3.5 So many packages!
At this point, you might want to ask me: How did you know about these packages? How did you know what functions to use, and how they work? And, as you learn about different packages, how do you remember which ones to use when?
All good questions (and ones I’m often asked at workshops). I try to keep up with package developments by looking at top tweets with the #rstats Twitter hashtag, scanning a number of R blogs, and following posts on the Google Plus R group. This may be easier for me than a lot of other R users because 1) I cover developments in data analysis for my day job as a tech journalist, and 2) I’m somewhat R-obsessed. If you don’t follow R developments for your job, a good shortcut to keep up with the latest R developments is the community-sourced R Weekly, which tries to round up the most interesting R news in a fairly easy-to-scan post at rweekly.org. You can also follow my tweets with the #rstats hashtag from my @sharon000 account.
When I learn about a very useful package, I add it to an interactive, searchable table published by Computerworld, which is available at http://bit.ly/Rpackages. You might want to keep your own spreadsheet of favorite packages and functions, whether or not it’s published for others to read. I’d suggest keeping it somewhere in the cloud even if it’s not public, like in a Google sheet or Excel spreadsheet on OneDrive, so you can access it from different computers.
There’s another way to store some of your favorite functions right in RStudio, called code snippets. I’ll be covering them in Chapter 6.
After you discover a package, reading the introductory vignette can help you figure out how to use it. Also, even if a package is on CRAN, the code may be on GitHub as well, and package authors will often add useful information there. In fact, there’s an extremely helpful tutorial on the dygraph package at https://rstudio.github.io/dygraphs/. If you Google “R” and a package name you may come across other useful content (when I wrote this, the RStudio dygraphs tutorial on GitHub came up first when Googling R dygraphs).