Preparing to Use R
In case you are taking one of my courses where I make use of the statistical software package R, and you have no prior experience with R, it would be useful to familiarize yourself with R a bit ahead of time (of course, this doesn't apply to the Introduction to R Course, since the whole point of this course is to teach R in the first place). A place to start would be the manuals that come with R: https://cran.r-project.org/manuals.html
In particular, you could start with An Introduction to R. Some find this document easy to go through, others may find it less accessible. In my opinion, it might not be the best place to start, because it covers some technical topics that are not so relevant for new users and is more geared towards those who have a programming background. So instead, or in addition, you could take a look at the following resources (in no particular order) which I've heard good things about:
You can also take a look at the Big Book of R for an overview of over 300 books on R (many of which are freely available!) in case you are looking for something specifically tailored to your interests.
The materials from my Open Online Introduction to R Course are also freely available.
Note that you don't need to be an R expert to follow the courses that I teach. I explain all of the R commands that we need for the analyses and do my best to arrange things so that the actual use of R is as minimal as possible (e.g., unless it would be instructional, I do things like data preprocessing ahead of time). But again, it certainly helps if you have seen some R syntax before and understand the basic principles of how R works (dealing with unfamiliar software plus the actual contents of the course is simply more challenging than just dealing with the actual contents).
Unless you already have a different setup, it would also be useful to install an integrated development environment (IDE) for R. What this usually does is provide you with a nice code editor (with features such as syntax highlighting, bracket matching, and code completion), shortcuts for code execution, project management features, and an organized workspace that can make the use of R more user-friendly. A popular choice these days is RStudio, which is available (for free) for Windows, macOS, and Linux.