March dataviz madness: table vs graph

March madness is here—this three-week period when college basketball fever sweeps the States on the path to crowning the NCAA national champion. We’re pulled into the drama and tension of a single elimination tournament (who will emerge as the Cinderella team to upset a No. 1 seed?) and the stakes are high for teams: one sub-par performance and you’re out.  

When it comes to communicating with data, the stakes can also be high. Maybe not quite as ruthless as a single elimination tournament (one ineffective graph usually doesn’t mean our season is over) but a subpar visual might mean a missed opportunity for our audience to make a data-driven decision.

In data visualization, well-designed visuals are buzzer beating 3-pointers: they capture our attention because they get the main point across quickly and effectively. In today’s post, we’ll look at a dataviz match-up: will it be the table or the graph for communicating an underlying message?

Imagine you’ve encountered the following table: either in a live setting (someone has shown this on a PowerPoint slide) or own your own (said PowerPoint slide has been emailed to you).


What’s your initial reaction to this much data? If you’re like me, you’d probably groan and move on, totally disregarding all the hard work that was done behind the scenes to produce this table. Ouch.

When deciding whether to use a table or a graph, consider what the audience needs to do with the data: Do they need a certain level of detail? Are there different units of measure that need to be relayed together? Will they need to refer to a specific line of interest or compare things one by one? If yes, then a table may meet those needs. However, if there’s an overarching message or story in the data, think about making it visual for your audience.

Back to our match-up—imagine that the underlying story is that in recent years, packaging costs have increased at a higher rate and are projected to exceed budget at the end of the fiscal year. Refer back to the tabular data—how long does it take you to find the data that supports this?

Contrast that time-consuming process with the visual below, where I’ve visualized the relevant pieces and added explanatory text and focus through sparing color to make the data more accessible:

after visual.png

So what is the appropriate use case for a table? When your audience needs detail on specific values or when you have multiple units of measure to report simultaneously. In my previous roles, we used tables frequently in monthly status meetings when the main goal was for participants to give updates on their lines of business and participants wanted to be able to go row by row (or column by column) and refer to specific lines of data. Over time we realized many of these tables weren’t being used and we’d push them to the appendix—they remained there for reference but weren’t competing for attention with the main takeaways.

While we won’t know who wins it all in March Madness until the national championship on April 8, in this match-up we can choose a clear winner: the graph!

In fact, the graph will typically win when there’s an overarching message in the data. A well-designed graph simply gets that information across more quickly than a well-designed table. Don’t make your audience do more work than necessary to understand the data!

For more examples of how to consider if a table is more effective than a graph, check out our previous posts:

Elizabeth Ricks is a Data Visualization Designer on the Storytelling with Data team. She has a passion for helping her audience understand the ’so-what?’ as concisely as possible. Connect with Elizabeth on LinkedIn or Twitter.

recommended reading: Info We Trust

Info We Trust Cover

Info We Trust: How to Inspire the World with Data is a beautiful book. It feels nice to hold. The colors are vibrant. The language is poetic. The content is inspiring.

If you work with or have an interest in data, you should own this book.

I read it from cover to cover in a two day sprint a few weeks ago in preparation for a conversation with author—and friend of mine—RJ Andrews. It’s the first book I’ve read so thoroughly in quite some time: pouring over not only the main content, but also the preface and end matter. Nearly every inch of this book is filled with information: margins are full of quotes from wide-ranging sources (RJ read hundreds of books over the course of development) and other relevant tidbits. The text and margins are interspersed with hand-drawn images (even the graphs are drawn by hand!) that help reinforce and illustrate concepts. Here’s an example 2-page spread:


The chapters are relatively short in length but dense in ideas and concepts, which provides good balance. The book is divided into six main sections: (1) Origin, (2) Metaphorical, (3) Mathemagical, (4) Sensational, (5) Informational, and (6) Onward. Also don’t miss the impressive and cleverly formatted bibliography and RJ’s essay on how the book came to be.

While I enjoyed it all, I especially appreciated the Mathemagical chapters: Create to Explore, Explore to Create, and Uncertain Honesty. I’m commonly asked questions about exploratory data analysis, and together these chapters pose a number of thought-provoking questions that can help direct those working with data through this process. I also really appreciated the Sensational chapters, which explore a number of other areas (e.g. Museum design), imploring the reader to draw their own parallels to data storytelling. Chapter 16 “Inspire Trust” was another standout section for me, with some great insightful discussion on people’s belief systems and the resulting difficulty of changing minds.

Info We Trust is definitely not a how-to book, and yet it is interlaced with practical advice. To give you a sense of language and style, here is one excerpt I highlighted, from Chapter 3, Embodied Encoding (pages 43-44):

There is a candy shop full of ways we get to communicate meaning visually. For example, the concept of importance is naturally associated with size. Big things are important. Why might this be? We start off small. When you are a child, big people like your parents are important. Bigger people, the ones who were already grown up, are much more powerful. Sometimes big adults are even scary. Even longer ago, big animals, you must remember, used to eat us.

Big things, whether parent or predator or palm tree, are also important because, to our eye, big things are closer. Ultimately, big things occupy a larger portion of our visual fields. There, big things vie for more of our attention. Important big things stretch, conceptually, into our language (e.g., “I wish you would stop focusing on small matters and see the giant issue we have”). Embodied metaphors transcend language because all people have similar embodied experiences. Big things are important in Zulu, Hawaiian, Turkish, Malay, and Russian. When we make pictures of important things, we do not have to abstract all the way to language metaphors. Just draw important things bigger on the page.

At one point, RJ discusses sparking curiosity in your audience. He says—and I’m paraphrasing—that good stories leave space for the audience to make connections. The book itself does this beautifully—not prescribing “do this” or “don’t do that,” but rather making observations and leaving the reader space to make connections and extrapolate to their own work.

I found myself experiencing both excitement and sadness as I neared the end: excitement, as I could tell it was building in a grand crescendo, sadness that it would soon be over. That sadness abated quickly, however, when I got the chance soon after ending my own experience with the book to talk with RJ about it. You can listen to our conversation:

There are a lot of fun and inspiring surprises throughout Info We Trust that I won’t spoil for you. Let me just end by saying that I highly recommend this book and I hope you enjoy it as much as I did.

Thank you, RJ, for creating Info We Trust and for sharing it with us all!

InfoWeTrust RJ in Office.jpg

#SWDchallenge: visualize THIS data!

UPDATE: It seems we may have overcomplicated things this time around. Let’s simplify!

You can download the file with country-to-country donations here. Create a visual to ANSWER ONE QUESTION: WHO DONATES? (Related subquestions you may also answer: How are donations distributed across countries? Who donates to whom? Are there any patterns, for example some group of countries tends to donate only to some specific group of other countries?)

SHARE: Tweet your graph(s) or post publicly and email the link to

NEW EXTENDED DEADLINE: Friday, March 15th (midnight PST).

You’re of course welcome to do more (original full instructions follow), but our hopes are that simplifying will boost participation and we’ll get enough content to push some important data viz research forward!


There is no single “right” way to graph data. Any data can be visualized multiple ways, and variant views of the data allow us to see different things. This is one of the reasons I believe data visualization is so much fun—it sits at an intersection between science and art. There is science to it: there are best practices and guidelines that make sense to follow more often than not. But there is also an artistic component, which is really interesting. This means two different people approaching the same data visualization challenge may do so in two totally different ways. Extrapolating that idea… many different people approaching the same data visualization challenge may do so in many totally different ways.

Or will they? Let’s see.

This is a brief prelude to the March #SWDchallenge: visualize a predefined dataset to answer specific questions. Not only will it be interesting to see the differences and similarities of the various views created, but the hope is that the output will help with some practical data visualization research as well.

This month’s challenge is in partnership with Enrico Bertini. Enrico is Associate Professor at NYU, where he studies, researches, and teaches data visualization. In his own words: Some of my research aims at better understanding visualization practice from an empirical standpoint. What prevents people from creating effective visualizations? What elements of a visualization make it hard for people to read or interpret the information? How do we go about researching these questions? This little experiment is a first step towards answering these questions. We want to see how many different ways people find to answer the same question with the same dataset. Also, we want to figure out how to evaluate them. What makes a visualization better than another? How can we measure it?

While a number of previous challenges have encouraged you to try something new, the aim this time will be effectiveness. The goal is to see whether we can judge the quality of a visualization by how easy it is to answer the questions we ask you to answer with your visualization. We recognize that “easy” can be defined in many ways, so for purposes here it will be defined primarily by two aspects: 1) how hard is it to interpret or make sense of? and 2) how accurately can you extract information out of it?

Ready to take part? We hope so. Not only is this a great low-risk opportunity to practice effectively visualizing data, but it will also help push some important research forward. Following are all of the specifics. We look forward to seeing what you create!

the challenge

Create visuals from provided data to effectively answer specific questions.

GET THE DATA. We’ll be using AidData for this challenge. Follow these easy steps:

  • Hit “Download” from the AidData main page.

  • Unzip the file.

  • The file you’ll want is called: AidDataCoreThin_ResearchRelease_Level1_v3.1.csv.


In this dataset, each row represents a financial transaction between two countries. Attributes include: Year, Donor, Recipient, Commitment Amount, and Coalesced Purpose Name. There is a README file in the folder you downloaded with a glossary explaining the meaning of each variable. Note that the full dataset also includes international organizations other than countries.

This is a large dataset (over a million rows): if you’d prefer to work with something smaller, Enrico created a version that only includes donations between countries that you can download directly here.

ANSWER THE QUESTIONS. Create a graph or set of graphs to effectively answer the following questions:

  1. WHO DONATES? How are donations distributed across countries? Who donates to whom? Are there any patterns, for example some group of countries tends to donate only to some specific group of other countries? Or maybe some countries tend to receive only from a specific set of countries?

  2. HOW MUCH DO THEY DONATE? How much do countries donate and receive? Who donates the most/least? Are there countries that donate and also receive? How does the amount donated/received by country change over time?

  3. WHY DO THEY DONATE? What purposes do the donations serve? Do countries tend to send (or receive) donations for specific reasons? For instance, is it possible that some countries tend to receive/send certain type of donations whereas other receive/send different types?

Build your solution with any tool you like. This could be a single graph, multiple graphs organized in your preferred layout or even an interactive dashboard if you prefer. Remember that your primary goal is effectiveness. For research purposes, submissions will be assessed in terms of how easy it is to answer the outlined questions (both in terms of difficulty of interpretation and ease and accuracy of extracting information). While it won’t be possible to provide individual feedback, we will plan to share any findings once the analysis is complete.

SHARE ON TWITTER using the hashtag #SWDchallenge. Unlike previous challenges, you do not need to email us—this month’s challenge will be conducted entirely online. This is due to constraints on collecting data from human subjects: we need to have your solution submitted in the public domain. If you aren’t on Twitter or would like to say more about your solution, share in any public forum (LinkedIn, blog post, etc) and send an email to with the link so we know where to find it (no need to email if you post to Twitter).

DEADLINE: Sunday, March 10th by midnight PST.

We look forward to seeing what you come up with! Stay tuned for the recap post in the second half of the month. Check out the #SWDchallenge page for past challenge details and recaps.

#SWDchallenge: visualize variability

Is an average always the best way to summarize data? No! It can be useful to look at the underlying distribution of data and sometimes makes sense to show the variation. This month’s challenge is to visualize the variability in data. There are numerous ways to do so: the recap post this time around should be a good collection of approaches!

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