Well, truth be told, I’ve not had a summer vacation just yet. Still, things do seem to slow down a little bit at work during the summer months and I’ve taken advantage of the time to learn a few new things that will hopefully make me better in my job. I thought I’d share some of them, along with resources in case you wish to add some arrows to your quiver, too.
One of the biggest challenges that I face as an evaluator is being able to quickly (and often on the fly) answer questions about the different programs and projects of the UMCCTS. I struggle with rarely getting the same question twice – or at least my ability, yet, to hear the same question twice – and too often find myself scrambling to gather data from different sources, analyze it, and present it back to a particular stakeholder “by the end of the day.” Granted, I was certainly used to giving quick answers to questions from patrons when I worked in the library, but I had a couple of advantages there; (1) I’d worked for a number of years as a medical librarian, so I was pretty up to speed on the library’s resources and (2) the library was a nice, neat, set container of resources as opposed to any number of individuals and project leads and program directors and data gatherers spread across the campus. Praise be the library! It’s difficult to overstate the value of organization. But I digress…
My challenge now is to make my own library, to build my own collection of resources, and to keep them current so that those stressful “by the end of the day” requests are less so. Enter spreadsheets, pivot tables, and dashboards. I was hardly a novice Excel user when I started this work, but enough reading in the literature and best practices of evaluation led me to believe that I needed to expand my know-how about Excel in order to make things easier for myself. After my last scramble to fulfill a “just in time” request, I decided to get to it. I read two excellent books on data visualization that base most of their material on examples from Excel; Cole Nussbaumer Knaflic’s, Storytelling with Data, and Stephanie Evergreen’s, Effective Data Visualization. These are both great, hands-on books to get you going. I also came across Excel Campus, with one of the best series of video tutorials I’ve ever viewed. The 3-part series on building pivot tables and dashboards was just what I needed.
With these new skills, I’m able to take lengthy, unwieldy (to me) spreadsheets and turn them into several separate sheets with associated pivot tables for analysis and interactive dashboards that let me quickly see the who, what, when, and where of our different programs. It’s a work in progress, but I can tell already that it will be helpful for me and – hopefully – when I develop more tables based upon the questions of the Center’s staff, it will be helpful for them, too.
Next up, I wanted to learn how to create both an overlapping bar chart and a heat map. I was inspired to learn the former from a blog post that I read, coupled with the task I had of writing a report summarizing the evaluation results of our annual research retreat. You know, when you create a survey to evaluate an event (a class, a retreat, a workshop, etc.), you’re often stuck with a whole bunch of questions producing a whole bunch of bar graphs showing how much people appreciated this, that, or the other thing about the event. My survey for the retreat was no different, but I knew that there had to be a better way to present the findings – “better,” meaning a one-page document. Overlapping bar charts seemed perfect. As you can see, I was able to use this type of chart to combine the results of several questions into one visual, making things a lot easier to read and a lot shorter in format.
Now the heat map. Why? Oh, I don’t know. It was last Friday and a quiet day. And they’re kind of cool looking, so … back to tackling R for analysis and visualizations. (My goal here is to be able to be comfortable with these tasks in Excel, R, and Tableau, thus I switch off between them, to hone some skills.) I’ve mentioned here before that I find Nathan Yau’s books and website, Flowing Data, to be essential to understanding and doing data visualization. To learn (better said, “follow the instructions”) to make a heat map, I used the example that he offers in his book, Visualize This, but he also makes this particular exercise available in his collection of online tutorials, so you can have at it, too, if you wish. As you can see, I did indeed follow the instructions and made a nice little heat map of NBA players’ stats.
I also wanted to try making a heat map in Excel (easier said than done, though you can find resources online). I downloaded the data from my Jawbone fitness band that I’ve been wearing since December and made a nice map of my daily step count. Nothing fancy, but it worked just fine as a learning exercise.
I still plan to tackle making heat maps in Tableau, as well as other dashboards and charts that will be useful. The tool kit is never full and the summer isn’t even half over yet.