Experimenting with Data Visualization: Learning in Times of Crisis Part II

In early Spring 2020, I was frustrated with the lack of informative COVID-19 data for my county, so I started building my own spreadsheet “dashboard.” I’ve collected the single numbers my county reports each day – e.g., new cases, hospitalizations, deaths, testing – added them to my spreadsheet, then began exploring different graph types to tell different stories. 

Beginning with purpose

For me, the purpose of this work is twofold. First, it’s an opportunity to keep myself up-to-date and use local data to inform decisions to venture out in my community. Let’s be be honest here, too. Some charts scare the hell out of me with what’s going on pandemically speaking (yeah, I made up that word), while others put me more at ease. That’s the power of data visualization to tell a story, evoke emotions, and catalyze change. Whoever said “data are objective” or “numbers are just numbers” is simply wrong, but that’s a storyline for another day…

The world cannot be understood without numbers. But the world cannot be understood with numbers alone.

– Hans Rosling (physician, academic, and public speaker)

Second, this presents an opportunity to work with different datasets and experiment with chart types I don’t normally use, thus enhancing my data visualization skill set – getting ready for whatever new project lies just around the corner. 

As mentioned in the first blog on this topic, right now, these graphs are for me (well, and you too now!), so I haven’t considered one of the most important questions in data visualization – who is the audience for these graphs? Who will read and consume them, and what exactly do they need in order to make meaning from them? Were I to share them for the purpose of communicating specifics about the pandemic in our area, my thinking would certainly change .

Experimenting with Data Visualization

I like experimenting with data visualization and writing about it. In a way, I’m making a map of my territory, knowing for sure that it will always be but a representation of reality – an abstraction – and always far from perfect. It’s clear that some of my graphs work well for the datasets while others clearly do not, and if I set out to craft a dashboard or report with a clear message for an identified audience, chart choices would likely change. 

Instance Chart

Instance charts can handle massive amounts of data and communicate trends over time without labeling individual data points. #ShowYourStripes  for example, takes up to ~170 years of climate data for different places on earth and plots them in what they call “warming stripes.” In my first post, I used a tutorial from Evergreen Data Academy to learn to make an instance chart. I’ve since updated that chart with a different color scheme and figured out how to add a line chart on top of it for an additional visual of the same data. 

Instance chart with line chart

Waffle Chart

Waffle charts, a version of an icon array, are great for visualizing a single number. I like this chart for its ability to put things in perspective. Just as I’m agonizing over the daily number of new cases and wishing it would go down to zero, I realize how lucky I am to live in the place that I do, even with its proximity to a former hotspot – New York City. This one is a simple “adjust and color” waffle. In other words, I eyeballed the column heights and row widths to get a (nearly) perfect square. I inserted a square shape on the sheet to use as a guide. I estimated the partially colored cell, and placed a little grey rectangle to hide part of the red cell. Low tech!

Stacked Bar Chart

Building on that theme of charts that make me feel better, I tried this 100% stacked bar to show the rate of infection by age and sex against total population estimate for my county. On the one hand, the tiny colored slivers in each stack give me hope, but that doesn’t mean the numbers are insignificant. 

Stacked bars chart of cases by age and sex

Vertical Dot Plot

To see other stories in the data, I tried plotting cases and some outcomes along a vertical dot plot for men and women (how my county reports this dataset). This allows me to see that women make up more cases, but that men seem to be beset with greater numbers of negative outcomes.

Vertical dot plot


There are times I learn a chart type and think, “I’ll never have reason to use that.” So far I’ve been wrong EVERY. SINGLE. TIME. While treemaps can be visually appealing and informative, they can also be confusing and challenging to make sense of. Every now and then, I find an opportunity to create one. Here, I assigned labels to age groups and used color, data labels, and brackets to summarize, along with a descriptive title that highlights one of the conclusions. 



Are you an amateur (or professional?) COVID-19 data tracker?

Are you experimenting with visualizing COVID-19 or other data? Feel free to share your work in progress here. I’d love to see it.

Here are some of my favorite resources for learning and experimenting with data visualization.

Experimenting with Data Visualization: Learning in Times of Crisis

I’m using time at home to try new things or return to old ones. I’m cleaning the basement, and planting a vegetable garden, things I haven’t done in a few years, and experimenting with data visualization with new and different datasets. Like many of you, I’ve also been transfixed by the news and tracking global and local COVID-19 data, as it makes for an intense study of data visualization and communicating with data. 

So many questions bubble up as we consume data like this. Why these data points? Why did {organization} choose that chart, graph, diagram, or single number to communicate their message? Why do they seem to be collecting this data but not that data? How can I make that cool visualization I saw on that website? 😁

Too close (to a hotspot) for comfort?

I live in New York State, one of the earliest US COVID-19 hotspots. I’ve watched the news in horror as tens of thousands of my statemates (not a word, but I’m using it!) fell ill or passed from the virus. But while tracking a virus by geopolitical borders seems arbitrary in a way, it’s also what we have at hand and makes for understandable reporting to the public. People are naturally concerned with data on a local level. We want to know how close to home the virus has hit. The vast majority of COVID-19 cases in my state, however, have been around the New York City area, and I live more than 350 miles northwest of there, so I’ve been studying data from my county, Monroe – 1300+ square miles (relatively small as counties go), with about 742 thousand people. 

I’m frustrated that our county dashboard and daily data releases rely primarily on single numbers – how many new cases, people hospitalized, deaths since yesterday, etc. I wanted to look at trend data, so I opened Excel and got to work experimenting with data visualization.

What works for whom, when, and under what circumstances? 

This is a question I learned to consider doing evaluation work, and I mention it here because what follows is a number of works in progress… unfinished, unrefined, undecided-upon graphs I made, just to see what the data looks like when I view it in different ways, AND to try my hand at a few chart types I don’t often use given the work I do. Right now, the graphs are for me (well, and you too now!), so I haven’t considered one of the most important questions – who IS my audience? Who will consume these graphs and what do they need in order to make meaning from them?

It’s clear some graphs are not appropriate for the dataset or for communicating certain messages or communicating to certain audiences, but hey, I like experimenting with data visualization and writing about it. One key decision I need to make (and soon!) is how much data I want to see in each graph – one week? Two weeks? A month? As you’ll see, most of these graphs have outgrown the number of data points currently in them. 

Experimenting with Data Visualization

Combo Column Graph with Line

The day-to-day data is so variable and fraught with questions. Was there more testing on some days? Do weekends make a difference? Does the availability of testing in different locations make a difference? For that reason, I thought it best to focus on a 7-day average vs the day-to-day ups and downs, but I still wanted to see the day-to-day. I added a couple of annotations where there were days that may have compelled some people to gather, to watch for potential spikes in the weeks that follow.

Combination column ad line charts of daily new cases and deaths

Overlapping Bar Chart

I love overlapping bars. What a fabulous way to show a subset of a set. I frequently use these in my work. This shows me clearly that while hospital admissions have been on the rise, ICU placements have decreased and make up a lesser proportion of those in the hospital. (Note: there is missing data in the early days when the county didn’t report these numbers for a bit.)

Overlapping bar chart of daily hospitalizations and ICU placements

Vertical Dot Plot

I tried the same dataset with a different graph. I love vertical dot plots. So great for comparing two things. While this looks attractive, and to me really emphasizes that gap between how many hospitalized and how many in ICU, it isn’t quite the right chart because ICU placements is a subset of hospitalizations. They’re not two separate groups. I shared these two charts with private Facebook and Slack groups associated with Evergreen Data Academy and everyone there agreed that the overlapping bars communicate this dataset better. That’s why we experiment, though, right? Note too that this graph is getting way to big and the dates on the x-axis are diagonal – a big no-no! I don’t do neck-breaker charts! 🧐

Vertical dot plot of daily new cases

Waterfall Graph

I’ve rarely (if ever?) used a waterfall graph, but wanted to ensure I could make and customize one if I needed to. This dataset isn’t quite the best fit since 1.) there are no decreases and waterfalls work best when you have both increases and decreases, and 2.) the individual data points are really small, and it’s hard to see the differences even when they’re substantial. So, an increase of 96 doesn’t appear much bigger than an increase of 19.

Waterfall chart of daily new cases

Instance Chart

I have to admit, this is one of the coolest charts I’ve ever made! Instance charts are relatively new on the scene, and I used a tutorial from Evergreen Data Academy to learn this one, so you’re seeing my first attempt! Now here’s one chart that can withstand a LOT of data (people have famously tracked eons of climate data in one of these), so I can keep collecting and adding to this one for a long time!

Instance chart of daily new cases

Excel Table with Indicator Dots

Before I started making charts, I entered data in a simple Excel table. I collect all data my county reports, and keeping it all in the table for the time being, even though I don’t pay too much attention to certain columns. I wanted to see whether some daily changes were going in the right direction or not, so I added indicator dots to three of my columns looking at how my 7-day average of new cases per day, new deaths per day, and new hospitalizations per day was improving (i.e., decreasing), staying the same, or getting worse (i.e., increasing). I added conditional formatting rules to highlight highs and lows in some columns and color-coded, so the “good color” is green whether it’s a high or low point (e.g., low hospitalizations is good; high number of tests is good). I’m currently heartened by the 6-day streak of declining 7-day average of new cases I can see in column E with the green indicator dots.

I’ve rearranged and grouped columns in different ways, changed column headings, etc., and added conditional formatting. Again, a work in progress that I continue to evolve as I think about what I want from this visualization. A note about colors: Dataviz rockstar Stephanie Evergreen (and many others) advise against using traditional stoplight colors – red, yellow, and green – as they can be problematic for those with forms of colorblindness. I couldn’t agree more, and if I were to make this table/dashboard public or for a specific audience I would make different design choices (Note: The column headings got squished when I shrunk this to get the screenshot).

Excel table with indicator dots; multiple COVID-19 data points

Are you an amateur (or professional?) COVID-19 data tracker?

Are you experimenting with visualizing COVID-19 or other data? Feel free to share your work in progress here. I’d love to see it.

Check this out: My friend Elizabeth Grim explores how we think about COVID-19 data in Revisiting COVIDeracy: What’s in a Number?

Here are some of my favorite resources for learning and experimenting with data visualization.

My work is a little sketchy today…

I stopped using pencils in 5th grade.

Without ceremony, my teacher rather mundanely announced to the class that we were now allowed to use pens on our school work. Pencils would henceforth no longer be required (except for math, of course). Huzzah! This was no small moment for me. Pens, like yearbooks, and switching classrooms for different subjects were a hallmark of middle school and thus, a monumental step toward adulthood in my 10-year-old mind. I giddily seized the opportunity to give up the graphite, filled my backpack with a fistful of Bics, and never looked back. Ballpoints would provide the gateway for the next four decades to roller balls and gel inks. I sought finer and finer points over the years, and a enjoyed a brief segue through a somewhat pretentious fountain pen phase. But I never purchased another pencil. Ever. (more…)

The Future of Dataviz: What’s in Store for 2015?

Happy New Year!

Forbes claimed in early 2014, “data visualization is the future.” Microsoft called 2014 “the year of infographics.”

And a few weeks ago, my friend, fellow evaluator, blogger, and dataviz aficionado Ann K. Emery  proposed that a bunch of bloggers write out our predictions about data visualization in 2015. According to Ann’s invitation, The only rules: Nobody gets to discuss their predictions/wishes ahead of time. It’ll be fun to see how our predictions overlap (or don’t), and then we can have live discussions via the comments section of our posts.

With that, I invite you to read not only this post, but also Ann’s Blog to see what she has to say, and to check out a few others as well.  (more…)

Data Visualization: Sail Forth – Steer for the Deep Waters Only (Part II)

Either you decide to stay in the shallow end of the pool, or you go out into the ocean.

-Christopher Reeve

In an ongoing quest to improve my data visualization skills, I recently ventured out from the security of the shallow end and took a stab at the next level of sophistication with some basic charts. In Part I of this series I describe the process I used to create my first back-to-back bar chart. Once again, I learned most of the skills I applied for these from Stephanie Evergreen and Ann K Emery, both wonderful dataviz artists and great teachers.  (more…)

Data Visualization: Sail Forth – Steer for the Deep Waters Only (Part I)

Sail Forth- Steer for the deep waters only. Reckless O soul, exploring. I with thee and thou with me. For we are bound where mariner has not yet dared go. And we will risk the ship, ourselves, and all.

-Walt Whitman

I consider myself a novice, for now, staying safe in the shallow waters of data visualization. I’ve learned to create clean and modern-looking bar, column, and {gulp!} the occasional pie charts. I follow basic safety rules, dispensing with the unnecessary – gridlines, tick marks, superfluous axis labels and legends. I avoid default colors, and proudly leave 3D charts to the real amateurs. To venture beyond that, into the deep current of dashboards and interactivity, I would need a life jacket and tow rope. Recently, though, I waded a bit deeper, and experimented with two variations – back-to-back bar charts and small multiples. In this post, I share how I created my first back-to-back bar chart. Part II will tackle the small multiples.  (more…)

#Eval13: #omgmqp, ESM, DataViz, Program Design, Blogging, and the Great Big Nerd Project

Here it is, less than a week after returning home from Evaluation 2013, and I’ve already used what I’ve learned in all three workplace settings. I’ve also enjoyed reading other bloggers’ conference highlights (see below for links) as they in a sense, let me peer vicariously into sessions I didn’t attend, or they enhance my own experience by offering a different perspective on sessions I did attend.

Here’s a recap (in a “longform” post, which, I’m told, is an effective blogging strategy) of what resonated most with me: (more…)

Unconventional Wisdom: Putting the WHY Before the WHAT of Presentation Design

There’s really not much good on television anymore. So, I enjoy some down time outside of work and entertain myself by designing slide decks. I just uploaded my second to SlideShare. While it’s all fun, there’s a purpose here too, and for me, it’s to practice what I’ve been learning about data visualization, information design, and presentations. There’s certainly no paucity of engaging, compelling source material available out there.  I’m so excited that just as I finished this project, the newest issue of New Directions for Evaluation (a publication of the American Evaluation Association (AEA)) – a Special Issue on Data Visualization – was released online and features the work of some of my favorite evaluators, data visualization experts, and information designers. You can read all of the abstracts here(more…)

Data Visualization & Information Design: One Learner’s Perspective

I‘ve been reading a lot on these hot topics and, ever the teacher, I know that applying my new learning, and teaching it to others is the best way to deepen my own understanding. With that in mind, I’ve created a slide deck and branched out to another social media outlet – SlideShare – in order to be able to share this content with you!

Once you’ve enjoyed this slide deck (or perhaps before doing so), check out my “before” slide below it. I originally had no intention of sharing this, but happened to stumble upon a PowerPoint presentation I had created for my dissertation defense. Yikes! What a dramatic illustration of what NOT to do on a PowerPoint slide! And I assure you, I presented it to my committee exactly as you see it here, and most likely read aloud what is on the slide (and the many others that complete the “show”). My only defense (pun intended!) is that it was 2007, and much of the information I share with you today was not yet “out there,” and quite frankly, I didn’t know enough to be looking for it!  (more…)

Declare YOUR independence (from bad PowerPoint) today!

It’s Independence Day here in the US and today, I’d like YOU to declare YOUR independence from bad PowerPoint. No more traditional title and content slides. No more endless bulleted lists. No more sentence after sentence slides that push the limits of slide boundaries. No more cheesy clip art “artfully” placed in the bottom right-hand corner of each slide.

Soon after I published “What NOT to Present” after attending a course at the American Evaluation Association Summer Evaluation Institute with evaluator-turned-information designer Stephanie Evergreen, another evaluator, Excel guru Ann Emery posted a link to economist and dataviz specialist John Schwabish’s slideshare: Layering: A Presentation Technique. As soon as I saw these slides, I knew I had to share them too.  (more…)

Is education (finally) joining the dataviz movement?

Poor education field. Why is it we always seem to be the last to know? As a career educator, I get excited hearing about new ways of thinking, knowing, or doing. Often I’m disappointed to find out that in fact, that what is new to us has been used in business or other fields for years. Such as it is with data visualization. Journalists and evaluators (among many others) have ridden the dataviz bandwagon for years now.  (more…)

What NOT to Present

A couple of weeks ago I had the honor of teaching and learning at the American Evaluation Association (AEA) Summer Evaluation Institute in Atlanta, GA. I taught a course entitled “It’s Not the Plan, It’s the Planning: Strategies for Evaluation Plans and Planning.” I’ll write about that course another day.

On Sunday June 2, I had the pleasure of taking a full-day pre-institute course from information designer Stephanie Evergreen, called Presenting Data Effectively.

Stephanie is to presentation design what Stacey and Clinton are to fashion (if you’re missing the analogy, click here). She’s the “What NOT to Present” guru. Show up with bad PowerPoint design and she will teach you “the rules.”  (more…)

Chart Changin’ Cha-Cha

As mentioned here, I’m learning about the art of data visualization and presentation, and am currently enrolled and engrossed in Alberto Cairo’s Introduction to Infographics and Data Visualization MOOC*  and loving it.

Professor Cairo has shared a wealth of information graphics for my 4,999 classmates and me to study and critique as he enlightens us on principles of graphic design. New York Times Infographics  is one great site to explore the variety of graphics designers use to convey information.  (more…)

Can a DataViz novice become a slide snob?

Yes. Yes, I can. Like so many other evaluators (and journalists, presenters, trainers, etc.) I’ve been sucked into the compelling world of Data Visualization and Reporting, Infographics, and the art of presentation. It’s evaluspheric reform at its best. In fact, I can see a new branch growing on Christina Christie and Marvin Alkin’s Evaluation Theory Tree. It’s the REPORTING branch, and it’s just starting to bud. It will certainly feature data visualization leaders and thinkers, and I imagine the first name to appear near the base will be Evergreen (hey, now THAT’S a name that works, given the tree metaphor!).

Stephanie Evergreen’s Potent Presentations Initiative (P2i) has helped launch a new wave of evaluation DataViz & Reporting enthusiasts, and catalyzed my newest learning journey which included giving my first Ignite Presentation at AEA2012. Back in 2010, John Nash mentioned two fabulous books in this aea365 post: Nancy Duarte’s Slide:ology, and Garr Reynolds’ Presentation Zen.  I can assure you, they’re both WELL worth the investment. Susan Kistler (among others) has posted and presented many, many tech tools and resources to fuel the cravings of any data or tech geek (a quick search on aea365 yields over a dozen of her posts on the topic).  (more…)