Data viz Archives - Agile Analytics, LLC

Visualizing a winter storm

By Lindsay Lamb

This week brought us some very cold, very wintery weather here in Austin. We all spent a lot of time refreshing our various weather apps, checking the news, and listening to meteorologists’ weather reports on the radio. The reports shifted by the day, and sometimes even by the hour. I get it, this was an unprecedented weather storm which hasn’t been experienced in Austin in nearly 30 years. Many of us went without power, heat, water and food. Other than completely redoing the state of Texas’ power grid, offering some more detailed insights into the weather, power and water issues may have helped.

Let’s start with the weather. One thing that would have made knowing when the cold weather was coming, and what type of cold weather was likely to hit our area easier were error bars. Okay, okay, I know this wouldn’t work in a radio spot, but I think it could work in data visualizations on the news, and on apps or in a newspaper. Now, I’m not trying to upstage meteorologists (they have a hard job!) but perhaps some of their visualizations could use a little freshening up.

Here are some examples of what we typically see:

Screenshot of Austin, TX weather forecast. — Source. The Weather Channel app on my iPhone

This is pretty a pretty typical experience that people have with navigating the weather. It is definitely informative, and you can click on the day and look at the forecast by hour, but there are still several unknowns. For example, it is always difficult interpreting (let alone explaining to an excited 2.5 and 6 year old) that a 35% chance of snow actually means there is a 35% chance that there will be snow in the greater Austin area, not that there is a 35% chance of snow at our house.

Here are some additional examples from the Weather Channel website and the Weather Underground website:

Source. The Weather Channel and The Weather Underground websites

The Weather Underground’s website contains much richer information such as time of day when the highs and lows will peak (which is helpful in Texas when sometimes the daily high occurs first thing in the morning), and what the temperature actually feels like versus what the reported temperature is.

Now, wouldn’t it be nice to also see something that includes a range of values, and the likelihood of wintry conditions happening? Here are two simple ideas. One is a traditional box and whisker’s plot, the other is a simple line graph. Obviously, having lots of these wouldn’t be helpful, but for one day it would be super helpful to see that the low temperature could drop below freezing, so you should probably take steps to protect your pipes.

Note. These are some simple ways to show what the projected temperature is (gray circle, or midline), and what the high or low ends might be based on the standard deviation of potential values.

After I created these charts, I watched the news. I was happy to see that one weather reporter showed an image with sliding bars indicating the likelihood of freezing rain, snow or sleet. This was incredibly helpful! Sure, we were going to get some snow, but the thing we were most likely to get was freezing rain. Had he not showed these sliders, we just would have heard 90% chance of a wintry mix. Not helpful.

So, speaking of freezing rain, that is what caused power outages in my neighborhood. Several trees were downed by the heavy ice causing additional power outages. Now there is no way a data visualization could have helped us on the front end here. The only thing that could have helped would have been the city trimming trees near power lines and Austin Energy winterizing equipment. Where data visualizations did come in and help is on the back end. Here is The New York Time’s take on explaining what happened:

Chart from the New York Times showing power generation in Texas by fuel source. — Source: The New York Times

This visualization shows us when the winter storm hit, so we can clearly see what normal power generation was, and how all sources of energy plummeted due to the storm. This made a bad situation worse as this storm affected nearly every Texan.

Another great data visualization came from Austin water:

These data visualizations are helpful because they show how much water people normally use, and how the reservoir was nearly depleted. The other image is a heat map by zip code indicating where there were water issues as of 2/19/21.

The good news is that as of this writing (which took a big pause in the middle due to power outages and spending all of my energy keeping my family safe), most people have power, water and food. My daughter’s school survived the storm, and we will get back to pandemic life soon. I am thankful to all of our neighbors who came together to help out during this challenging time – in a pandemic no less! We came together to make sure people had warmth, water and food. In a time where we have been learning about resiliency, wants vs. needs, I am constantly amazed by how resilient my children – and all children – have been this year.

2020: A year full of data

By Lindsay Lamb

As we near the end of what was a year like no other, I wanted to reflect on what I have learned and shared with you all while writing this blog.

Front and center for me are all of the innovative, telling, and nuanced data visualizations that have come from modeling and documenting Covid-19 cases. Initially, my go-to source was the John Hopkins Coivd-19 tracker. This interactive website was critical when the virus was beginning to spread worldwide and across the US. It painted a realistic picture and helped us mentally prepare for what was to come. We could drill down to countries, states, and counties with alarming precision. As the pandemic progressed, I found myself turning more and more to the visualizations presented in the New York Times. To this day, a visualization is included front and center and it can be filtered based on all sorts of criteria. Additionally, there are usually several other visualizations showing trends based on different populations, trends intersecting with state policies, and more. I have really enjoyed digging into these visualizations (okay, I guess enjoyed is the wrong word as I am constantly looking up Travis County (where I live), Dona Ana County where my folks live, and Orange County in California where my brother and his family live). In a weird way, I find comfort in looking at the trends in the data. It helps me mentally prepare for what is to come and to take comfort in knowing that we are all in this together. I also think they are really thought-provoking and have made folks who are not usually comfortable with data become more so.

I have also enjoyed the infographics the CDC have created. Some are pretty alarming, such as this one:

Source: CDC.gov
Note: I would argue that all of dots should be in some variation of red as no one should swallow hand sanitizer, no matter who says it is a good idea, but other than that it is an effective infographic.

To be fair, I have not spent a lot of time reviewing the CDC’s infographics prior to the pandemic, so maybe they have always produced high quality work. Regardless, I have enjoyed their infographics and have gotten a lot of great ideas from them.

On a more personal level, one lesson I learned this year, okay re-learned, is to let your data tell the story. It is okay to have complicated data, let it tell its own story. I wrote a post about this issue earlier in the year when Andrea and I took data from a line graph and changed it into a lollipop graph. I also wrote about it in reworking some Covid data displays, which can get confusing. I think we all remember my extreme Covid makeover post 🙂 As a rule of thumb, if data cannot be explained on their own, consider breaking it up, making an infographic, or creating a one-pager.

Finally, as I recently discussed, simplify your presentations/reports/data visualizations and then simplify again. PowerPoint is a great tool to use in creating reports, and obviously presentations, and sometimes can get too busy with all the bells and whistles. As I wrote back in March, simpler is better.

This year was a challenge, to say the least. I spent a lot of time looking inward and forcing myself to work even when the world around me felt like it was falling apart. Having a blog was a good outlet for me and allowed me to share my experiences, some personal and some less so, in a way that also taught me important lessons. My hope is that at least one of you out there learned something or enjoyed my musings this year. I honestly believe that you are never too old/young/experienced/unexperienced/exhausted/stressed to learn something new.

So, my friends, go forth and visualize your data and remember my biggest lesson of this year: have grace and patience.

Timeline visualizations with Covid-19 vaccine data

By Lindsay Lamb

During the times of Covid-19, we have seen a boom for data visualizations. In my opinion, this is great for everyone (one of the few benefits of this awful pandemic)! Sure, there have been some misses (well, maybe some were more than misses) but people who never talked about or heard the term data visualization are now talking about data. Data visualizations are making it into mainstream media – TV news channels, newspapers, school district dashboards… everywhere.

We have written blogs about Covid-related data visualizations in the past, and as the end of the year is approaching — and with it, hopes of a vaccine — I thought it would be a nice time to share how folks are depicting vaccine timelines.

I came across this one on the Orange County Register that depicts how long it took other vaccines to get approved and stop the spread of a particular virus.

When I first saw it, I thought the syringe was a cannon. (Perhaps that says something about my current mental state.) I like the idea of using an icon (you know how I love icons!), but perhaps the shot icon is just too big. I do like the overall concept of the visualization — how you can see the timeline of when the disease emerged and when the vaccine was created. Simplifying could make this graphic so much more clear.

Here is a simpler version from A Learning a Day that averages the number of years it has taken to produce a vaccine and compares it to the rapid timeline for the Covid-19 vaccine. You can easily see that most vaccines take about 5 years to develop with several years devoted to the trial process. I like this graphic much better, but I also think it would be good to also include number of years across the top so you could see that the Covid-19 vaccine will be distributed in about a year and a half compared to 5 years. That information can be inferred, but it takes some previous knowledge.

Here is a similar graph from the New York Times:

The grey-ed out blocks indicate the normal timeline for producing a vaccine, which is also helpful in showcasing how truncated the timeline is for the Covid-19 vaccine. I also like this visualization because it shows the projected year when each component of the vaccine timeline is typically completed, which really hammers home how quickly and unprecedented the Covid-19 vaccination timeline has been.

As you can see, there are several ways to depict timeline data. When making a timeline visualization, the important rule is to choose what works best for the data. Let the data tell their story and build a visualization around that story. You might start with an idea in mind, but after messing around with your visualization realize you need to change plans. That is totally ok! Unsure what to do? Ask a colleague, friend, or family member (or all three)! Ideally, all of these folks should be able to read your visualization and understand it without having you explain it to them.

I hope you have found this era of data visualizations as inspiring as I have. Now get out there and share some data stories!

Quick fix: Survey items not displaying properly in your visualization

By Lindsay Lamb

With the election and obsessively recent rise in Covid cases, I have been spending a lot of time obsessively reviewing data. That coupled with reviewing recent survey data from teachers and students regarding their experiences in school (both in person and online), has left me knee deep in data. No matter where I review data, I keep coming across the same problem (which happens to be one of my biggest pet peeves): survey questions not displaying properly in a visualization.

Survey questions do not display properly when the survey question is too long, and instead of being able to read the full sentence, you get this:

Source: EdWeek To be fair, the website where I found this visualization was interactive and displayed the entire survey question when you hovered on the bar. This got difficult as the bars got smaller.

In some cases, you can resize the entire graph which can sometimes help. However, if you have limited space to display your data, you need to make some changes. You can either shorten survey item in your figure (which should signal that you should shorten the item when you administer the survey), allow the ellipses (my eye is twitching just writing this), or only show some of the responses (maybe set up small multiples).

If you do nothing (as in the figure above), this can be problematic. As the consumer of the data, you have to guess what the survey question is before you can make sense of the data. This is difficult and ultimately takes away from the overall meaning of the data. Sometimes you don’t choice. Some data dashboard programs (like Vocalize) do not allow you to change the space allocated to survey item labels. If, however, you are up for some Excel Ninja hacking, keep reading!

If you are using excel, simply change the data label by creating a fake row (I learned this super cool trick from Stephanie Evergreen), adding a data label for the fake data, and then displaying the survey questions with each corresponding label. Here’s a quick tutorial:

Create a fake column with fake data.

Now, add data labels in the figure to your fake data bars. As you can see, my fake data column contains one entry of 100% and the rest are 0%. I did this so I could easily grab the bar corresponding with the 100% data entry and add a label to it and all entries in that series (see below):

Now, change the label position so it is to the left your bar and add the survey question. If you are using a Mac, you have to type in each survey item. I know this is tedious, especially if you have a lot of survey questions, but it is worth it. You can clone data labels if you have found a position you like you can clone this in the label option menu. If you have a PC, in the label menu you can select label values from a range in your spreadsheet and voila! Done!

Still seeing some ellipses? No problem! You can now manipulate the size and position of your survey labels. Yay!!!

Here is a revised version of the visualization:

I should also note that I removed the axis Y-values and removed the horizontal tick marks to clean it up a bit.

This chart is still a lot to take in, so in reality I would recommend splitting it up, or changing the color of items to either highlight the most or least common practices… but that is for another day and another blog post 🙂

Back to the basics with PowerPoint

By Lindsay Lamb

Recently, Andrea and I were asked to create a mini-course on survey design for a virtual conference. Given our years of experience creating, researching, administering, and analyzing survey data, this seemed easy enough. We decided to call it Survey design 101. We were excited –this presentation was going to be fun! (Okay, that is nerdy, but we have to find things that motivate us these days!)

I got to work on the PowerPoint, and boy did I have a lot to say! I remembered all I have learned from my data viz heroes Tufte, Stephanie Evergreen (who just posted a great blog on changes to make to your powerpoint to take it form looking great in-person to looking great in a webinar), and Ann Emery. I used color purposefully, only included points I considered integral to the presentation and trimmed my content. Then I trimmed again.

I was also motivated to keep the presentation short. I wanted to ensure there was ample time for questions, and wanted to have some time for interaction – at least interactions over chat. I sent a draft over to our colleague to review and her feedback was clear, and also a little unexpected: remove bullet points (maybe have one or two per slide, but no more), and if bullet points are important, make them their own slide. Oh, and add more images and icons.

The icons and image piece was fine (you all know how much Andrea and I love icons and images) but I was worried about making my presentation too long by pulling apart the bullet points. It felt a little weird only having one sentence or thought on a slide, but what the heck. As always, Andrea helped by adding some flair to our slides.

Here is how we took one slide and pulled it apart.

*Here is what our slide looked like before*

…And here is how some of our slides looked after the revamp.

*Here are some slides containing the same information, but each point is presented separately making our points clearer.*

Importantly, all of the information is the same. The only difference is that the information in the first slide is pulled apart so each point can stand alone. If the point couldn’t stand alone, we removed it. We kept the ‘title’ of the slide (Pitfall #1 of surveys – Too long!) the same for consistency in all 3 slides.

Although I thought adding more slides to the presentation would make the presentation itself longer, the amount of time we spent discussing each point was the same.

As a final note, pulling apart information into multiple slides is even more important now as people are passively watching webinars rather than actively participating in-person during conferences. Unfortunately, our attention spans are even worse now that we are working from home (and if you are anything like me these days, you are likely watching a presentation while also helping your 5 year old with school, keeping your two year old from turning your dog into a Jackson Pollock painting, and ensuring everyone is quiet while your husband talks with state leaders), so keeping your presentation moving at a relatively fast clip will help with this issue… Maybe 😉

Extreme Data Viz Makeover: COVID Edition

by Lindsay Lamb

Andrea and I have mentioned it before: we are in a golden age, albeit a morbid golden age, of data and data visualizations. One way we can stay ahead of Covid-19 is by reading the reports and analyses that are readily available to the public. Sometimes this information can be super helpful, and sometimes the story gets lost in the data.

Generally, there are two places I typically go to check out recent statistics. These are The New York Times and Johns Hopkins University.

Recently however, I stumbled upon a site which examines data on COVID-19 and consumer behavior. The referenced survey was administered in mid March.

Take a look at the below charts. (These are similar to the ones from the site but have been remade in Excel.) As you look at them, see if you can spot some areas for improvement.

Chart about fears concerning the coronavirus outbreak. — *Data source & graph inspiration from* Astound commerce; figures remade from data
*Note. Survey administered in March 11-13, 2020*

Chart showing predictions on when shoppers feel COVID-19 will be under control or eradicated. — *Data source & graph inspiration from* Astound commerce; figures remade from data
*Note. Survey administered in March 11-13, 2020*

Chart showing how many people have altered their day-to-day activities in order to be as "contactless" due to COVID-19. — *Data source & graph inspiration from* Astound commerce; figures remade from data
*Note. Survey administered in March 11-13, 2020*

There is a lot to take in. Like, a LOT. These figures are pretty overwhelming.

Before I get into it, let me be clear: it is not my intention to trash talk the folks who created this. I am sure they worked really hard on it and that it took them a ton of time. Not to mention that they are working on top of the stress of COVID-19, childcare issues and job security. We are all in this together. I’m simply looking at this as data and thinking of ways to make it easier for readers to understand the story.

What these charts do right

The percentages are right there on the chart and I like the use of icons. Focusing on consumers is an interesting reference point. I haven’t seen this side of the COVID-19 story before, which is why I thought it would be interesting to highlight in our blog. Overall this chart succeeds at drawing in the reader, but at that point things get murky.

What’s missing?

The use of different types of charts is confusing and might lead the reader to think we’re looking at different types of data, or data in a different way. Really, I think the graphic designer was trying to create visual interest by breaking things up a bit, which is admirable, but not at the cost of easy interpretation. Additionally, the colors used throughout are really similar and readers must carefully search the legend over and over.

We ended up with some questions. What are the key takeaways? What are we really comparing? What is that wheel shape? And why are the hands floating?? These are not questions that you want your readers to be asking themselves, because in all likelihood, they will just walk away.

Let’s Do a Makeover!

Let’s start with the donut chart and the bar chart.

Here are some ways I came up with to make the story pop a little bit more.

Change to a Lollipop Chart & Add Icons

First, I redid the donut charts – which are not a great substitute for a bar graph – and used my favorite chart type: a lollipop chart. I added images of each country/region for some flair and to make it easier to see which country was which. Now readers can more easily find the story behind the data. It is easier to see that respondents from the Middle East expressed greater fears concerning COVID-19 than did survey respondents in other countries.

Add a Graphic

Andrea edited this to include a graphic (a scared face) because we planned to use the chart again, and she wanted to make sure the two charts were distinctly different at-a-glance. I think you could leave out the graphic out though.

I also made the chart title more informative. I gave a short statement of what I felt was the main message of the chart. Then I added some subtext. Readers should get enough information from the title to be able to easily interpret the chart.

Middle East Citizens Report Highest Fear of COVID-19. Collectively, three quarters of all people surveyed had fears concerning COVID-19. Fears were similar for the US, Canada, and Europe, but much higher among those surveyed in the Middle East.

Reworked chart showing that Middle East citizens reported the highest level of fear of COVID-19. — *Data source* Astound commerce survey – March 11-13, 2020

Let’s take a look at the second chart with the floating hands. We could do another lollipop chart. The use of the same chart type informs the readers we are looking at similar types of data and it invites them in to make comparisons. I bet that the story will pop here for you, even without a helpful title.

Before:

and here’s the after:

Middle East Citizens Report Largest Behavior Changes Due to COVID-19. Around half of people surveyed in the US, Canada and Europe had altered behaviors, but over 80% of those in the Middle East had made changes.

Reworked chart showing that Middle East citizens reported the largest level of behavior changes due to COVID-19. — [After makeover] *Data source:* Astound commerce survey – March 11-13, 2020

Let’s get back to that busy bar chart.

Before:

What makes this difficult to interpret?

The response options are included as a bar within the chart, making it difficult to read. It took me a while to even notice the survey questions, probably because the text is insanely small, making it difficult to read (particularly when you have old eyes and got up at 5:30 AM to get some work done before the kids wake up and chaos ensues… but maybe that is just me!).

It is also difficult to know what we are supposed to compare. Are we interested in comparisons across country? Are we interested when participants think COVID-19 will be under control? Do responses within country and across survey options add up to 100? All of these questions are difficult to answer.

Slider chart!

For this chart, I am going to use a slider chart (which is basically two lollipop charts combined). This makeover was a bit more complicated, but I think it tells a better story. First, I combined categories such that we now only report “within 1-2 months” and “6 months or more” (combining 6 months, one year and over a year). I felt like these two categories were more meaningful than looking at each one separately. I then created the two lollipop charts, added text boxes for labels and added my icons.

Color!

I selected the two tones from the green color family since the data were depicting participants’ likelihood to spend money. The main point is that we want two different tones in the same color family so people can distinguish them. I chose a deeper color for the “a year or more” category because that response was more intense.

Now the story is much more clear.

Now we can clearly see that most survey participants believe COVID-19 will be under control within 6 months. As always, a descriptive title helps.

Across regions, half of participants felt that COVID-19 would be under control in 6 months or more. Only one-third of participants across regions felt that COVID-19 would be under control in just a couple of months.

Reworked chart showing when citizens of varying countries think COVID-19 will be eradicated. — *Data source* Astound commerce survey – March 11-13, 2020

As a business owner, I would find this information helpful. I could mentally prepare for customers to not feel comfortable coming back to my business until 6 months at the earliest (but maybe more). This information might help me plan what to do to pivot my work for the next 6 months to keep things afloat until our economy starts to normalize. Obviously, opinions may have changed since the time this survey was administered, but it probably still gives business owners a good ballpark.

Regardless, this story got lost in the jumble of infographics. I’m sure there are other stories buried in the data, but that is what is fun about creating data visualizations. Sometimes you do not know the full story until you see it.

What are some key takeaways?

Multiple pie charts (and their cousin, the donut chart) are difficult to interpret, and it is usually best to represent the data differently.
Make data visualizations clean and not too busy. Use white space, and make sure the reader can walk away from your visualization understanding the information without having to read a full report.
If presenting several visualizations together, make sure the images/graphics/colors all work well together and tell a consistent story.
Use icons and images to draw interest and help the reader interpret the graph.
Add a descriptive title.

Chart Makeover: Line/Area to Hybrid Graph

by Andrea Hutson

The New York Times is one of the best places to look for good data visualization design. Some of their visualizations are truly mind blowing and thought-provoking, like this text analysis of presidential candidate song preference during their rallies (back when rallies were still allowed, and there were many more presidential candiates).

But sometimes, they miss the mark a bit. Take the graph below. The headline reads “U.S. Jobless Claims Soared to a Stunning 6.6 Million Last Week.”

Graph courtesy of The New York Times, April 2, 2020

This graph isn’t terrible, but it’s not great either. Three things I dislike:

Grey bar in the middle – I get that it’s supposed to indicate when the recession took place, but there’s something that feels really off about it.
Lots of irrelevant data in the middle – again, it’s there to show the general trend over time, but there’s just too much of it.
The relevant data is squished WAY over to the side. This is the main issue — we’ve got so much data, that the real story gets lost. And the orange color isn’t helping, either.

COVID-19 Job Losses 10x Greater Than the 2008 Recession

How could we better display these data? Let’s try a hybrid graph.

Just to keep things simple, I used the left side of the initial NYT graph, and added on to the right side. Instead of just having one skinny line to represent the jobless claims of today, I added a large bar to show the new data. The large, red bar draws the eye to the important information, and the change in style also makes it clear that there’s been some sort of break here.

Then I added arrows, descriptions, and labels. The graph still isn’t perfect, but I think it’s better, and, importantly, it tells the story more clearly. And it wasn’t hard to do. With my reuse of the left side of the graph, and a very simple bar, this makeover in EXCEL and Word took less than 5 minutes.

Making the graph above, I realized that there was another big story buried here. I looked up some jobs data, and found that, in Feburary 2020, there were 129.73 million full-time employees in the United States. I realized that the job losses from last week were huge considering that fact, so I thought about how to best visualize it. Here’s what I came up with:

Many Americans Have Lost Their Jobs In the Past Two Weeks

Source: The New York Times, April 2, 2020

I made this graph using a super-fancy proprietary software that I will be happy to sell to you for $199 per month.

Just kidding, y’all – I made this in PowerPoint.

Far from being sophisticated, these are just icons that I copy-pasted onto the screen a few dozen times. Add a couple text boxes, and it’s a pretty nice looking graph. Definitely better than a plain bar chart. Importantly, both graphs use the same information from the New York Times article, but tell distinct, and sobering stories.

Chart Makeover: Line to Lollipop

By Lindsay Lamb

A few weeks ago, we talked about one of the big controversies in data visualization – the pie chart. When used properly, pie charts can be a great way to display certain types of information. Sometimes it’s hard to know what those rules are, and which graph should be used where.

Are there guidelines on the best ways to present data? The answer is yes – there are many great tools, like Stephanie Evergreen’s chart chooser, that we often use to help me make these kinds of decisions.

One rule of thumb we use in our data visualizations is to only use line graphs when displaying time-related data.

Here’s an example based on confirmed Covid-19 cases (as of 4/1/20):

Confirmed cases for selected countries

Horizontal axis shows the number of days since the case count exceeded 500 in each country

These data are perfect for a line graph. Readers naturally start reading the graph from left to right; without reading anything else we can quickly understand:

the number of cases are increasing over time.
some lines have a steeper slope compared to others,
some countries have had confirmed cases of Covid-19 for a longer period of time than other countries.

We can visualize in our mind projections of each line (unfortunately for us here in the United States, that line seems to be trending straight upward, but who knows! Maybe you are reading this blog in the future and are saying, “What are they so worried about?!? We found a cure, and everyone was fine!” Let’s hope so).

What would the data look like if it were presented differently? Let’s look at the same data in column chart format (updated 4/1/20).

Source: Johns Hopkins University CSSE data map
Note: Data in this graph were pulled on 4/1/20. Some numbers are estimates, but numbers from days 15 and 20 were accurate as of the date and time in which they were pulled.

This graph just doesn’t tell the same story. We don’t get the same sense of time, or quite frankly, urgency. We cannot see how some countries flattened the curve compared to other countries. We cannot see where the U.S. is in terms of our trajectory compared to China and Italy. Sure, this graph is informative, and we do see that South Korea has far fewer cases over time, compared to other countries, but the power in that story is gone. Having the lines, slopes, and trajectories for each country in one graph together tells a much more compelling story. This is exactly why line graphs, or time series graphs, are so powerful. The time series graph created by Johns Hopkins CSSE contains a lot of valuable information all in one place (if you like data, I highly recommend taking a look at their dashboard). You can look at the graph and understand the story at first glance, without having to do much reading.

Now let’s walk through an example from a report we were working on with one of our clients. In this example, a line graph did not make sense with the data we had.

When I looked at this figure, as with the Johns Hopkins figure above, I started reading the data from left to right, and assumed the data progressed over time. Instead, the data were based on categories of information (e.g., scores on different components of the ACT) and displayed scores for treatment and control group students in each category. It was difficult to compare the two groups within each category. I was spending too much time thinking about the data within the figure, and if I was thinking too much, our clients certainly would be as well.

I talked to Andrea about the graph (I’m calling her out – it was her graph, originally) – and she said that she had considered a column chart, but it just wasn’t having the same impact as a line chart.

We needed a different visualization.

I reworked the data using one of my favorite graphs I learned from Stephanie Evergreen – a lollipop chart!

Now, the reader can look at the data and quickly compare the two groups (i.e., treatment and control) within each category. It is easy to see that the treatment students outperformed matched control group peers in each subject.

Want to know how to make a lollipop chart? Check out Stephanie Evergreen’s website where she walks you through an example. This is the type of skill we include in our telling stories with data training we are excited to offer. This training is available online so if you are looking to hone your skills while quarantined at home, let us know!

Take your PowerPoint skills to the next level!

By Lindsay Lamb

Looking for something to take your mind off of the corona virus? Wanting to brush up your skills in presenting data? Our office is going to strive to offer you fun blog posts to increase your knowledge and skills in data visualizations, data analysis, and SEL evaluation techniques. Read below for our current tip!

Ever been faced with creating a PowerPoint but felt stymied by fear (How do I get started? How do I make something look cool? What are my key points?)? Or do you just have writers block when it comes to presentations in general? I get it! I went to an Edward Tufte course wherein he spent a good chunk of time hammering home the message that PowerPoints are terrible and have dumbed us down! However, we live in a world of PowerPoint, so how can we make them work?

His advice, and my advice to you: keep them simple. Make the meat of your slides limited to things like data visualizations, images, movies, and icons. Limit the words on each slide (his recommendation is to use NO words).

This is all well and good, but sometimes, it is hard to even get started or know what to do.

A few weeks ago, my colleague Andrea contacted me to help save a PowerPoint presentation other colleague worked on and would be presented in just a few hours. She said it needed some help to make it pop, to be trimmed down, icons and other data visualizations. I had just two hours to help her out. I held my breath and waited to hear my email ding announcing the arrival of the presentation.

Ding!

I opened it up and read… and read, and read, and read. Slides were plain or had walls and walls of text.

The default powerpoint title slide doesn’t make your audience excited to hear your content.

Look at this slide.

First off, how do you feel when you see this? I felt a tightness in my chest. How was I going to fix this??? How would you fix this? Can you read anything? What are the key take-aways?

I felt the fear and writers block kick in. I took a breath, checked the time, and got to work!

My first step, something I learned from my graduate school advisor, was to start at the beginning and make the structure of the presentation look professional. I quickly found a photo relevant to the study and made it the title slide. I found logos for each school highlighted in the presentation. I even used the Design Ideas feature in PowerPoint. I added our business logos. I started to feel better. It felt official. It felt real. It felt comforting. I could do this!

Transforming the title slide by adding an image professionalizes your presentation right from the start

I then started working through the rest of the presentation. I used the Design Ideas feature when it helped tell the story. What were the key points? If I was listening to this presentation, what did I need to know? In a way, it was helpful that I had not written the first draft because I was able to read through the existing slides, find the story, and trim it down. There was a pattern. I could do this.

I found icons available through PowerPoint and the nounproject.com (check them out if you haven’t done so already! They have an icon for just about everything!). I created a template and used the same icon for similar themes so participants could anticipate information. Remembering what I learned in a training provided by Stephanie Evergreen while I worked for the Austin Independent School District’s Department of Research and Evaluation, I made sure to add white space. We are visual learners, and adding white space helps us see the main points more clearly. I cut, and then cut some more.

Adding color and breaking up the text was extremely helpful

I looked at the clock.

I chugged away.

That wall o’text is now an attractive list.

I couldn’t get to everything I wanted to, but I distilled the information, made a template for how to present the information (e.g., consistent colors, layout, font), added informative icons to trigger what information was coming, and made more white space to draw readers in. I followed advice from Tufte and Evergreen as best I could and sent my slides to Andrea.

Within seconds I got a phone call, “ This is AMAZING!!!”

Andrea then shared slides with our other colleague who said, “OH MY GOD, WHERE DID THIS GIRL COME FROM?!?! WOW. ”

So, my friends, you can do this too, I know you can! Remember these general rules, and they will take you far:

Powerpoint Best Practices

1) Keep your slides simple – less is more! No paragraphs if possible (put text in the notes if needed)

2) Use consistent branding and color schemes, and don’t use PowerPoint’s default of blue/orange if possible

3) Remember that PowerPoint is a visual medium. Increase the use of images, icons, graphs, and other data visualizations.

4) Decrease the number of words on each slide (add white space!). Some of the slides above could have been further augmented by moving each point to its own slide.

As a golden rule, TELL your audience key points with your voice, and SHOW them key take-aways in the slides; the two go hand-in-hand.

Good luck!

Covid-19 & Pie Chart Best Practices

by Andrea Hutson

You don’t need to search very hard on the web to find people that cannot stand pie charts. In fact, one of the first posts I found when searching for the topic was The Five Stages of Grief Over The Death of Pie Charts.

(Great read, by the way.)

But I think, as with many hated data visualizations, pie charts have their place, and today I’d like to share some things I’ve learned using Covid-19 as a timely example. (If you’re reading this way in the future, Covid-19 or Coronavirus was a nasty lung-infecting virus that swept the world in 2020, devastating countries and entire economies.)

I’m going to give a few examples of best practices for making pie charts useful. Much of what I’ve learned came from Ann Emery’s amazing course, Great Graphs. At publication time, Great Graphs wasn’t open, but I highly recommend following Ann and trying out her other courses. She’s at Depict Data Studio.

Do’s and Don’ts for Pie Charts

Don’t: Use too many categories.

Top 10 Countries with Most Confirmed Cases as of March 8, 2020

Bad pie chart example: too many categories. Shows a pie chart with 11 categories, the vast majority of which are too tiny to see. — This is sourced from a real pie chart, and I’m assuming the author is normally a great data visualizer who is too stressed and overwhelmed to use best practices at the moment.

This one is a rainbow of awful. What’s wrong with this chart? Several things, but the most important is that it has far too many categories. There are 11 in total, and all but the top 5 are almost impossible to see. Go ahead, try to locate Switzerland.

I’ll wait.

This chart forces you to use the legend to understand what’s going on, and in the end, the legend becomes the only thing that actually gives you relevant information. So what’s the point of a pie chart here? I would recommend converting this instead to a bar chart if you want all of the information. But first think – do my readers need all this information? Why am I presenting data about 10 countries instead of 7, 11, or 200?

If you can lower the number of categories, I give you full permission to If use a pie chart.

Do: Keep the number of categories to 5 or fewer

Top 10 Countries with Most Confirmed Cases as of March 8, 2020

A streamlined pie chart with just 5 clear categories. This chart shows that Mainland China has the most cases with 74%, and that the other countries are about equal, with Italy, South Korea, Iran, and all other coutntries having 6-7% each. — This pie chart only has 5 categories and gives the message much better — Mainland China has the vast majority of cases, and Italy, South Korea, Iran and all other countries split the remaining 26% almost equally.

This is the same data, but with the number of categories kept to 5. Now we can actually interpret the pie chart itself, without relying on the legend.

Honestly, 3 or 4 categories is ideal. Keep in mind that if some of your categories are really tiny, unless that is a fundamental part of your message, it is likely better to use another type of visualization.

Do: Include the percentages and labels inside or next to your pie chart

Second, include your percentages and your data labels IN the pie chart. Ditch the legend. A good rule of thumb: if, in the end, you need a legend to interpret your pie chart, consider using another form of data visualization.

Do: Let the title tell the story

This is a point I try to make with every graph, and it’s so important. When you make a data visualization, you are telling your reader a (hopefully compelling) visual story. You are editing what you show to make a point. Why not explain the point so that readers don’t have to figure it out (or misinterpret the findings?)

The Vast Majority of COVID-19 Cases in the US are from an Unknown Origin

Exploding pie chart showing that the vast majority of COVID-19 infections are from an unknown source (89%). — Source: CDC, March 18, 2020

The pie chart above is pretty clear on its story, but having the title spells it out for the reader. Sometimes the mere act of typing the title will also help you to make your graph more clear to make sure the message is consistent.

Do: Get creative

I used an exploding pie chart in the above visualization. Data visualization experts will tell you that you should never use exploding pie charts, but don’t listen to those spoilsports. I used one here because I tend to think of viruses and the immune system as a Pac Man-type system. I think it works. Anything you can do to keep your reader interested is always a good idea.

(By the way, it is absolutely maddening that as of time of publication, it’s almost impossible to get tested for the virus in Central Texas and other places in the U.S. unless you have been to a place with a high rate of outbreak or have had close contact with a confirmed case. As you can see, this practice makes no sense whatsoever.)

In conclusion: don’t be afraid of pie charts, but don’t make terrible pie charts either. With just a few easy rules of thumb, you can make great ones. Want to learn how? Contact me to enroll in my 2-hour in-person class, Telling Stories With Data , where I walk you through the principles of good graph design in EXCEL. Or take Ann Emery’s course when it’s available. It’s fabulous.