I was recently reflecting on some of the ways that my research has shifted in focus over the past 10 years or so that I have been an independent (or semi-independent) researcher. It occurred to me that the story of my research can be perhaps most succinctly summarized through changes in the R packages that I’ve used most through time.
When I first started using R (~2006), almost all of my analyses revolved around the ‘VEGAN’ package. VEGAN is the ‘community ecology package’ and provides user-friendly ‘tools for descriptive community ecology. It has most basic functions of diversity analysis, community ordination and dissimilarity analysis.’ I used vegan to look at basic patterns of diversity and composition on the islands of Lago Guri and then in the large 50-ha forest dynamics plots run by the CTFS (with whom I was doing a postdoc). These analyses all focused around simple presence absence or abundance matrices and looked at core questions in community ecology.
From VEGAN, I transitioned to ‘SPLANCS’. SPLANCS is the package for ‘Spatial and Space-Time Point Pattern Analysis’. I had moved into the realm of spatial statistics and was looking at detailed spatial patterns in the CTFS databases. Eventually the CTFS developed its own package which included many functions built around VEGAN and SPLANCS.
Now, the most common package in my R codes is ‘RASTER’ which is the package for ‘Geographic data analysis and modeling’. The use of RASTER reflects my shifting focus on larger-scale biogeographic and macroecological questions. This shift in turn reflects the expanding nature of anthropogenic disturbances. In order to understand how we are screwing with the planet in big ways, we need to do big research.
Beyond just using different packages, there has also been a shift toward writing and developing more and more of my own personal handwritten code. When I was an R infant working with VEGAN, my analyses were built around the available functions. The questions I asked were constrained by what VEGAN could do for me. Now I build my own code around the questions I am interested in and use the functions available from VEGAN, SPLANCS, RASTER and any other package, just to flush out the sub routines. Now my analyses are built around the questions – not the other way around. So not only does my shifting use of R reflect the changing focus of my research, it also reflects changes in the very way I think and do research.
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