top of each other, the fact that the shape of the readings was changing over time was obstructed.

  • 10:35 - The important benefit of 3D was the shape of the data as time progressed. You could see how the data changed in a way that wasn’t clear in the 2D view.

  • 11:02 - Cons of 3D:

      • 1 - When you extrude objects, you might block some data behind it - called occlusion.

      • 2- You introduce perspective distortion and it becomes difficult to compare lengths and angles accurately.

  • 11:17 - Pros of 3D:

      • 1 - You can quickly understand shape.

      • 2 - Helpful when the viewer needs to understand the 3D geometric structure of objects or a scene.

  • 12:00 - If you have a use case for extruding buildings or other shapes on a map, I actually just made a series of short video tutorials for Mapbox on how to do this.

  • 12:15 - My final takeaway is that I think Tamara Munzner summed it up perfectly in her book by naming the section “No unjustified 3D”. Not never 3D, but take note its limitations and keep it in your toolbox.

  • 12:35 - Finally, I asked Ryan what’s his advice to designers just starting out, and he said as a beginner using color in your visualizations, it’s too easy to tell a story you don’t intend or that even misrepresents your data. So keep your colors simple - start with well known designer palettes for data visualizations that you can find in tools like colorbrewer.

  • 13:00 - You can follow him on twitter @RyanBaumann and head over to athletedataviz.com to make your own visualization of your training data!

  • 13:12 - I’m finishing up another course, this one is on creating and editing visualizations in Adobe Illustrator, so if that’s something that you’re interested in learning, you can sign up for my newsletter to get notified of its launch and maybe a coupon code too… :)

Allison Torban3d, baumann
Episode 30: [Mini] How to Use Help Desk Tactics to Build More Useful Visualizations

Welcome to episode 30 of Data Viz Today. Do you ever have a client that's not sure what they need, and you also feel at a loss on how to visualize their goal in the most useful way? In this episode, I talk about how I'm bringing back my old Help Desk skills to break through that wall (or vizzer's block ;D) and dig up useful dashboard ideas.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:30 - Today’s topic is about breaking through that wall that you hit when you’re trying to create a data viz for a client but they don’t really know what they want but you also feel at a loss on what to do to help.

  • 00:45 I had a low-stakes version of this this past weekend, where I asked my husband if he wanted me to create a dashboard for his new workout plan so he could track his progress but he said he didn’t think it’d be helpful in this case. I knew there was probably something that I could do, but didn’t know exactly what…I realized that I could use my experience on a Help Desk to breakthrough that wall.

  • 01:15 - I was on this small Help Desk team at the Pentagon a while ago, where I helped people who used our custom software learn how to use it and I’d also answer calls to troubleshoot any issues they had. If you’ve never been on a Help Desk, let me tell you what it’s like...It’s like trying to drink from a firehouse, and instead of water, it’s spraying lots and lots problems. People only call you when their work’s been interrupted, they’ve wrestled with the software and have now become so frustrated that they dig up your number to get the solution ASAP. When people call, they have a wide range of abilities in terms of how well they can communicate what they’re experiencing and where their problem is. It was really frustrating when I first started because I’d just take the information someone gave me, hung up and I’d run with it, usually spinning my wheels. But as I got more experienced with the software and how to deal with people, I started to know which questions to ask to get to the problem quickly, and also get to the solution quickly.

  • 02:30 - I’m sure as I’m saying all this, you can easily draw the parallels between being on a Help Desk and consulting with a client about their data viz project. So I wanted to share a few key questions that I found useful while on a Help Desk, and then show you how I used these questions on my husband to go from nothing to lots of dashboard ideas.

  • 03:03 - So my husband started this new workout routine last week called 5x5 where you do 5 sets, 5 reps each of some exercise with a certain amount of weight, and then each week you increase the weight. The idea being after like 2 months you’ve significantly increased the weight you’re able to lift. I’ve created dashboards in the past for him so he can track his progress on new workouts, so I asked him if he wanted one for this endeavor…but he said he didn’t think he needed one because he’s just increasing the weight the same amount each week so there’s not really much to track. And I thought he had a good point, and we left it at that. But I kept thinking that there had to be something that would be useful to track to inform him about his progress, and then I had that thought about being on a Help Desk…there are techniques that I know that can help me get more information from someone with a goal.

  • 04:04 - So I convinced my husband to let me ask him questions to see if I could break through this wall and see if I could create a dashboard that would help helpful…

  • 04:15 - First thing I did when someone called the Help Desk is ask what they were trying to do when they got their error. I want to start with their goal so I know where we’re going.

  • 04:30 - Then, second, I want zoom out to get as much context as possible to see what they’re seeing. Start as zoomed out as possible and zoom in. People usually want to just tell you the zoomed in issue (“document 126 is stuck”)...but if you start chasing document 126, you might realize 3 hours later that you’re in a different application than they’re in. So zoom out and get more details...What browser are you using? Which application are you in? What type of document is this? What were you doing with it when the error occurred? What exactly did the error message say? I want to feel like I’m in that person’s seat, using the same application, trying to do the same task and achieve the same goal. So I know their goal, and I know the context around the situation, so I now I have enough information to go try to investigate behind the scenes

  • 05:30 - The third part is that I’d recreate the issue in the test environment, see if I got the same error, then I’d start testing some hypotheses on why this error is happening… if I change this, can I get around the error and achieve the goal? What about this? I’m trying to hone in on the things that introduce problems and throw the goal off. Then I can tweak whatever I need to and report back to the user with the solution.

  • 05:50 - The most important questions to ask to solve a help desk issue efficiently are

1. What’s your goal?

2. What’re you seeing?

3. What’s causing the problem?

Goal, context, cause

  • 06:10 - So back to the workout …. I asked my husband these questions about his new workout plan.

1. What’s your goal? He said to get stronger. Each week increase the weight he can lift by a certain number each week over 8 weeks.

2. What’re you seeing? What’s the context? What are the things you can measure that’s around this goal? He said measurable things around this goal are the amount of weight and number of reps he completes at each workout, and whether he needs to repeat the workout because he couldn’t complete the last one.

3. What’s causing the problem? What’re some measurable things that could throw the goal off? He said things that might make him not be able to finish a workout at a certain weight is his protein intake, or the type of workout he did the day before.

  • 07:05 - After gathering all these answers, I went from a shoulder shrug and “there’s not really any useful way to visualize progress for this” to a bunch of ideas of things that I could visualize and build into an interactive dashboard that he could use to track his workouts - amount of weight use, workouts completed or repeated, protein intake, off-day workouts…The idea being that by tracking all those things, he can keep close tabs on where he is along the path of achieving his goal, and start to see patterns around what’s affecting his progress.

  • 07:40 - It was a really fun to try this in a no-stakes situation so I could kind of flesh out this idea… and I look forward to trying to work through these questions the next time I feel like I’m hitting a data viz wall with a client...

  • 07:50 - My final takeaway is that when you’re trying to build a data viz that will be useful to your client but you’re feeling stuck, try getting in the Help Desk mindset to uncover the metrics that are meaningful to the goal.

    1. What’s your goal? How can it be measured?

    2. What’re you seeing? What’s the context? What are the things you can measure that’s around this goal?

    3. What’s causing the problem? What’re some measurable things that could throw the goal off?

    4. Goal, context, cause

  • 08:21 - A bonus technique that’s helpful on a Help Desk AND when building data viz for clients is make it a priority to build trust. When you have a trusting relationship, those questions go a lot smoother. So make sure to really listen and leave your ego at the door.

  • 08:50 - If you’ve been enjoying the show, it would mean a lot to me if you could leave me a review in iTunes! :)

Allison Torbanmini, help desk
Episode 29: 3 Essential Steps To Finding Your Unique Style - Featured Data Visualization by Federica Fragapane

Welcome to episode 29 of Data Viz Today. How can you find your unique data viz style? I've started my quest to find mine, which I hope will help me find my voice and create work that’s more representative of my point of view. I know it’s not something that happens overnight, but what can I do to get started? Featured data visualization project by Federica Fragapane provides plenty of inspiration for how to get on the right path.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:22 - Today’s episode is about how to find your unique style. How can I take a step toward defining my own style and unique data viz point of view?

  • 01:00 - Today’s featured data viz project is a book called “Planet Earth” illustrated by Federica Fragapane. Federica is an Italian award winning freelance information designer.

  • 02:15 - So how does someone get into designing a kids book? Federica had started to experiment with combining data visualization and illustrations, and a publisher had seen some of her projects and approached her for a collaboration. They were looking for someone to design a whole children’s book combining data visualizations and illustrations, which fit exactly with her experimentations.

  • 02:30 - She collaborated with Chiara Piroddi, who is a psychologist and helped Federica design the infographics with children in mind. Some tips for designing data viz for kids: create a familiar connection with the shapes - have certain shapes and colors repeat often, and create legends in many places so they have all the information they need to understand the visualizations.

  • 04:00 - What was Federica’s design inspiration for this book? She told me that she wanted to create a joyful connection between the pages and the readers, so she actually went back and flipped through her own kids books that she read as a child. She still had them, and it helped her recall colors, shapes, and details that really brought out positive feelings for her when she was a kid. So she used those positive feelings and the visual elements that conjured them up as your starting point and worked from there to develop the style of the book. Federica looked at illustrations from her audience’s point of view, but she even took it a step further and sought out illustrations were meaningful when she was the intended audience.

  • 05:06 - Federica used Adobe Illustrator for the visualizations and Photoshop for coloring her hand-drawn illustrations.

  • 05:20 - Get the book on Amazon!

  • 05:50 - I love how Federica has a really unique style, and it inspired me to start defining my own personal style. How can I get to the point where people see my data viz and instantly know that’s from me?

  • 06:10 - Reminded me of data viz style guides used at companies. Check out Jon Schwabish’s curated list of style guides from around the world.

  • 07:30 - The thing about style guides is that they’re built with the company’s brand in mind, but also with their audience in mind. What color complexities and formats work best for their audience. Just like Federica does - she uses her audience as a starting point for her design inspiration.

  • 07:53 - I thought this was a perfect first step to defining my own style - get in the mindset of your audience.

  • 08:20 - Let’s build this out in 3 actionable steps…

    • #1 - Who is my audience? Who am I designing visualizations for? Is there a Style Guide in my organization? List out the “cracks” in the style guide where you can inject your own style. There might be certain colors and fonts that I have to use, but maybe font size and line style are free game. Or I use certain patterns and strokes to highlight certain areas that would look unique. Maybe there are certain techniques that I could use like we talked about in episode 27 about Edward Tufte’s book where he suggested some techniques for erasing non-data ink like the range frame. Federica uses a lot of circles, curved lines, small multiples, and plays with opacity, shading and layering… all things that give her a unique style that she’d probably be able to bring with her into many situations.

    • #2 - Build inspiration boards of designs that you catch your eye. Like color palettes, shades, fonts, spacing, lines styles, and chart techniques. Federica told me that she’s constantly looking for visual inspiration, even if she doesn’t have a specific project in mind. She’s learned that her eyes are attracted to certain shapes, colors and elements. She seeks out the visual elements that give her positive feelings and works on incorporating them into her work so that she can recreate that joy. So try scrolling through pinterest and pin the images (data viz or not) that make your eyes light up and bring you joy. Keep an eye out for different color palettes, shading, shapes, lines, corners, edges, spacing. All those little things….If you missed episode 26 that’s a great one to help you zone in on the tiny, specific elements of great design.

    • #3 - Embrace your evolution. Your style is going to change over time, and it’ll probably need to change from project to project depending on your audience, so don’t hold too tightly and just keep experimenting. So I’m just going to add anything and everything, knowing that my style is going to evolve over time.

  • 13:45 - My final takeaway is that the 3 essentials steps that you need to take in order to define your own personal data viz style are

    1 - Define the parameters around what your company or audience needs, and then identify which design elements are free for you to play with. Even with strict style guides, I bet you can find some cracks.

    2 - Start with one image that really brings you joy or you wish had your name on, and search Pinterest for similar image. Build a board of inspiration with color palettes, shading, shapes, lines, corners, edges, or spacing that you like.

    3 - Keep in mind and embrace that your inspiration is going to change and evolve over time and with each project so go with it and keep experimenting and refining.

  • 14:38 - Eventually we’ll turn the corner and create work that people can immediately identify as ours… just like the beautiful work of Federica.

  • 15:15 - You can keep up with all her work on Behance and on Twitter

  • 15:30 - Check out my Resources page for links to all my favorite books, blogs and tools!

Episode 28: How to Build a Connection With Your Data Through Original Visualization - Featured Data Visualization by Sonja Kuijpers

Welcome to episode 28 of Data Viz Today. Is it ever beneficial to stray from the usual chart types and create your own original, novel data visualization? i.e. A viz where you decide what each free-form shape, line, and color represents. In this episode, host Alli Torban explores how this technique can lead to a deeper connection with your data. Featured data visualization by Sonja Kuijpers perfectly illustrates how creating an original visualization can turn overwhelm into clarity.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:30 - Today’s episode is about how to build a connection with your data by creating original visualizations. And by original, I mean something that’s out of the typical chart type (bar chart, line chart, scatter plot), where you decide what each shape, line, and color represents and build a visualization from it that represents some dataset that you have.

  • 01:15 - Today’s featured data viz project is called “Keuzestress” by Sonja Kuijpers

  • 01:20 - Sonja is an information graphics designer who runs her own company called Studio Terp based in the Netherlands.

  • 01:35 - Her viz Keuzestress was a personal project of hers, translated from Dutch it means Choice Stress.

  • 01:45 - Sonja was searching for a mascara that fit her needs, but soon found out that the there’s an overwhelming number of decisions that you need to make in order to choose a mascara - add length, add volume, add curl, or how about all three? How can she pick one?

  • 02:10 - Well, since she’s an information designer, she decided to create a viz out of all the information.

  • 02:20 - She scraped the data on all the mascaras that a Dutch makeup webshop supplied using Parsehub, which is a free web-scraping tool. And she cleaned it up in Excel.

  • 02:50 - So she took the characteristics that she wanted to visualize and started drawing some forms that she felt fit for that characteristics, like a black circle to represent a black mascara and a thick grey ring to represent adding volume, and she put all the little shapes that she came up with and put them on top of each other and then she realized that all the shapes together actually looked kind of like an “eye”.

  • 04:00 - Then she was finally able to give her mascara anxiety a little bit of order and put each one in its place amongst the others. She identified the couple of specifications that she wanted and was able to put her finger on the exact one she wanted. The final viz was created using Adobe Illustrator.

  • 04:55 - By putting the mascara choices into a custom-styled data viz, she was able to take something that was giving her anxiety and turn it into something more tangible that she could sort, order, and connect with.

  • 05:35 - Sonja’s project reminded me Giorgia Lupi and Stefanie Posavec’s Dear Data project where they hand-drew visualizations of little things in their life like how many times they checked the time during the day.

  • 05:50 - Giorgia has a wonderful TED talk where she talks about this project and how when she explored her reality and visualized it with these hand-drawn visualizations, she was able to transform the abstract and uncountable into something that can be seen and felt, and it helped her feel more connected to her life. By handcrafting visualizations of information, she tries to re-connect numbers to what they stand for: stories, people, ideas. What she called data humanism.

  • 07:40 - My inspired viz used the data visualization survey results from Elijah Meeks.

  • 08:15 - Goal: feel a connection to my fellow data vizzers, specifically other women in the field. Listen for my process!

  • 10:00 - It was a really tedious process arranging every single shape to put together each of the 142 women who took the survey. But it was also really cool because it allowed me to feel really connected to each one, like I found my heart and I could see the women next to me who are similar age, similar experience, does she have a STEM major, was she self-taught, is she interested in learning more design?

"The Women of Data Viz" by Alli Torban 

  • 10:40 - My final takeaway is that by creating a custom, free-form visualization of your data, you can create something that’s not only beautiful and engaging, but also something that helps you connect with your data - like in my women in data viz project or help you quantify something that feels overwhelming to you like in Sonja’s project. If we can visualize data in an unrestricted way, it can open us up to appreciating our imperfect and intricate realities in a beautiful and meaningful way. So try freeing yourself of chart types, and see if you can connect with your data through this visualization technique.

  • 11:45 - Finally, I asked Sonja what’s her advice to designers just starting out, and she said “Ask. Don’t be afraid to ask! The dataviz community is a warm one (in my experience) and you can reach out quite easy on Twitter (where most are active) to ask for advice.”

  • You can keep up with all her work on her website and follow her on Twitter!

Artboard 1How to read# years doingdata vizSTEMmajormostlyself-taughtwants to spend moretime on data vizwants to get better atdesigndataage25 & under26 - 3536 - 4546 - 5556 & upNumbersmedian # of years doing data viz457%age 26 - 3549%STEM major77%mostly self-taught76%want to spend more time visualizing data49%32%aspire to be better at designaspire to be better at data

Episode 27: [Mini] 3 Things I’ll Try To Do Better After Reading Tufte’s Book

Welcome to episode 27 of Data Viz Today. I loved reading Edward Tufte's “The Visual Display of Quantitative Information” for the first time as part of the Data Vis Book Club! In this episode, I talk about the 3 things that I'll try to do better in my data viz now that I've read Tufte's book. What about you?

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:30 - I just finished “The Visual Display of Quantitative Information” by Edward Tufte as part of the Data Vis Book Club, and I wanted to share the 3 things that I’m going to try to do better after reading this Tufte book.

  • 01:40 - #1 First thing I want to try to better is to refocus on showing the data. The proportions, the variation, the context. It’s easy to get caught up in the design and trying this or that color or technique, but throughout the book, Tufte always comes back to his main point - focus on showing the data clearly - show data variation, not design variation. Try to erase as much non-data ink as possible, that’s the ink you use to print things that are not essential to the data information. You want to strive toward erasing as much non-data ink as you can. It’s a really valuable perspective to think in terms of data ink and non-data ink, and assess what’s truly important to communicating your data and insights.

  • 02:40 - #2 The second thing is that Tufte came up with some interesting techniques in support of minimizing non-data ink, which I want to try:

 White grid lines through data.

White grid lines through data.

One technique is erasing as many gridlines as possible and if some gridlines are necessary, instead of using black lines in the background, you could use white lines that just go through the data so it gives you a way to visually break up segments without adding any non-data ink.

 Range frame.

Range frame.

Another technique he calls the range frame, which is where you draw your x or y-axis, but only in the range where you have data. Like if you have a scatter plot that has data only between the values of 10 and 15 on the x-axis, then only draw the x-axis line in the range between 10 to 15 not like 0 to 15 like you normally would. It might be a nice minimal way to show the axes, and the reader can quickly see the range of the data points.

The book has lots of other little techniques like that helped to get me thinking about creative ways to reduce non-data ink.

  • 03:55 - #3 Third thing I want to try to do better, is not rely on color so much

He has a great section about not turning your data viz into a puzzle. He says that a sure sign that you created a puzzle is if the graphic has to be interpreted through a verbal process, rather than a visual process. Like if your reader has to rely on reading a legend and repeating phrases to themselves to decode what you’re showing, then you probably created a puzzle.

Tufte notes that often color creates graphical puzzles because we attempt to give color an order and assign it meaning, which means someone needs to decode it, so it can very easily get complex. So instead, think about using shades of grey because it has a natural visual hierarchy so there’s less decoding that needs to happen.

I think I rely on color a lot and go straight to it to start using it to encode attributes of my data, but Tufte makes a great case to keep things simple and consider whether you’re making a puzzle out of your graphics, and oftentimes color can be the culprit.

  • 05:00 - My final takeaway is that reading Tufte’s book “The Visual Display of Quantitative Information” gave me a refreshed perspective on data viz and was a great reminder to keep things simple and show the data, erase as much non-data ink as possible, and can be creative in the ways that you do this by not just erasing but using different techniques, and finally be weary of creating a graphical puzzle especially with color. You want to show data proportions, variations and context so your reader can gain insights, and as Tufte says, graphical elegance is often found in simplicity of design and complexity of data.

  • 05:45 - I highly recommend reading his book, not necessarily as a data viz rulebook, but as a valuable perspective to consider.

  • 05:55 - Did you read the book? What was your impression of it? Let me know on Twitter or Instagram.

  • 06:05 - I put together a Resources page with my favorite books, blogs and tools!

Allison Torbanmini, tufte
Episode 26: How to Develop Your Design Eye & Transform Your Work - Featured Data Visualization by Jane Pong
 Jane Pong

Jane Pong


Welcome to episode 26 of Data Viz Today. Being able to see the difference between well-designed and poorly-designed data viz is half the battle! But when your work always looks amateurish to you, it can be really frustrating. In this episode, host Alli Torban identifies specific ways that you can close the gap between your good taste and your developing skills. Featured data visualization by Jane Pong perfectly illustrates how dense data can still be designed in a clean and engaging way, and I take notes from her to remake my viz from a past episode!

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is about how to cultivate your design eye. I think before you can design well, you need to be able to see the difference between good and bad design. It comes more naturally to some people than others, but that doesn’t mean it’s not learnable! This is important because by training your design eye, you can elevate your taste and know what it takes to create even more beautiful and functional data viz.

  • 01:45 - In this episode, we’ll talk about the viz that inspired this, how it was built, and my 3-step process that I’m going to call the “IRA method” that I plan to execute every time I come across a data viz that I love so that I never waste an opportunity to further cultivate my design eye.

  • 02:09 - Today’s featured data viz project is called “Rain Patterns” by Jane Pong. Jane is a data visualisation designer based in Hong Kong.

  • 02:20 - I came across Jane’s viz because she was interviewed by Jon Schwabish from the PolicyViz podcast.

  • 02:55 - Her viz was exactly what I tried to do in my inspired viz in episode 23 where I wanted to show daily rainfall in D.C. over the past decades, except Jane did it for a Chinese newspaper back in 2013 and her design was so beautiful.

  • 03:15 - Jane created this viz because Hong Kong was heading into the monsoon season and she was curious to see whether it occurred at the same time every year, and when the typhoons were happening. The daily rainfall and cyclone warning data is from the Hong Kong Observatory.

  • 03:40 - Design inspiration: She knew she wanted to create a bar graph showing daily rainfall, but wanted to invert the y-axis so the bars looked like they were falling down from rather than rising up out of the axis. She thought it’d be a really nice visual metaphor for rain falling, which was inspired by Simon Scarr’s viz on the Iraq War where he showed the number of fatalities in red as if it was blood dripping down.

  • 04:18 - Jane said the hardest part of creating this viz is that back in 2013 she was just getting into data viz and her coding skills were pretty minimal so she spent a lot of time looking up how to load the data and draw the data with code. Another wrinkle is that since she was creating this for a newspaper, it had to published with the right timing, so she had to update the graphic several times while she was waiting for the rain to come!

  • 04:50 - The final viz was created in Processing, exported as a PDF and touch up for publication in Adobe Illustrator.

  • 05:58 - After seeing Jane’s viz, I became super interested in figuring out WHAT made the design of her viz so beautiful and effective?

  • 06:18 - It’s easy to see good design and appreciate it and also feel frustrated that your own designs look so amatear in comparison to others, but that thought reminded me of a short video by Ira Glass that I had seen a while ago. In the video, he said “If you’re someone in a creative field, you probably started because you have good taste.”  And he went on to make the point that when you first start creating, there’s a gap between what you are capable of creating and what you think is good. And it’s frustrating… and this is where most people quit because it’s hard making amateurish work when you know it looks amateurish… but keep pushing! Ira says to keep creating because through practice you can start closing that gap.

  • 07:22 - If I can analyze good design in an intentional way, I can start training my design eye to see all of the little choices that produce really amazing work. Which also reminds me of Andy Kirk’s blog series called “The Little of Visualisation Design” where he highlights and comments on a small design choice that can make a big difference in data viz.

  • 08:23 - I put together three questions to ask yourself whenever you see a data viz that you really like, so you can quickly identify what’s making it good design so you’ll know how to apply it to your own work.

1. What is my first impression? What feelings or emotions is it bringing out in me?

2. What makes it easy to read? Things like visual hierarchy, font choices, color, and sizing. It’s general readability.

3. What’s making this viz stand out or unique? What specifically is making it so alluring to me?

  • 09:00 - So in honor of the sage advice from Ira Glass, I’ll use the acronym IRA to remember these questions: Impression, Readability, Allure.

  • 09:15 - Every time you find a viz that pulls you in, ask yourself IRA and write down your answers: Impression - What’s my first impression? Readability - What’s making this easy to read? Allure - What makes it unique and alluring?

  • 09:27 - And list out a few specific things that are contributing to your answers… like is it specifically the color palette that’s making it alluring? Is it the abundance of white space that’s making it easy to read?

  • 09:40 - Listen for how I put the IRA method to the test on my data viz from episode 23 using Jane’s viz as inspiration!

  • 12:55 - My final takeaway is that we should take the advice of Ira Glass - don’t be discouraged if your work doesn’t match up with your taste - be patient and keep practicing and you’ll close the gap. Specifically for data viz, take a well designed viz and turn it into actionable edits to your work by using this IRA method. Write down what exactly is giving you a good first impression of the viz, what exactly is making it so readable, and what’s giving it that special allure or uniqueness. By taking note of these little design decisions, we can cultivate our taste and design eye so that we can edit our own work in a more refined and elevated way, and keep closing that gap.

  • 13:40 - Jane’s advice to designers just starting out: “Always remember the audience you’re designing for, and what you want to achieve with your data visualisation. Experiment and iterate, and judge your designs based on the goals you want to achieve.”

  • 14:00 - Jane’s website and follow her on twitter!

  • 14:10 - Join the in-person data viz book club if you’re in the Northern Virginia area.



Episode 25: How to Design a More Inviting Data Viz - Featured Data Visualization by Sarah Bartlett
 Sarah Bartlett ( source )

Sarah Bartlett (source)


Welcome to episode 25 of Data Viz Today. How can you create a data viz that feels inviting to your reader? Host Alli Torban explores the specific design elements that can offer your reader an enjoyable experience. Featured data visualization by Sarah Bartlett perfectly demonstrates how investing in an inviting design can lead to a pleasant, informative, and memorable experience.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is about what makes a design inviting. What do I mean by inviting… the definition of inviting is “offering the promise of an enjoyable experience.” To me, an inviting design is one with a harmonious color palette, it’s easy to read and orient myself, and basically the whole thing doesn’t look intimidating, like it’s going to be a lot of mental work to decipher.

  • 01:35 - Today’s featured data viz project is called “Explore European Cities on a Budget” by Sarah Bartlett. Sarah’s a data visualisation consultant & Tableau Ambassador based in London.

  • 02:40 - Sarah said she almost didn’t submit a viz into the feeder competition this year because she had gotten super busy and her search for a dataset was turning out to be uninspiring. There was only 6 days left until the deadline so it seemed hopeless. Then she received the best advice from a former iron viz champ Tristan Guillevin: don’t get hung up on the data set. It’s never going to be perfect and exactly what you envisioned. Pick one, start visualizing it in tableau and a new idea and story will come to you.

  • 03:26 - So with this new perspective of finding something basic and building up from it, Sarah started searching with a tourism perspective and found a website called www.priceoftravel.com that breaks down the costs of traveling to different cities, like lodging, food, activities.

  • 03:50 - But she had a problem…She couldn’t get the data off of the website easily. Her friend Lorna Eden scraped the data from the website using Alteryx.

  • 04:28 - For design inspiration she used pinterest and the site CSS Drive to upload an image of a European city that she liked and it automatically generated a color palette for her to use which had soft blues and browns.

  • 05:40 - She added two things that I think really take it from a nice viz to a really inviting viz.

    1. She added a small map with her color palette using Mapbox and picture for each city. It’s super easy to create custom styled maps in mapbox and if you’ve been wanting to try it, check out my free Mapbox course.

    2. She used icons instead of labels for her bar chart.

      • Pros of icons: save a lot of real estate by replacing text with pictures, and these pictures give your readers the benefit of being able to easily scan and process the information. It’s inviting because people are drawn to real life objects that they’re familiar with and the data doesn’t seem so intimidating.

      • Cons of icons: you have to use icons that are really easily understandable so you don’t make it even harder to understand than text (test it with your audience). It can look cluttered if you don’t use the similar colors and style (like line thickness, curved or straight edges).

  • 08:25 - Sarah used the website NounProject to download royalty free icons

  • 09:45 - Then she asked some fellow tableau users to give her feedback. She said it’s always amazing to her how helpful getting feedback is because you just get so blind to easy mistakes because you’re staring at the viz for so long.

  • 10:33 - Sarah was able to pack in so much information but make it so inviting and fun to explore. I think the top 3 things that contributed to this was her harmonious color palette, the map and image that orients you to the city, and the icons that just give the charts a less intimidating feel.

  • 10:55 - Applying these three things to the viz that I did about chess in episode 11 because it just feels like kind of a cold viz to me, but it’s about something fun and interesting, so I think it could benefit from some design elements that make it softer and more inviting.

  • 11:40 - So first thing, color. I used CSS Drive to get the color palette out of an image of a forest that I chose because it made me think of those chess sets that are carved out of wood.

  • 12:10 - I added a map of the small town outside of Amsterdam where the tournament takes place to orient the reader to what this is. I used Mapbox’s site called Cartogram which lets you upload an image and it’ll automatically style your map features based on colors from your image. So my color palette was extended to my map.

  • 12:38 - Then I started looking for places that I could add icons to make it easier to read. I used a brown person icon and an image of the eventual winner Magnus Carlsen instead of diamonds in the viz.




  • 13:37 - My final takeaway is that inviting design is your way of offering the promise of an enjoyable experience to your reader. And to me, an inviting design is one that’s easy to read, orients you, and doesn’t feel intimidating and cluttered.

  • 13:55 - Try using a color palette inspired by nature or art, use a map and pictures to orient your reader, and use icons to reduce clutter and make your information easier to process.

  • 14:06 - Sarah’s advice to designers just starting out: “Get as much practice as you can. Practice your craft every day if possible. To avoid getting bored, try and visualise data on a subject you enjoy such as your favourite band, movie or hobbies.”

  • 14:40 - Follow Sarah on her website and on Twitter

  • 14:55 - Come and join the Northern Virginia in-person version of the Data Vis Book Club (started by Lisa Charlotte Rost) on August 22nd!