• Government Data

    How to Make Government Data Sites Better

    Accessing government data from the source is frustrating. If you've done it, or at least tried to, you know the pain that is oddly formatted…
  • Naked Statistics

    May 8, 2014  |  Statistics

    Naked Statistics by Charles Wheelan promises a fun, non-boring introduction to statistics that doesn't leave you drifting off into space, thinking about anything that is not statistics. From the book description:

    For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.

    Naked StatisticsThe first statistics course I took—not counting the dreadful high school stat class taught by the water polo coach—actually drew me in from the start. Plus, I needed to finish my dissertation, so I didn't pick it up when it came out last year.

    I saw it in the library the other day though, so I checked it out. If anything, I could use a few more anecdotes to better describe statistics to people before they tell me how much they hated it.

    Naked Statistics is pretty much what the description says. It's like your stat introduction course with much less math, which is good for those interested in poking at data but well, slept through Stat 101 and have an irrational fear of numbers. You get important concepts and plenty of reasons why they're worth knowing. Most importantly, it gives you a statistical way to think about data, flaws and all. Wheelan also has a fun writing style that makes this an entertaining read.

    For those who are familiar with inference, correlation, and regression, the book will be too basic. It's not enough just for the anecdotes. However, for anyone with less than a bachelor's degree (or equivalent) in statistics who wants to know more about analyzing data, this book should be right up your alley.

    Keep in mind though that this only gets you part way to understanding your data. Naked Statistics is beginning concepts. Putting statistics into practice is the next step.

    Personally, I skimmed through a good portion of the book, as I'm familiar with the material. I did however read a chapter out loud while taking care of my son. He might not be able to crawl yet, but I'm hoping to ooze some knowledge in through osmosis.

  • Getting started with visualization after getting started with visualization

    July 12, 2013  |  Tutorials

    Starting after started

    It's easy these days to get started with visualization. There are a lot of resources — books, tutorials, blogs, and classes — to help you learn, and the many new and old software applications let you work with data right away, point and click.

    You don't have to stop here though. A lot of people do stop at this point. They read the Tufte books (and by read, I mean casually flip through the pages and memorize the bold text), and stick them on a shelf or stack them on a desk like visualization diplomas. Maybe you're one of these people. I was.

    But we want to improve, right? I'll assume the answer is yes from here on out.
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  • Flexible data

    April 17, 2013  |  Statistics

    Data is an abstraction of something that happened in the real world. How people move. How they spend money. How a computer works. The tendency is to approach data and by default, visualization, as rigid facts stripped of joy, humor, conflict, and sadness — because that makes analysis easier. Visualization is easier when you can strip the data down to unwavering fact and then reduce the process to a set of unwavering rules.

    The world is complex though. There are exceptions, limitations, and interactions that aren't expressed explicitly through data. So we make inferences with uncertainty attached. We make an educated guess and then compare to the actual thing or stuff that was measured to see if the data and our findings make sense.

    Data isn't rigid so neither is visualization.

    Are there rules? There are, just like there are in statistics. And you should learn them.

    However, in statistics, you eventually learn that there's more to analysis than hypothesis tests and normal distributions, and in visualization you eventually learn that there's more to the process than efficient graphical perception and avoidance of all things round. Design matters, no doubt, but your understanding of the data matters much more.

  • 01-start-finish

    A Survival Guide to Starting and Finishing a PhD

    Tips on making it through, what I would tell my previous self going in, and advice on taking advantage of the unique opportunity that is graduate school.
  • Show me something good

    September 24, 2012  |  Design

    Visualization is a great way to explain and describe data to people who don't know data. Good visualization lets the data speak, as they say. But this doesn't mean you shove your data into a program or stick it into a presentation template and expect others to care. You still have to analyze and explore the data yourself, find what's interesting, and you present that.

    "But how do I make this graphic look cool?"

    Tell people something more about the data that isn't just, "Here's the data."

    You could use an obscure visualization method in place of your standard one, but what's the point if you just say the same thing? You might catch an eye or two because of the novelty, but those eyes will bolt just as quickly if there isn't any substance.

    So instead of showing the same non-message in different ways, you iterate. You cut and explore the data in different ways, and you make a lot of graphics that never see the light of day. Many will be ugly, and most of them will be uninteresting, but you might also find something worthwhile. Let that something guide you.

  • Resources for Getting Started with R

    June 4, 2012  |  Software

    R, the open source statistical software environment, is powerful but can be a challenge to approach for beginners. For me, the best way to learn R, especially on the visualization side of things, is to dive right in. Grab some data and make some charts, or better yet, find a graph you like and try to replicate it.

    R core functionality and the many available packages let you do a lot without having to know what's going on underneath. I use this approach in Visualize This and the tutorials around here. I like the satisfaction of immediate results. Then I learn the nitty gritty later.

    That said, it doesn't hurt to familiarize yourself with the environment. Also, visualization is a small part of what you can do with R, so it can help to know what else you can do analysis-wise.
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  • On low-quality infographics

    December 8, 2011  |  Design

    This has been sitting in my drafts folder for a few months. Figured I'd just hit publish and throw it out there.

    Obvious statement: there are infographics that are horribly made. Some are way too big for the information conveyed and others are useless because the creator had no idea what he was doing. Some infographics are both. Here's the thing though. There's plenty of suck of everything online, and yet somehow we manage to find the good resources, applications, and sources of endless entertainment.

    A couple of years ago, infographics spiked and even what seems like subpar work now, passed as amusing at the least. It's like the time on the Web when it was pure awesome to have a site decked out with animated GIFs, blinking backgrounds, and delightful MIDIs that were a treat for the ears. Sites like this still exist — some just as an archive of the past and others by someone learning HTML with a book they checked out from the library — but you'd never mistake one of those sites as an example of great Web or interaction design.
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  • The Don’ts of Infographic Design

    October 19, 2011  |  Design


    Written by Amy Balliett of Killer Infographics, the post in question is basically tips for how to create linkbait that doesn't work. Or at least I hope it doesn't.
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  • 5 misconceptions about visualization

    September 23, 2011  |  Design

    Last month, I had the pleasure of spending a week at the Census Bureau as a "visiting scholar." They're looking to boost their visualization efforts across all departments, and I put in my two cents on how to go about doing it. For being a place where there is so much data, the visual side of things is still in the early stages, generally speaking.

    During all the meetings, there were recurring themes about what visualization is and what it is used for. Some people really got it, but others were new to the subject, and we ran into a few misconceptions that I think are worth repeating.

    Here we go, in no particular order.
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  • Why learning code for data is worthwhile

    July 12, 2011  |  Design

    There are lots of tools that have come out in the past couple of years that make data easier to handle, analyze, and visualize. Maybe you've used them. I use them all the time. However, no matter what software you use, there is always going to be a limitation in what you can do with it.

    Have you ever been using an application (not just for data) and wished it could do something else? If you want a new feature, you have to wait for someone else to develop it, but if you program, you could implement your own features.

    With a little bit of coding know-how, you gain more flexibility — and a little goes a long way.
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  • Learn the rules of data graphics so you can bend them

    November 15, 2010  |  Design

    If you take away anything from The Visual Display of Quantitative Information, make it the epilogue. This is the most important part:

    Design is choice. The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper. Most principles of design should be greeted with some skepticism, for word authority can dominate our vision, and we may come to see only through the lenses of word authority rather than with our own eyes.

    When we first start out with data graphics, it is easy to read a list of rules about ratios, flourishes, and sizes, and then trick ourselves into believing that is all there is to it. But like cooking, writing, programming, painting, speaking, designing, sporting and numerous other things, you learn the basics first. The principles. And then you figure out what rules can bend and how far.

  • Graph Design Rule #2: Explain your encodings

    August 26, 2010  |  Design

    Rule #2 - Explain your encodings

    This is part two in a seven-part series on basic rules for graph design. Rule #1 was to check your data. Today we cover rule #2: explain your encodings.

    The design of every graph follows a familiar flow. You get the data, you encode the data with circles, bars, or colors, and then you let others read it. The readers have to decode your encodings at this point. What do those circles, bars, or colors represent?

    William S. Cleveland and Robert McGill have written about encodings in detail. Some encodings work better than others. But it won't matter what you choose if readers don't know what the encodings represent. If they can't decode, the time you spent designing your graphic goes to waste.
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  • Graph Design Rule #1: Check the data

    August 17, 2010  |  Design

    Check the data

    Now that we've covered the 7 basic rules to graph design, it's time to go deeper, starting with the first: check the data.

    I have to admit. Data checking is definitely my least favorite part of graph-making. I mean, when someone, a group, or a service provides you with a bunch of data, it should be up to them to make sure all of their data is legit, goshdarnit. But this is what good graph-makers do. After all, reliable builders don't use shoddy cement for a house's foundation. You don't use shoddy data to build your data graphic.

    Data-checking and verification is one of the most important—if not the most important—part of graph design.
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  • 7 Basic Rules for Making Charts and Graphs

    July 22, 2010  |  Design

    Basic rules for making graphs and charts

    Charts and graphs have found their way into news, presentations, and comics, with users from art to design to statistics. The design principles for these data graphics will vary depending on what you're using it for. Making something for a presentation? You'll want to keep it extremely simple and avoid using a lot of text. Designing a graphic for a newspaper? You'll have to deal with size constraints and try to explain the important parts of your graphic.

    However, whatever you're making your charts and graphs for, whether it be for a report, an infographic online, or a piece of data art, there are a few basic rules that you should follow.

    There's wiggle room with all of them, and you should think of what follows as more of a framework than a hard set of rules, but this is a good place to start for those just getting into data graphics.
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  • The Boom of Big Infographics

    May 6, 2010  |  Infographics

    Big information graphics have been around for a long time. They've come in the form of maps, visualization, art, signs, etc. That was all on paper though. In the past couple of years, humongous, gigantic, and often really long infographics have found their way onto the computer screen, through blogs and news sites. Some are great. Some really suck. The volume is booming for both.

    Let's take a look at when this all got started, where the trend is headed, and how much we should really read into these things.
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  • Think like a statistician – without the math

    March 4, 2010  |  Design, Statistics

    Think like a Statistican

    I call myself a statistician, because, well, I'm a statistics graduate student. However, ask me specific questions about hypothesis tests or required sampling size, and my answer probably won't be very good.

    The other day I was trying to think of the last time I did an actual hypothesis test or formal analysis. I couldn't remember. I actually had to dig up old course listings to figure out when it was. It was four years ago during my first year of graduate school. I did well in those courses, and I'm confident I could do that stuff with a quick refresher, but it's a no go off the cuff. It's just not something I do regularly.

    Instead, the most important things I've learned are less formal, but have proven extremely useful when working/playing with data. Here they are in no particular order.
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  • 11 Ways to Visualize Changes Over Time – A Guide

    January 7, 2010  |  Design

    Deal with data? No doubt you've come across the time-based variety. The visualization you use to explore and display that data changes depending on what you're after and data types. Maybe you're looking for increases and decreases, or maybe seasonal patterns.

    This is a guide to help you figure out what type of visualization to use to see that stuff.
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  • 9 Ways to Visualize Proportions – A Guide

    November 25, 2009  |  Design

    With all the visualization options out there, it can be hard to figure out what graph or chart suits your data best. This is a guide to make your decision easier for one particular type of data: proportions.

    Maybe you want to show poll results or the types of crime over time, or maybe you're interested in a single percentage. Here's how you can show it.
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  • 30 Resources to Find the Data You Need

    October 1, 2009  |  Data Sources

    Let's say you have this idea for a visualization or application, or you're just curious about some trend. But you have a problem. You can't find the data, and without the data, you can't even start. This is a guide and a list of sources for where you can find that data you're looking for. There's a lot out there.


    Being a graduate student, I always look to the library for books and resources. Many libraries are amping up their technology and have some expansive data archives. Many statistics departments also tend to keep a list of data somewhere. Continue Reading

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