nice summary by UP

I have been religiously wearing my UP24 band over the past two months, after taking a hiatus from the technology while pregnant. I originally strapped it on to have record of my sleep. With a newborn, of course sleep looks much different now; there's something strangely gratifying when you can not only know that's the case, but also see it. Over time, I've seen the number of night-wakings generally go down (though last night was an exception, which I feel as I groggily type this post) and sleep consolidate into bigger chunks as my little chunk sleeps for increasingly longer segments. I can start to see a pattern emerge (bed at 11pm, wake for feeding at 2-3am and again at 5-6am, get up around 8am). Visual evidence of slow but measurable progress!

What caught me by surprise when I started wearing the band again is the motivation it inspires when it comes to my activity level. The recommended goal is 10,000 steps per day. When I don't hit it, I feel a bit of shame. When I do hit it, I feel a gratifying sense of accomplishment. That sense of accomplishment goes up as the amount by which I surpass the goal increases. This motivates me to get out and move on a daily basis to ensure I'll hit my goal.

So all of this is a long prelude to the summary from UP that hit my inbox this morning. I tend to post a lot of examples of data viz with which I take issue, so thought I'd mix it up and focus this post on one that I found to be effective. Of course there are things that I would have designed differently, but this summary gets the job done. It's keeping me motivated. Let me step you quickly through what it shows.

It starts with an overall summary of week-over-week changes:

Relative to the prior week, my average sleep per night went down a hair and my movement increased a little. I like the big, clearly articulated takeaway: you held steady.

This is followed by detail on my sleep this past week:

My average nearly hit the nightly sleep goal of 8 hours. I even beat the goal three times (versus prior weeks where I haven't hit it at all!). In fact, this summary looks perhaps deceivingly good, though the number of nights of uninterrupted sleep, at 0, starts to point to the newborn effect. It will be life-changing when that number moves, even by one!

The sleep summary is followed by a movement summary:

I hit my 10,000 step goal each day (my informal goal for myself is to hit it every day in August). You can see the days where a jog or long walk really put me over the top. My most idle time of 8-9am comes as no surprise, as that's when the little one eats breakfast, sequestering me to an armchair for the better part of an hour. The rest of his day remains less predictable.

It's a straightforward and simultaneously (for me, at least) motivating summary.

For more on UP from a numbers-person's perspective (including downloading the data it captures to analyze on your own), check out Nathan Yau's recent review here. For more on the cool insights the team at Jawbone is starting to make based on the crazy amount of data they are amassing, check out their blog (for example, this post). They've shared it with others who have started to analyze and share as well (here's a recent WSJ example, though I still lament the lack of color-key on the heatmaps - ok, turns out it is impossible for me to write a blog post without critique!).

Someday, I'll download all of my data and perhaps do something fun with it. For now, I'll continue to check out the daily and weekly summaries to track my progress and for that feeling of accomplishment when I hit my goals.

the challenge of teaching data visualization

You want to increase your skills when it comes to creating effective, captivating, and informative data visualizations. I want to teach you. But with this, comes certain challenges. It is these challenges that I hope to discuss in a SXSW 2015 panel (along with three expert colleagues: Jon Schwabish of, Kaiser Fung of Junk Charts, and Ben Schneiderman of University of Maryland).

But we need your help.

Over 3,000 proposals have been submitted for SXSW Interactive. Obviously, only a fraction of these will be chosen for sessions. Public voting accounts for about 30% of the decision making process. That's where you come in.

Please take 20 seconds to vote for our session by clicking the button below (which will prompt you to create an account before voting if you haven't already - it's super fast, I promise).

Vote to see my session at SXSW 2015!

Check out the following slideshare for more detail on the session topic.

The Challenge of Teaching Data Visualization from Jon Schwabish

Thanks and I hope to see you at SXSW 2015!

love and hate for NYT graphics

I rarely find myself in front of a computer these days. My time has been overtaken by a tiny little man (related post), who insists on spending hours a day with me, sitting in a rocking chair, at least one arm rendered otherwise useless by cradling and cuddling (not a bad way to spend one's time, I must admit). Only in the past couple of days have I emerged from my lack-of-sleep haze to realize that it only takes one hand and my cell phone to reconnect with what's happening in the world via Twitter and Feedly.

It was during one such cuddling-and-catching-up session that I came across the recently published New York Times article, Gains seen for Medicare, but Social Security holds steady. To be honest, I'm less interested in the findings, but the data visualizations within the article caught my eye.

At first glance, the two visuals look really clean and well-designed. Still, I am initially a skeptic when it comes to looking at any data viz. I started out hating the two data visualizations included in the article, but with a bit of patience, my feelings morphed from hatred to... well... I guess we can call it love and hate. Let's take a look at the two visuals included in the article and do a little analysis of each.

Here is the first:

My initial thought was that, with time on the x-axis, the above should be a line graph. But I was too quick to judge: it's not exactly time that's being plotted, but rather the forecast for expected Medicare solvency at the given point in time. Given this, it makes sense to treat the points as discrete (rather than continuous) in a bar chart, as has been done above.

My next would-be beef was with the gridlines drawn across the bars. Gridlines often add clutter, bringing little informative value with them (and making the visual appear more complicated than necessary - related post). But the increments of 5 on the y-axis and coordinating gridlines allow your eye to do a bit of math without your brain really having to. The gridlines within the bars could perhaps be made a little thinner so your eye would still see them without the cluttering effect, but this is minor.

While it took a little time to like the above components of the graph, other design features were love at first sight: it's well-labeled with clear title, axis titles and labels, the words above the graph tell you what you are meant to takeaway while attention is drawn to this point in the data - the most recent forecast - via difference in color.

Now let's turn our attention to the second visual included in the article:

This time, I'll begin with the components I like. Again, the takeaway is clearly articulated via text. Everything within the graph is clearly labeled. But in this case, I'm having a hard time moving to full-on love. The background shading and gridlines - though I can understand the motivations for them - bother me. And the labeling within the graph just doesn't seem as clean to me as it could be from a placement standpoint.

I really wanted to remake this visual, but was unsuccessful in finding the data being graphed and not patient enough to take the time to eyeball it. When I was considering the design choices I would make (get rid of grey background and gridlines, change the forecast portions of the lines to dashed lines, label the series with both title and % change to the right of the 2023 projections), I read the takeaway at the top again and realized that I don't even agree with it. The callout says the forecast is for faster growth for Prescription drugs and Physicians, yet the slope for the Hospital line is steeper (faster growth) than the Prescription drugs line. I assume it's true that the increase over the entire period forecast for Hospital is 25%, as noted, but the forecast is for a brief reduction followed by rapid increase, so I find this description to be misleading. 

Based on the data alone, to me more interesting is the inflection point and subsequent forecasts for Physicians and Hospitals. Historically, Hospitals have accounted for the majority of cost, but this is projected to change, with Physicians expected to make up a bigger (and rapidly increasing) proportion of beneficiary cost going forward. Interesting. I wonder why that is?

Perhaps this is explained in the article. But my call-to-duty by the little man is bound to be soon, so rather than go back and read the article, this is where I'll wrap up today.

My hatred turned to love in the initial visual, but I failed to get there in the second case.

What do you think? What do you like about these graphics? What would you change?

lead with story

When asked to write a guest blog post for this month's focus on storytelling on the Tableau Public Blog, I spent some time reflecting: if I had just a single lesson to share, what's the #1 piece of advice I'd give in this space? I'd boil it down to three simple words: lead with story. The following is the guest post I authored.

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and then there were four

Three may be a magic number, but my favorite number of the moment is four.

As in, we are now a family of four.

We welcomed Dorian Werner Knaflic into the world on June 23, 2014. You may recall the timeline that I posted after Avery's arrival. In comparison, this birth was pretty much the opposite experience (we had an appointment, walked into the hospital prepared for what was happening, baby came home from the hospital the same day I did). I continue to be amazed at the absolute perfection of this tiny being.

And because it wouldn't be a proper storytelling with data blog post without a data visualization of some sort, I'll share the following, created from some of the stats I've been collecting, both by hand and with my UP24.

A couple things are clear: Dorian is eating plenty, as evidenced by his steady weight gain since hospital discharge on 6/26. The longest sleeping stretch I get is typically the one preceding the first nighttime feeding (though there have been some nice stretches between that and the second night feeding as well). I was (naively) hoping that clear eating patterns would emerge, but we aren't quite there yet. In time. Surely there are other interesting insights to be drawn, however since I'm operating on a somewhat impaired brain from broken sleep, I'm not going to look too hard for those now.

Rather, let's focus on the cuteness of this little one...

Dorian Werner Knaflic
Born June 23, 2014
6 pounds 11 ounces

leverage animation: what you present vs. what you circulate

A common challenge in storytelling with data is the following conundrum. When presenting content live, you want to be able to walk your audience through the story, focusing on just the relevant part of the visual. However, the version that gets circulated to your audience - as pre-read or takeaway, or for those who weren't able to attend the meeting - needs to be able to stand on its own without you, the presenter, there to walk the audience through it. In this post, we examine a strategy for dealing with this challenge.

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