How to choose the right chart (part two)

How timely. Last week I wrote about choosing the right chart. Juice Analytics recently created an interactive Chart Chooser, based on Andrew Abela’s original Chart Chooser decision chart (via FlowingData). Both tools are excellent and offer a great start to choosing the right chart/graph format for data. The interactive chart offers little in terms of best practices (it wasn’t designed to do that) but helpfully separates out different chart types by the data that you have (quantity, comparison, distribution, etc.). And the best part of the interactive is that it provides you with downloadable templates for both Excel and Powerpoint. I’ll try this and might write about how well it works for me in a business setting.

I actually like Andrew’s original (static) chart a little better, as I find the flow diagram does a nicer job of providing context for the decision-making process.

Put both of these things together and you’re off to a good start.

[UPDATE]: Read Naomi Robbin’s (Forbes) excellent counterpoint to the chart-by-menu mentality.

Andrew Abela Choosing a Good Chart

Andrew Abela’s original chart chooser tool

The ethics of information

I don’t pretend to be an expert on any subject other than one: how to recognize a perfect pizza. That’s not false humility, it’s a candid admission. Most of what you’ll read in this blog are summaries of my learning curve in pixels–summaries built on the experience of those more patient, methodical and talented than I. Thank goodness for the interwebs.

To the wealth of information out there, I can add only a small amount of experience, most of which is gleaned from making mistakes; from not asking the right questions; from not sufficiently challenging, and thus not understanding, the premise of a project; and from occasional bouts of arrogance or foolishness. Okay, the confessional is closed. But the reason I make this point is because this is exactly where ethics and best practices come in. It’s your first line of defense against silly ideas foisted upon you by unknowing clients, editors, writers–even you.

A recent post by Alberto Cairo entitled “Infographics as Moral Acts” reminds us, yet again, that as much as we raise the bar in each and every way–via the visual arts, or through  technology, or by envisioning new ways to tell our stories through data–it doesn’t amount to much without some guiding principles. This is not a new idea, but I when I look at the proliferation of infographics I do wonder how top of mind this is for information designers (myself included). Some signs are encouraging–as some of you may remember, Visual.ly, a popular data viz sharing site, adopted a code of ethics for data visualization in February (other blogs, including Tableau, wrote about this as well, though the discussion generated little comment other than a reference to Fox News–below).

So, read this post, as well as a related article from the Harvard Nieman Watchdog Journalism Project (co-written by Mr. Cairo) which the article references, and try to make it part of your work in meaningful ways.

We’re listening to you, Alberto. But apparently, Fox News still is not.

Gas prices example from Media Matters

How to choose the right chart (corrected)

A friend recently asked me, “how do you choose the right chart?” I thought about it, and essentially sent her a list of the sites that I have bookmarked, along with a few comments. This is by no means an exhaustive list, and it’s meant more for a layperson, but here’s the list, nonetheless. If you have more suggestions, I’d love to hear them.

I’ll follow up with a future post illustrating a few of these, and summarizing best practices and my experiences (a post which my toddler recently published in draft form–word to the wise, never let your toddler near your blog ;-)

In the meantime…

Which chart should I use

Limited to basic charts but half the time, that’s all you need.

  • SAP Design Guild: A great reference that can get technical and, if you’re so inclined, introduces (gently) some basic statistical concepts.
  • CDC (pdf): Yes, this is from the CDC but for a layperson it provides a succinct reminder to keep things simple.
  • Graphs.net: This is by no means exhaustive, but it’s a nice primer on the types of basic graphs out there.
  • Stephen Few (Effective Chart Design – pdf): These guidelines are from Stephen Few, a man more practical than Tufte (in my opinion), yet just as hell-bent on clarity and focus. If you can read his books, do so. At a minimum, spend some time on his white papers and you’ll learn a lot.
  • A periodic table of data visualization: Less helpful if you’re looking for charts, and more helpful if you’re interested in mapping ideas or processes, this graphic mimics the structure of the periodic table, but for data visualization.
  • Interactive version of the periodic table of data visualization: If you like the periodic table, this page actually has links to each example cited in the periodic table. The most helpful part is that the links point to either images in Google or links to wikipedia articles that discuss each graphic type. If you’d like to learn more about different charts and their uses, this makes for a good, albeit long, starting point.
  • Creating graphics in Excel: There is also a very excellent blog about creating graphics in Excel. I hate Excel and love this blog. This is much more than a “there’s a chart for that” approach; lots of good information on best practices and case studies that go beyond Excel.

From Illustrator to information designer:

For more traditional graphic designers (not coders) seeking to make the move to data visualization and understanding both the mechanics and the theory behind visualizing information, a crash course in handling data in Adobe Illustrator is helpful. Lots of terrific designers never get the chance to interact with data in Illustrator, so that’s not unusual.

Free, open source data visualization tools for the non-designers that are good, and useful

Many Eyes: Many Eyes was developed by IBM labs. It’s a phenomenal tool for quickly visualizing a ton of information in a few seconds, without spending much time on having to learn how to format the data. Just copy/paste from Excel and you’re set. To start, first create an account. Then on the left under the “participate” heading, choose “create a visualization.” That takes you to the “upload data” screen, into which you can simply paste in your data. Then in that same screen go to step 4 (you can ignore the rest) and give your data a title (e.g., “test). Hit “create” to go to the next screen. Click the “visualize” button and then choose a format (bar chart, etc). What’s great about this is that each format has a “learn more” button, which explains in simple terms what each graphic type is best suited to do. At any rate, once you’ve chosen a format, you can see what the viz looks like. At that point, I just take a screenshot and exit, because I don’t wish to publish the data—I just need help with visualizing it. But you can click “publish” to do so.

Tableau: The “Tableau public” version is free, though you do have to publish what you use, I believe. There is definitely a learning curve to understanding how to format the data–different than Excel and not intuitive if you’re expecting an Excel experience. But very powerful once you get the hang of it.

The Guardian’s list on free data visualization tools: This article by the Guardian also mentions the above and a few other tools, most of which I’m sure you know about (Google maps, Google Fusion tables and Google charts) but also a few others that I haven’t tried.

On good data visualization practices:

There are three absolutely phenomenal articles by Enrico Bertini.

 

Here, there, everywhere: Scatter plots and heat maps in Illustrator

Many designers choose to export data from Excel into Adobe Illustrator’s charting tool. A few months ago, we found ourselves scratching our heads over a request that we received. The internal client had lots and lots of data on personal income and wanted to show this data as a scatter plot graphic, divided into quintiles. They had already created a basic version of this graphic in Excel, but needed the designers to redesign it to make it easier to understand and to add the visual polish and presentation for which design software is better suited.

Goals, approach and tools

We took a look and determined that our biggest task was simply learning how to create a scatterplot graphic that could handle the large volume of data the original Excel file contained–1,043 rows of data.

Our goal: Show intensity and data patterns across five categories (quintiles). Keep the data “live” in order to be able to quickly update the graphic with new data.

Our approach: Use Illustrator’s scatter plot tool to graph the data. Customize the graph to create a heat map in order to show intensity/concentration of data.

Our tools: Adobe Illustrator’s graph creation tool and Illustrator’s transparency settings to create a heat map

An explanation of the final product: a heat map produced using Illustrator’s scatter plot graph tool

Take a look at the graph below. I recreated something similar to that which my team designed (update: because we haven’t yet published the data, this graph shows widgets instead of the subject matter of the original graphic). This hypothetical graph shows costs of production (money) spread out across four categories (quartiles)–a bottom quartile, a second quartile, a third quartile and the top (fourth) quartile. The darker the color (the heat map effect) the greater the intensity of those data. In other words, where the color is darkest represents a large number of widgets that with that cost of production. Where it is lightest represents a smaller number of widgets with that cost of production.

Fig. 7: Bells and whistles: Showing intensity in a scatter plot graph in Adobe Illustrator

Showing intensity in a scatter plot graph in Adobe Illustrator

Needless to say, we learned a lot about Illustrator’s scatter plot capabilities in six hours.

What you need to know before you begin this tutorial

Before I begin, I’m assuming a basic level of understanding with Illustrator (we were using CS5, but I believe all CS levels should work for this example) and its graph creation tool. If you’re not, search for Illustrator graphs and you’ll find plenty of tutorials. Better yet, FlowingData has a good basic tutorial on Illustrator graphs here. And so does Adobe. If you’re a designer, you probably already know the basics.

Although I’m also assuming that you know what a scatter plot graph and what a heat map is, this tutorial will explain a bit about its uses and compare it to a line graph, albeit briefly.

To better explain all of this, let’s first start by building a more basic graph, a scatter plot graph.

Fig. 4: Formatted scatter plot graph in Adobe Illustrator

A scatter plot graph in Adobe Illustrator

As you can see from the example above, the graph contains four category rows (labeled “Category 1,” “Category 2,” etc. In the real world, these categories could be years, quartiles or however you wish to divide up your data. Each category shows a row of data points associated with it (Category 1 shows a gap in the values between 6 and 15, for example). Keep an eye on Category 4′s outlier, the number 20 in the top right. More on that later.

How to correctly import data into Illustrator’s data tool

Half the battle is learning how to enter or import this data into Illustrator. Essentially, think of your data as a series of columns that alternate. The first column has your “Y” axis values (your categories); the second column has your “X” axis values (the data associated with each category).

In my example, for Category 1 to appear first on my “Y” axis, I entered“1” in the first column (repeating “1″ as many times as I had data for that category). In the second column I entered all the data associated with Category 1. Repeat, and you’re all set.

Take a look at these first two columns in the data table to see how simple this is: [FIGURE 1].

Data table for a scatter plot graph: FIGURE 1

Fig. 1: Data table in Adobe Illustrator

Fig. 1: Data table in Adobe Illustrator

Customizing your graph by using Illustrator’s “Graph Type” feature

As a final step, once you are finished working in the data table, click the checkmark button in the top right corner to output the graph. Then right-click on the graph itself and select “Type” from the menu. From the resulting “Graph options” dropdown in the dialog box, select the “Value axis.” In that dialog box, make sure that “Override Calculated Values” is checked. This is how to format your values for those fields:

  • Minimum value: should always be set to zero
  • Maximum value: should match the number of your categories (in the basic example shown in Figure 1, I had four categories, so I entered “4″)
  • Divisions value: this is how the categories will be divided up. I always find this one intuitive, though difficult, to explain. For this tutorial make sure that the number you enter is one less than the total the number of categories that you have (I had 4 categories, so I entered a “3″).

Basic scatter plot graphic in Illustrator: FIGURE 2

Here’s the resulting graphic that Illustrator will produce at this point (I added the color manually). The blue row in the graphic represents column 2 in the data table. Remember:  the reason Illustrator knows to put those blue points under the row labeled “1″ is because you labeled them as 1s in column 1 of the data table. You can later change the name of that row from “1″ to “Category 1″ (as an example) in the graphic itself.  [FIGURE 2]

Fig. 2: Unformatted scatter plot graph in Adobe Illustrator

Fig. 2: Unformatted scatter plot graph in Adobe Illustrator

[Aside] How a scatter plot graph is different than a line graph: FIGURE 3

As an aside, if you’re not familiar with scatter plot graphs, here’s a quick explanation of how to interpret this one. Take a look at category 4 (it’s the green square in the top right of the graph shown in Figure 2, in the data table in Figure 1 it is the last column). Do you see how Category 4 has ten values, each numbered as 20?

But on the scatter plot graph, you only see the number 20 represented once (green square). Scatter plot graphs won’t show you data points when they overlap exactly–a line graph will, however. Here’s the same data in a line graph. Category 3 (green) now shows you each data point that is numbered as 20. [FIGURE 3]

Fig. 3: Comparison of same data: A line graph in Adobe Illustrator

Fig. 3: Comparison of the same data: A line graph in Adobe Illustrator

Customizing the scatter plot graph: FIGURE 4

Back to the scatter plot graph, you can customize the labels in the graphic itself once you’re finished with the data view. For example, you can change the category numbers from 1,2,3 and 4 to specific category values that reflect how the data is actually organized (e.g., by quartile, by year, etc.). More importantly, you can customize further with fonts, colors, stroke widths, etc., some of which Illustrator will retain if you return to data view and change the data. Which ones, you might ask? That’s a post for another day. [FIGURE 4]

Fig. 4: Formatted scatter plot graph in Adobe Illustrator

Fig. 4: Formatted scatter plot graph in Adobe Illustrator

So, in the real world, what can Illustrator do for you? FIGURE 5

You can create a graphic that looks like this (this is not real data, of course): [FIGURE 5]

Fig. 5: Finished: A scatter plot graph in Adobe Illustrator

Fig. 5: Finished: A scatter plot graph in Adobe Illustrator

How to set up 1,043 rows of data: FIGURE 6

Here’s a look at the data. You’ll notice that the setup is identical to the basics that I outlined earlier. The figure below shows you a snapshot of how each row is set up. [FIGURE 6]

Figure 6: Data view of hypothetical widgets

Figure 6: Data view of hypothetical widgets

Turning a scatter plot into a heat map: Using transparency to further customize Illustrator’s scatter plot graph to create a heat map: FIGURE 7

I promised you a heat map, and here it is. Remember that a heat map essentially shows areas of concentration (or lack thereof) in data–intensity.

To show intensity for those data points that overlap (like the repeated series of 20s that I mentioned in Figure 2), simply select *all* the points in a category with your direct selection tool. (If you’re familiar with the “Select Similar” feature in illustrator, use your direct selection tool to choose just one data point, then choose use the “select similar” feature to automatically select all of the points in that row.) Then apply transparency to them all at once. Because transparency has a cumulative effect when layered on top of something else that is transparent, you are essentially creating a heat map effect. [FIGURE 7]

Fig. 7: Bells and whistles: Showing intensity in a scatter plot graph in Adobe Illustrator

Fig. 7: Bells and whistles: Showing intensity in a scatter plot graph in Adobe Illustrator

A simple explanation of transparency: FIGURE 8

Fig. 8: Bells and whistles: How to use transparency to show intensity in Adobe Illustrator

Fig. 8: Bells and whistles: How to use transparency to show intensity in Adobe Illustrator

Well, that’s it. Please let me know if I’ve left anything out. Remember, this tutorial is meant to show how to customize Illustrator’s scatter plot graph tool. Just because you can, doesn’t mean you should! Once we publish the actual graphic, I’ll post that as well.

It’s time to watch (or explain) soccer again

Football fans, it’s Euro Cup 2012 time. If you live in the U.S., like me, prepare to justify your existence to people who prefer to watch other sports. If you live in other countries, I celebrate your freedom to share your joy with anyone within spitting distance. Lucky you.

But surely what unites all of us is the onslaught of football-related infographics, complete with awesome Photoshop cutouts of players, images of the coiffed Ronaldo, exploding piecharts and Pinterest boards too numerous to mention. Thus far, I haven’t found any graphics as funny as the Onion’s World Cup 2010 interactive, but it’s early, so I’m still hopeful–there’s a lot of Photoshop licenses out there.

The Onion's World Cup 2010 interactive

The Onion's World Cup 2010 interactive

If you’re a football fan, I’m sure you’ve already seen this interactive calendar by Marca.com as well as this fixture schedule, both featured on the CultFootball site.

interactive calendar cult football by Marca.com

UEFA's Euro Cup 2012 fixtures map

Aside from logistics, there’s the ongoing boycott petition in Germany, sponsored by a GLBT group (and Germany’s first openly gay football player) in protest of the Ukraine president’s refusal to denounce his country’s laws which criminalize homosexuality. The petition asks Angela Merkel to follow France and the UK’s example and boycott the Euro Cup (as foreign dignitaries). We’ll see, she says. This isn’t an infographic, per se, but it’s a visual statement nonetheless.

Euro 2012 anti homophobia

And then there is this, kindly brought to us by the gambling community (reminding us that betting is a huge part of football):

Euro-2012-Infographic

One of my favorite football-related infographics is always the slew of stadium infographics and maps, each one looking suspiciously like high-tech UFOs (why did I say that? Is there any other kind of UFO?). This year is no different. When the little green men/women/whosamawhatsit come to visit, they’ll feel right at home in one of these:

UFO stadiums Euro Cup 2012

And then there’s the Spanish team buzz on social media. Well, it is what it is. Go Spain!!!

Spain and the Euro Cup social media

 

Should graphics be easy to understand?

Ah, the glamorous life of the data visualization designer… to draw or not to draw? To obfuscate or not to obfuscate? I’ve been doing some reading lately about a debate that is making its way amongst the data viz community. At what point does too much illustration, creativity or innovation get in the way of the primary purpose of data visualization? And how well is the design community being transparent about art based on data versus data visualization? Or, to put it more simply, should data visualization be easy to understand and what happens when it’s not?

Allow me, first, to offer up my own definition, artfully cadged from people much smarter than I and enhanced by my own experience in the field, such as it is. So, data visualization is what, exactly?

Information served up visually in order to inform and improve/enhance our understanding of the data.

Clumsy, but I’m hitting the main points: inform and understanding. If pressed, I would add the word “easily.” Actually, it’s the word “easily” that prompted me to write this.

If you can’t understand a data visualization piece, then it’s pretty useless, isn’t it? Maybe it’s beautiful, but if you walk away more confused than you began, it’s useless. And if you walk away as confused, or a bit less confused, it’s still useless.

How far can we take this concept? Here is a quick survey of what folks have been saying lately. Props to infosthetics for providing a good starting point for these discussions. And here they are:

Stephen Few’s blog post on the two types of data viz is a good start. According to Few (Tufte’s alter-ego), there are two approaches to presenting data graphically—data visualization and data art. As he puts it, “rarely do the twain meet.” Therein lies the problem. They do meet. All the time. Though Few makes a good point—failing to distinguish between them creates confusion and harm, I would argue that the two are not mutually exclusive.

Few defines data visualizations as products created to inform, and “data art” as visualizations of data created to entertain—“art based on data”—something which can be judged accordingly.

My response? Would that the public were quite as discerning as he. The train has left the station and what we have before us is—at worst—a proliferation of eager designers too quick on the draw to consider the very important questions that need to be asked about the data that are being depicted. At best, a cadre of informed (and willing to learn) designers who humbly allow the information, the audience and the goals of the visualization to drive the design—who are loathe to add one extra pixel that doesn’t belong, and willing to take away any element that obscures a better understanding of the data. I’d like to think that I fall into the latter category but I fall somewhere in the middle, as do most designers.

Rather than drawing a bright line between these two approaches and dogmatically refusing to accept a middle ground, I suggest we embrace a blend of these when they are produced well—when they inform and present a clearer understanding of the data and are at the same time aesthetically pleasing. As a designer who chooses to serve both masters—art and data, I find joy in being able to translate a jumble of Excel rows and columns into a plain bar chart—sometimes the beauty lies in the hard work of sifting through the data and simplifying complexity. And sometimes the joy comes from experimenting with different formats and adding visual accents to enhance the data—provided, of course, that the user’s ability to understand the data is not impeded, but enhanced.

Nevertheless, I agree with Few’s depiction of the pitfalls of “data art” being misperceived as data visualization, and I’ll add one myself. In addition to spreading poor practice instead of best practice, it creates unrealistic expectations about what is acceptable in a data visualization, particularly for those of us who are working in the industry in a supportive capacity to researchers and writers with an uneven understanding of best practices (how many of us have been asked to create 3D graphics or exploding pie-charts on a whim?).

And a rising tide floats all boats. In this case, I’ll agree with Few’s point that the proliferation of “data art” and other fancy-schmanzy graphics that pass for data visualization imply that data viz is a closely-guarded secret known only to denizens of the data underworld (paraphrasing liberally from Mr. Few, here). But I take issue with his assertion that this prevents the “democratization of data”—implying that the public is somehow being dissuaded from engaging and creating data. For better or for worse, they aren’t. Just google “infographics.”

As an interesting aside, note that Eagereyes’ Robert Kosara wrote a primer on the two types of data visualization that Few discusses, waaaay back in 2007. Like Few, Kosara was also bothered by the blurred line between data and art. What Few calls “data art” Kosara called “artistic visualization.” Nonetheless, they each underscore the same points—keep data and art separate in order to be as transparent and clear about the data as possible. I agree with the goal.

As Kosara puts it, “looking at one type of visualization expecting the other will lead to disappointment and misunderstandings.”

Kosara, uses what is, in my opinion, one of the best data viz sites out there (infosthetics) as an example of sites that don’t make those distinctions, thus creating confusion. Granted, this was back in 2007. I wonder what he’d say now? Nonetheless, I disagree. Let’s not confuse lack of best practice (for example, normalizing your data to prove a point, and not being transparent about it) with the so-called sin of creating a piece that is visually striking. A designer can produce a graph with no artistic aspirations whatsoever that nonetheless obscures the data. And a designer can produce a terrific visual that observes best practices (to inform) and serves up the data artistically and well.

Adam Crymble has a different moniker for Few’s “data art” and Kosara’s “artistic visualizations.” He calls these graphics “shock and awe.” I love that term. Of all the discussions that I have read, Adam’s make the most sense to me. He doesn’t touch on all data viz that is artistic, but rather focuses on the extreme—and in this I strongly agree with the points he makes.

Adam Crymble: “shock and awe” graphics

We’ve all seen these very beautiful, complex visualizations that belong inside of a picture frame or a screensaver. Or, for a few seconds, they give us pause and food for thought.

a complex visualization of World Economic Forum data

I’ve seen them, written about them and admire them for what they are—unique explorations of the complexity of data. An artistic or visual expression of the complexity of the information we spew out and take in. But they don’t inform in the traditional sense of the definitions of data viz. They may underscore a pattern, convey a sense of weight through sheer numbers or complexity (as the example above does), but that’s about it. They’re pretty much impossible to understand on a granular level without some work.

Adam’s assertion that these complex visualizations have no place in the academic world is beyond my ken. For the record, the example above is mine, not his (see his post for his own, more humorous example). But if he is correct that peer reviewers are afraid to betray their lack of understanding of these graphics, and thus—through tacit acceptance—are endorsing their validity, well then that should concern all of us.

The most interesting point to be gleaned from Adam’s perspective, I think, is the bullying nature of shoving a terabyte of data in front of someone’s face and saying “Aren’t I clever? Don’t you get it?” I don’t. Point well-taken, Adam.

Mark Ravina writes an interesting rebuttal to Adam’s criticism of “shock and awe” graphics. He compares these artistic and complex visualizations to early feminist scholarship that provoked anger when it challenged the systemic sexism of the ivory tower. I’m a huge fan of confrontation and anger-provoking methods to push movements forward. In the early 90s, ACT-UP did the same thing for GLBT rights, if you’ll recall. Without ACT-UP, Queer Nation and Lesbian Avengers, there would be no fancy Human Rights Campaign fundraising dinners today. I get it.

But Ravina’s assertion that these complex visualizations of data somehow push the field forward is a bit much for me. He calls them “intellectual challenges.” I’m not so sure about that. How many of us who are willing to spend more than a few seconds trying to piece together a gazillion threads and data points in a fancy graphic. I think we consider it more of a waste of time to do anything other than admire the concept, the novelty of the presentation and then move on. Intellect doesn’t play a big role here (the creator, on the other hand, gets some bragging rights for creativity). Does it stick? Does it move the field forward? Um, maybe, sometimes?

Ravina spends a fair amount of time discussing how humanities researchers (he knows them better than I, certainly), insist on tables when they ask for data. I didn’t really read that into Adam’s criticism of these graphics—he was merely pointing out that data viz designers were making information too complex—he never claimed that the solution was to create charts. Then Ravina cites the misuse of pie charts to make the point that just because something is familiar, it can be misused. Is he implying that unfamiliar things can’t? As he puts it, “is schlock worse than shock?” Aside from the clever turn of phrase, it’s a bit of a moot point. Nothing that I have read criticizes innovation—merely obfuscation.

Mark Ravina: “Is schlock worse than shock?”

Ravina makes good points. He surveyed (presumably informally) graphs produced in history journals and notes that the bulk of them rely on formats developed (according to him) 200 years ago—pie charts, line charts and bar graphs. And he mentions how slow the field (I’m unclear if he means academics or history journals in particular) has been to adopt and thus understand formats that even today’s eighth graders are learning (box plots, for example). That’s a valid argument, certainly, but it has little to do with the complex visualizations that Adam was addressing or, for that matter, that Kosara and Few discuss. (To be fair, Ravina’s post was mostly in response to Adam’s).

However, he conflates different types of complexity, predictably citing Tufte and Menard (some of you know how I feel about that) as well as Rosling. Perhaps it’s a matter of taste, but I feel that Rosling bends over backwards to make his visualizations inspiring and accessible (not necessarily complex and beautiful), whereas the Menard graphic, while certainly elegant and ground-breaking, does not (of course not, and how could it, given when it was produced).

Lastly, one of the most important concerns that Adam raised was around obscuring data. By introducing unnecessary complexity into a visualization or graphic, data visualization designers can make academic and peer review verification and transparency needlessly difficult. Ravina counters this by saying that liars will lie. I don’t think that’s the point. They will lie, but transparency is as much about spotting errors or raising valid concerns as it is about unmasking willful deceit. Hats off to Ravina for taking the time to provide some very thoughtful counterpoints to the discussion.

Excelcharts is a pretty good resource for charting and data viz in general, despite the Excelcharts.com name (*smiling*). Jorge Camoes nicely (and literally) draws the elusive line between art/entertainment and data/information.

Excelcharts.com Data Visualization Continuum

More importantly, he puts a restraining hand on eager designers, quite reasonably underscoring Few’s point to make sure that, as designers, we emphasize that charts and graphics are readable and easy to understand, not memorable or beautiful. Of course, I’ll see your readability and raise you ten, Jorge. Let’s make the data understandable and, if we can, beautiful as well.

Lastly, there is this. It is a tome. You could spend hours here. It’s an open-review paper, part of which is around data viz, part of which I have skimmed. It deserves careful reading, and I’m eager to do so and write a follow-up post.

Well, if you’ve hung in there with me, I hope you have learned something. I know I have.