I’m laid up with a sprained ankle the size of a tennis ball from messing up a foot switch on a rock climbing problem. So, trying to look on the bright side of things, this means I can write a blog entry I have put off for far too long.
I like digging into software. I’ve learned that if I think there should be a better way to do something, someone has already thought of that and done it. When I switched from using a Mac as my primary computer to using a PC, I had to adjust to the user-unfriendlyness of it.

The following pieces of software have helped me in my first year of graduate school. Oh and they’re all free.

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Having been thoroughly impressed by Edward Tufte’s first book, The Visual Display of Quantitative Information, I decided to pick up another, Visual Explanations. Tufte envisions his books fitting together like so: The Visual Display of Quantitative Information is about pictures of numbersEnvisioning Information is about pictures of nouns, and Visual Explanations is about pictures of verbs. I was not drawn in by Visual Explanations (VE) as I was by The Visual Display of Quantitative Information (VQ). VE felt disjointed, and the lessons learned are less applicable for graduate students. However, there are still two very important lessons I gleaned: be subtle and avoid legends.

In a nutshell, VE states that images should be honest and scientific. Images should lend themselves to easy comparison through similar composition and repetition. Lastly, images alone can make an argument or tell a story through juxtaposition and symbolism.

I want to highlight a couple of these lessons, starting with one that I initially thought was wrong.

Subtlety is better than garishness.

Tufte states, “Make all visual distinctions as subtle as possible, but still clear and effective.” I would think you would want to eschew subtly to make sure the point comes across. However, this can be far too overwhelming, as shown above.

I’ve seen this lack of subtlety in lab presentations where the presenter shows a chart with ten or more curves. Every curve is a different color and has different markers for the points. Either a different color or a different set of markers would suffice, but both is excessive. Adding insult to injury, the default Excel colors are garish and unsubtle. To fix my own graphics, I’ve taken to using shades of black and gray to differentiate between each curve.

That same chart had a legend. Imagine trying to look at the legend, then the curves, then the legend, then the curves, then the . . . I simply gave up. You might think I give up too easily or that I am nit picking, so I’ll let you experience the difference between a legend and direct labeling for yourself.

Which image is quickest to understand? Which do you finish reading?

In light of this striking difference, I always label my curves directly. To do so in Excel, do not use a text box. Use a data label. You can overwrite the label and move it anywhere in the graph, so it is just like a text box. However, unlike a text box the label will stay in the same relative place on the graph and rescale along with the graph.

Gerrymandering.

The most important message for me is the same message Tufte stressed in his previous book. Graphics should be honest and scientific. To make a graphic honest, there must be a sense of scale and orientation for the viewer. If time-averaging or area-averaging has been applied, it should be done carefully as it can easily obscure important trends. The graphic to the right illustrates how area-averaging may doomed the residents of Broad Street. To make a graphic scientific entails quantification, comparison, and investigation of cause-and-effect. Quantification means applying a scale and going further to assign numbers to seemingly qualitative data. Comparison means plotting similar graphs on the same scale so that when they are put side-by-side, a line falling an inch in one means the same thing in the other. Investigation of cause-and-effect is the most difficult, but it means always plotting the suspected cause on the X-axis and the effect on the Y-axis, rather than plotting both against time.

Tufte does a fantastic job illustrating his message through the Challenger explosion. This is definitely a favorite case-study of his as it shows up in at least three of his books. Here he shows the actual set of slides the engineers sent to NASA the night before trying to persuade them not to launch. Tufte dissects the slides to show why they failed to persuade. The slides omitted many critical data points, failed to quantify the extent of damage to the O-rings, and presented important comparisons with many slides in between. At the end of the dissection, he shows the graphic below.

The slides only discussed the two labeled data points. This shows the full trend and makes a case for cold being dangerous.

By creating a damage-index, Tufte quantifies the data that was previously only qualitative. By plotting this data against temperature, Tufte makes a case for cold temperatures causing damage. I think that if this graphic had been shown to NASA, the launch would have been postponed.

While not all design decisions involve life or death, design can make or break the viewer’s comprehension of your argument. Taking the time to make your graphs easy to read will lead to better questions from the audience. Better questions will push your research along more quickly. Or maybe good design will help a grant-reviewer understand your argument and see why it is significant. In any case, this is not a small matter. While content is definitely king, it must be presented in an intelligible manner for it to take over the kingdom of the viewer’s mind.

Starting Graduate School

August 28, 2011

The first week of classes are over, and I have already learned so much. Let me get my cynicism out of the way first. Chemical engineering graduate students are not business savvy. Most graduate students are not. Would you sign on to work with a company for five or maybe more years and not know your vacation hours? Or leave parts of your job description undefined? You would be very stupid to do so, and I should know as I have just done so. I’m regretting it right now. I skimmed the only few sheets of paper I signed, and no where is it spelled out what my job is. Thus, when my TA hours increased 50% from two classes to three classes, I have no recourse. I am simply at the mercy of the university.

When the graduate director broke the news to us, he was sure to impress upon us how much the university is spending on us. He wants us to realize that we are an investment on the part of the university, and so we should feel an obligation to do all of our work with the utmost diligence. However, I am still unclear what my work is. I will be taking three courses, and I will be making straight A’s. I will be taking and passing three qualifying exams come January (So yeah, good bye Christmas break!). I do not know when I will begin my research. This is frustrating, as the PhD is at its root a research degree.

In this first week, I have learned that boldness is rewarded. The single greatest lesson I learned from my brief stint editing Wikipedia is that their motto, “Be Bold!” is a great motto. Being bold in graduate school means introducing yourself and making friends with your fellow classmates. It means emailing the professors you are interested in and knocking on their lab doors. Sure, there will be missteps, but the end result will be getting to know if you mesh with a group. Being bold means—above all things—not to hesitate.

In this spirit, before classes started I knocked on four professors doors. Only one was in at the time, and he was not taking students this year. However, the conversation that followed was very helpful. He recommended reading “A PhD is Not Enough” by James Feibelman. It goes over all aspects of post-graduate education. I highly recommend it to anyone considering graduate school or wanting to understand what host of benefits and problems a graduate degree confers. For me, it cemented my desire not to work for an assistant professor.

Now that my cynicism has run its course, I must say that I am truly excited by what I will be learning. The courses I am taking are going to force me to come to grips with my weaknesses. In fact, I’ve already had to shore up some of my mathematical deficiencies. The research I will be involved in will force me to think critically and impart me with vision. I mean vision in the sense that leaders have a vision for a future. With this vision, I will see problems and their solutions. I will solve those problems, and the world will be a better place for it. How could someone not get excited at that prospect?