prep_for_r

In case you are taking one of my courses where I make use of the statistical software R – and you are not already somewhat familiar with R to begin with – it would certainly 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 point of this course is to teach R in the first place). A place to start would be the manuals that come with R:

http://cran.r-project.org/manuals.html

In particular, you could start with reading *An Introduction to R*. Some find this document easy to go through, others may find it less accessible. Instead, or in addition, you could take a look at the contributed documentation that is available for R:

http://cran.r-project.org/other-docs.html

I cannot say anything about these documents in particular, but I am sure that some of them are of high enough quality to be printed and sold without question.

There has also been an explosion of books on R in recent years, many of which are specifically geared towards those who want to learn using R in general:

http://www.r-project.org/doc/bib/R-books.html

And that list is far from complete. Just search for R books on Amazon (or some other bookseller of your choice) and you are going to be inundated with options and choices. Again, I'll refrain from providing any personal recommendations, as I have only read a few of those books myself.

Also, you don't need to be an R expert to follow the courses that I teach. I explain the R commands that we need to use 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.

If you are starting to work with R for the first time, it may be useful to also 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, brace 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 also available for Windows, MacOS, and Linux.

Finally, for the ESM Data Analysis Course, you do not have to use R in the first place. As explained here, I will use R in the lectures and will use it for illustrating the analyses, but I will also make scripts/datasets available for Stata and SPSS (in case you are more familiar with one of those packages). However, since I do use R myself in the lectures, it may in fact be easier to follow the course if you also stick to R and therefore do not have to switch back and forth between different software packages (the way results are presented differs slightly between different software). But this also depends a bit on how comfortable you are with adapting to a new statistical software package.

Also, sooner or later, everybody should switch to R anyway (or at least, be familiar with it), so you might as well get started now. Oh, and I also teach an entire Introduction to R Course, so if you really want to be thorough with your preparations, you could consider taking this course first.

prep_for_r.txt · Last modified: 2017/08/08 21:47 by Wolfgang Viechtbauer

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