Motivation:
Background: At the moment, I am, very happily, a postdoc in ecology. But it wasn’t always the case that I knew what I wanted to be when I grew up. My freshman year in college, I was really eager to dive into organic chemistry. I couldn’t wait to become a chemist, or maybe a chemical engineer. My high school chemistry teacher had talked it up to the point where I couldn’t wait to start diagramming molecules. Turns out, organic chemistry can be really difficult. I worked harder for that class than I ever had before in my life, and just barely squeaked through. It was a lesson in humility, for sure, and also a bit of ‘be careful what you wish for’. The following semester, I was fortunate to take a field course at the university’s research forest: we’d go out one weekend a month and spend two days learning and living in a research forest. We got to meet forest ecologists, do some tree rings studies, and learn to identify species. I was hooked. I should have known I would enjoy being around people who spent their days thinking about how forests worked and living among the trees (and not synthesizing molecules from precursors of 3 carbons or less in a fume hood). Sometimes the thing we think we want obscures what we should have recognized we actually wanted all along. (this applies of course to many things in life)
Inspiration:
During college, I worked for several summers as an intern with the US Forest Service. It was a great experience: I learned how to run sampling transects, measure tree DBH (diameter at breast height), and analyze community ecology datasets. One of the important statistical methods used by ecologists to visualize community similarity patterns is called ordination. While I was at the Forest Service, we had access to a commercial software package, called PC-ORD, that ran these analyses. I learned to use it, and all was good. When I got to grad school though, I no longer had access to that software, and as a grad student, didn’t have the several hundred dollars it would have taken to get myself a license. Since it’s a relatively specialized technique, it wasn’t as if I could do it in Excel. I had heard from friends about the R statistical programming language, but hadn’t spent much time with it myself by that point. Some Internet research suggested that it would be possible to do the ordinations I needed to do in R. Since R was (as is) free and open source, I could download it and start fiddling with it immediately. It took me a while to get the hang of optimizing the figures and getting everything else working right, but soon enough, I was hooked. The power and freedom that come with working with open and freely available software certainly are counterbalanced by a bit of a learning curve, but the ability to do the types of advanced analyses that could only otherwise be done after shelling out hundreds or thousands of dollars for commercial software, certainly seems to make that investment of time worth it to me.