I’m taking a couple of undergraduate courses this semester, Basic Statistics and Experimental Psychology. In Statistics, we’re learning a statistical programming language called R. I’ve been interested in learning R for a while, and I was happy to see it on the syllabus.
Two weeks in, it turns out R is hard.
Yes, Captain Obvious: R is hard
R has a notoriously steep learning curve, and it’s especially challenging if you’re new to programming. I’m comforted by that. But the real problem here isn’t R.
As I work through R exercises on a learn-to-code site called DataCamp, I’m struck by how often I don’t know what to do next. I stare alternately at the exercise’s instructions and the flawed code I’ve just written, trying to understand why my code won’t produce the desired output. I’m frustrated with myself and with R’s persnicketyness. “Look, R,” I seethe, “you know what I’m trying to do here. Can you just help me out and do it?” But that’s not how programming works, of course—computers can only do exactly what you tell them to do.
My struggles with R should come as no surprise: R is vast and complex, and I’m not yet an experienced coder or a skilled statistician. That’s a recipe for befuddlement. But the problem isn’t R—it’s me.
I’ve gone intellectually soft. Seven years out of grad school, I’ve forgotten what it feels like to be befuddled.
Befuddlement is a good thing
Befuddlement is a healthy part of the learning process. When students approach a problem and don’t know how to do it, they often decide they’re no good at the subject. Brighter students, in particular, can have difficulty in this way—their breezing through high school leaves them no reason to think that being confused is normal and necessary. But the learning process is all about working your way out of confusion.
— Kenneth R. Leopold, Distinguished Teaching Professor, Department of Chemistry, University of Minnesota
The above quote is taken from Barbara Oakley’s A Mind For Numbers: How to Excel at Math and Science (Even If You Flunked Algebra). A professor of engineering, Oakley distills what’s so hard about learning new and challenging things, especially in math and science: you must be comfortable with long periods of not knowing what you’re doing. I’ve written about a similar idea—the Gap between where you are and where you want to be—but now I’m getting to live in the Gap for a while. It’s rough.
But it’s also exhilarating. When I do find the error in my code, when I revisit an earlier concept I didn’t fully grasp and find I now understand it easily, I get to experience the thrill of progress. I’m smarter today than I was yesterday, and I can point to exactly where the learning occurred.
That’s a great feeling, and it’s worth braving befuddlement.