## 2.1 Getting RStudio to run on your computer

As a prerequisite for this guide, you need to have RStudio and a few essential R packages installed.

2. If you do not have R installed yet, you will have to install the latest R Version before you can use RStudio. You can get the latest R version here.
3. Once RStudio is running, open the Console on the bottom left corner of your screen.
4. We will now install a few packages using R Code. Here’s an overview of the packages, and why we need them:
Package Description
tidyverse This is a large package containing various functions to tidy up your data
meta This package is a so-called wrapper for many meta-analytic functions and makes it easy to code different meta-analyses
metafor This package is used by the meta package for many applications, so we need to have it installed

5. To install these packages, we use the install.packages() function in R. One package after another, our code should look like this:

install.packages("tidyverse")
install.packages("meta")
install.packages("metafor")

Don’t forget to put the packages in “”.

Otherwise, you will get an error message.

You are now set and ready to proceed. Below, you can find some basic information on RStudio and troubleshooting

### 2.1.1 Running R Code

Order to get the most out of this guide, it’s helpful (but not essential) if you have some programming experience already. If you’ve never programmed before, you might find Hands-On Programming with R (Grolemund 2014) to be a useful primer.

There are three things you need to run the code: R, RStudio, and collection of R packages. Packages are the fundamental units of reproducible R code. They include reusable functions, the documentation that describes how to use them, and sample data.

Gladly, once you’ve reached this point successfully, these three things are set already. Nevertheless, we will have to install and load a few new packages at some place in this guide, for which you can use the install.packages() the same way as you did before.

Throughout the guide, a consistent set of conventions is used to refer to code:

• Functions are in a code font and followed by parentheses, like sum() or mean().
• Other R objects (like data or function arguments) are in a code font, without parentheses, like seTE or method.tau.
• Sometimes, we’ll use the package name followed by two colons, like meta::metagen(). This is also valid R code. This is used so as to not confuse the functions of different packages with the same name.

### 2.1.2 Getting Help

As you start to apply the techniques described in this guide to your data you will soon find questions that the guide does not answer. This section describes a few tips on how to get help.

• If you get stuck, start with Google. Typically, adding “R” to a search is enough to restrict it to relevant results: if the search isn’t useful, it often means that there aren’t any R-specific results available. Google is particularly useful for error messages. If you get an error message and you have no idea what it means, try googling it. Chances are that someone else has been confused by it in the past, and there will be help somewhere on the web. (If the error message isn’t in English, run Sys.setenv(LANGUAGE = "en") and re-run the code; you’re more likely to find help for English error messages.)
• If Google doesn’t help, try stackoverflow. Start by spending a little time searching for an existing answer; including [R] restricts your search to questions and answers that use R.
• Lastly, if you stumble upon an error (or typos!) in this guide’s text or R syntax, feel free to contact Mathias Harrer at mathias.harrer@fau.de.

### References

Grolemund, Garrett. 2014. Hands-on Programming with R: Write Your Own Functions and Simulations. O’Reilly.