Have you heard of #MakeoverMonday or #WorkoutWednesday? I’m sure many of you have, but if not, they are two of the many free and publicly available challenges offered by the Data Visualization community.
Some challenges are offered weekly while others are monthly. Some focus on creation of new visualizations while others focus on challenging you to recreate existing ones – with the goal of teaching new visualization techniques.
For me, there are three main reasons why I have become a regular participant in these challenges over the last year and a half:
As with any art form, to get better at data visualization you need to practice. The more that you create, the more scenarios that you encounter, the more functionality that you’ll be able to understand and use, and the better your skills will become. You can never stop learning or get enough practice with data visualization. Even Tableau Zen Masters are constantly learning and working to expand their knowledge.
While it’s easy for me to set goals for myself, if I don’t have a means of staying accountable to those goals, I have a much harder time of staying on track. Creating these challenge visualizations, publishing them to Tableau Public, and then posting them on social media has been a great way for me to stay accountable to my progress.
3. Building a Portfolio
Along with accountability, as I continue to publish my work on Tableau Public, I’ve amassed a nice portfolio of non-job related data visualizations. This has been especially useful when I’ve applied for new positions in the past. You usually cannot show potential employers visualizations you have done at work, but if you have worked on things on your own time, and have them published on Tableau Public, you have a portfolio of your work in reverse chronological order.
My Creative Process
I just recently participated in #ProjectHealthViz, a monthly challenge by Lindsay Betzendahl focused on publicly available health data. Health data is fun to visualize as there are many different stories to be found within the data and many different ways of presenting them.
This month’s dataset was from the CDC and focused on disease outbreaks across the US. The data was in an excel file with over 400 thousand rows. Each row contained information on a single outbreak.
Let’s walk through how I got from the raw data to my final viz.
Process, Not Rules
While each challenge is different, when I’m creating something new there is a general process I follow. By no means are any of the following points meant to be seen as rules (very few of those exist in data visualization). Rather my intent in sharing these guidelines is to provide a spark to those who are just getting started with these data viz challenges and provide a possible structure for doing so.
Reviewing Data Structure
The first thing I do when presented with a new dataset is to look at which data fields are available. In this data from the CDC, there were:
- date fields for when an outbreak occurred
- state location information
- an Etiology field that listed the name of the disease
- some descriptor fields (like mode of transmission and outbreak setting)
- several measures including the number of illnesses, hospitalizations, and deaths.
When reviewing the structure of the data, you want to get a general understanding of what your main dimensions and measures will be for your visualization.
Exploration and Choosing a Topic
This CDC dataset is a little on the heavier side as far as content goes but it’s rich with stories to be told. To decide which story I wanted to tell, I needed to explore the data. While I could have done this in Excel, I like to bring the data into Tableau to start exploring. I start by reviewing overall statistics with the goal of finding a topic that stands out in the data.
For this dataset, that included the following:
Number of Outbreaks Per Year
Total Outbreaks by Location
Number of Outbreaks by Disease Type
It was clear from my exploration that Norovirus had the most outbreaks of all the diseases in the dataset. I decided at this point to use Norovirus as the focus for my viz. While I don’t always create visualizations based on the highest or lowest numbers in a dataset, once I have found a good topic I like to start creating my viz in Tableau.
Although you can and should take as long as you’d like when creating your own visualizations, I try to keep some structure around the amount of time I spend on any single viz. Some take longer than others, but I have a family and friends that I like to spend time with so I make sure my viz time does not take away too much from my personal time.
Telling a Story
Sometimes you have a dataset and you know exactly the story you want to tell right away. In those cases, you may start by creating the overall design of your dashboard and filling it in with your data later.
I tend to start with the data before I create a dashboard. I create several different charts, and iterations on those charts, before I put them all together. For the #ProjectHealthViz challenge, I created several different views. However, not all of them made the final version of my dashboard.
It’s up to you how many charts you want to create before combining them into a dashboard, but don’t feel like everything you create needs to be included. Sometimes your best ideas don’t come till after several iterations or a few times stepping away from your computer and coming back to it. More importantly, even if you really like one of the several charts that you have created, if it doesn’t fit with your overall story, you do not need to include it in your final viz.
Putting It All Together
Once I have a few different views to choose from, I start putting them together. My dashboards usually are not the same shape or size, and I often resize my dashboards as I’m working on them.
The one actual rule I stick to though is using fixed dashboards. You never know what size screen your viewers will have and if you do not fix the size of your dashboard, it may stretch or shrink in ways that make it unreadable.
Alignment and Chart Selection
There are many different ways to layout your dashboard. I often like to make a rough sketch with a pencil and paper of what I want my dashboard to look like. This helps me visualize what I want to do before I start creating in Tableau.
For my ProjectHealthViz I decided that I wanted to make it look like a long and narrow infographic – something where the story builds as you go from top to bottom. While I had several charts to choose from, I decided on these three to present the data:
- Bar Chart
- Hex Map
- BANs (Big Ass Numbers)
I’ve used bar charts and BANs many times in the past. They’re both great ways to highlight simple information. The bar chart I created clearly shows that Norovirus is the top cause of outbreaks in the US. The BANs at the bottom deliver raw numbers for how many have been affected by Norovirus.
As for the hex map, while I could have used a traditional map instead, there are two reasons I chose the hexagon style.
- It makes it easier to see the number of outbreaks per state when each state is the same size.
- I’ve always wanted to create a hexagon map and hadn’t done so before.
While some charts are more effective than others for different scenarios, sometimes stretching your ability by trying something new is fun too. And data viz should be fun!
Font and Color
I’m no expert on fonts. However, Tableau Public has a limited number of fonts it can render. Since the options are limited, it’s not too difficult to choose a font.
Jennifer VonHagel put together this great resource on which fonts are supported by Tableau Public and how they look:
I personally like the look of the Georgia font but sometimes stick with Tableau Regular (and use Tableau Bold or Tableau Semibold for bold characters). For my ProjectHealthViz, I stuck with Tableau Regular and Tableau Semibold. The bold function does not work well with Tableau Regular so I used Tableau Semibold for making certain text and numbers stand out.
When it comes to color selection, I try not to be too flashy. You don’t want extra color taking away from what you are presenting with your data. You also don’t want to use color that doesn’t have meaning. Color can be a great asset in your viz, but it is also very easy to overuse. My recommendation is that less color (and a fewer number of overall colors) tends to work better for visualizing data. In my viz, I used red to highlight the outbreaks while keeping everything else black or white.
Custom Fonts in Tableau
For my title at the top, to give it a little something more than the standard font look, I turned to PowerPoint. You can create a title in PowerPoint using any font available, save it as an image, and import it into your viz as an image. This way it looks like you’re using custom fonts, even though they’re actually image files.
At the end of last year, Zen Master Ken Flerlage tweeted about a tool called wordmark.it that lets you type in a word or phrase and see how it looks in all of the fonts installed on your machine.
I’ve used wordmark.it on several of my visualizations. It’s an easy way to find a font for a custom title, especially if you do not want to go through each font in PowerPoint one at a time.
The footer is where I place information to tag my viz as my own. I put the name of the challenge I’m participating in and my Twitter handle.
This is also where two very important pieces of information should go:
- Link To Your Datasource: If the data is publicly available, make sure to cite where it came from.
- Attribution: If you were inspired by someone else, or took a similar approach to a visualization as someone else, make sure to note that in the footer. Attribution in data visualization is as important as attribution in a book or research paper.
Be Deliberate About Difference
Once your viz is done, make sure to review it. Check for consistency in both your font usage and your colors. If you decide to make something a different font or color or shape than other items in your viz, do it intentionally and know that you did it. Do not do it because you overlooked it. Be deliberate about difference in your viz.
Check your tooltips too! They’re often overlooked and can be a useful feature for including additional information.
Pro tip: It never hurts to have someone else review your viz. The Data Viz community can be a great resource for this, but sometimes it helps to have someone from outside the field review it as well. If they have trouble interpreting your viz or understanding your data, you may need to make it clearer.
Sometimes getting started can be the hardest part. Regardless of the process you use, the most important part of getting started with these challenges is to create something. Import your data into Tableau and start making charts.
I’d say the second most important part is sharing your finished viz online. For me, sharing my work online has been the best way to stay accountable to my progress and to elicit feedback from the community.
Here’s a list of challenges that I have had the pleasure of participating in (in no particular order):
- MakeoverMonday: http://www.makeovermonday.co.uk/
- WorkoutWednesday: http://www.workout-wednesday.com/
- SWDchallenge: http://www.storytellingwithdata.com/swdchallenge/
- ProjectHealthViz: https://data.world/zendoll27
- DataForACause: https://www.olgatsubiks.com/data-for-a-cause
- VizForSocialGood: https://www.vizforsocialgood.com/
I challenge you all to participate in more challenges. And if you ever would like any feedback on your visualizations, I’m more than happy to help!