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(This article was first published on Revolutions, and kindly contributed to R-bloggers)
by Joseph Rickert
It is always a delight to discover a new and useful R package, and it is especially nice when the discovery comes with at context and testimonial to its effectiveness. It is also satisfying to be able to check in once in awhile and get an idea of what people think is hot, or current or trending in the R world.The schedule for the upcoming useR! conference at Stanford is a touchstone for both of these purposes. It lists 128 contributed talks over 3 days. Vetted for both content and quality by the program committee, these talks represent a snapshot of topics and research areas that are of current interest to R developers and researchers and also catalog the packages and tools that support the research. As best as I can determine, the abstracts for the talks explicitly call out 154 unique packages.
Some of the talks are all about introducing new R packages that have been very recently released or are still under development on Github. Others, mention the packages that represent the current suite of tools for a particular research area. Obviously, the abstracts do not list all of the packages employed by the researchers in their work, but presumably they do mention the packages that the speakers think are most important or in describing their work and attracting an audience.
The following table maps packages to talks. There are multiple lines for packages when they map to more than one talk and vice versa. For me, browsing a list like this is akin to perusing a colleague's book shelves; noting old favorites and discovering the occasional new gem.
I am reticent to try any conclusions about the importance of the packages to the talks from the abstracts alone before the talks are given. However, it is interesting to note that all of the packages mentioned more than twice are from the RStudio suite. Moreover, shiny and rmarkdown are apparently still new and shiny. Mentioning them in an abstract still conveys some cachet. I suspect that ggplot2 will figure in more analyses than both of these packages put together, but ggplot2 has become so much a part of that fabric of R that there is no premium in promoting it.
If you are looking forward to useR! 2016, whether it attend in person or to watch the videos afterwards, gaining some familiarity with the R packages highlighted in the contributed talks is sure to enhance your experience.
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