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Doing_Meta_Analysis_in_R.tex
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\title{Doing Meta-Analysis in R and exploring heterogeneity using metaforest}
\pretitle{\vspace{\droptitle}\centering\huge}
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\subtitle{A Hands-on Guide}
\author{Caspar van Lissa¹}
\preauthor{\centering\large\emph}
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\date{¹Utrecht University}
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{
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}
\hypertarget{about-this-guide}{%
\chapter{About this Guide}\label{about-this-guide}}
\includegraphics{coverbild.jpg}
\begin{rmdinfo}
This guide shows you how to conduct Meta-Analyses in R from scratch. The
focus of this guide is primarily on clinical outcome research in
psychology. It is designed for staff and collaborators of the
\href{https://www.protectlab.org}{\textbf{PROTECT Lab}}, which is headed
by \textbf{Dr.~David D. Ebert}.
\end{rmdinfo}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\textbf{The guide will show you how to:}
\begin{itemize}
\tightlist
\item
Get \textbf{R} and \textbf{RStudio} set for your Meta-Analysis
\item
Get your data into R
\item
\textbf{Prepare your data} for the meta-analysis
\item
Perform \textbf{fixed-effect} and \textbf{random-effects} meta-analysis using the \texttt{meta} and \texttt{metafor}packages
\item
Analyse the \textbf{heterogeneity} of your results
\item
Tackle heterogeneity using \textbf{subgroup analyses} and \textbf{meta-regression}
\item
Check if \textbf{selective outcome reporting (publication bias)} is a present in your data
\item
Control for selective outcome reporting and publication bias
\item
Analyse the \textbf{risk of bias} in your data
\item
Do advanced types of meta-analyses, such as
\begin{itemize}
\tightlist
\item
\textbf{network analyses} or
\item
meta-analyses with \textbf{more than one outcome}
\end{itemize}
\end{itemize}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\textbf{What this guide will not cover}
Although this guide will provide some information on the statistics behind meta-analysis, it will not give you an \textbf{in-depth introduction} into how meta-analyses are calculated statistically.
It is also beyond the scope of this guide to advise in detail which meta-analytical strategy is suited best in which contexts, and on how the search, study inclusion and reporting of meta-analyses should be conducted. The \href{http://handbook-5-1.cochrane.org/}{\emph{Cochrane Handbook for Systematic Reviews of Interventions}}, however, should be a great source to find more information on these topics.
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\textbf{Generally, there a two other sources to recommended when conducting Meta-Analyses:}
\begin{itemize}
\tightlist
\item
If you're looking for a easily digestable, hands-on introduction on how Meta-Analyses are conducted, we can recommend \textbf{Pim Cuijpers' online courses on Meta-Analysis}. The courses are freely available on YouTube. To have a look, click \href{https://www.youtube.com/watch?v=pP7_VBrG_TY\&list=PL-h5cI5Bkvt0J-O0kq_9J9_aksWFPgR7s}{here}.
\item
If you're interested in more details on how to conduct Meta-Analyses in R, you can either have a look at Wolfgang Viechtbauer's page for the \texttt{metafor} package (\href{http://metafor-project.org}{Link}). Or you can consult a book on the \texttt{meta} package which was recently published \citep{schwarzer2015meta}.
\end{itemize}
\begin{rmdinfo}
\textbf{How to get the R code for this guide}
\includegraphics{githublogo.png}
All code behind this book is available online on \textbf{GitHub}. We
have created a website containing a \textbf{download link} for all
codes, and a \textbf{quick guide} on how to get the code running on your
computer. The site can be found
\href{https://mathiasharrer.github.io/Doing-Meta-Analysis-in-R/}{here}.
\end{rmdinfo}
\textbf{How to cite this guide}
Harrer, M., Cuijpers, P. \& Ebert, D. D. (2019). Doing Meta-Analysis in R: A Hand-on Guide. \url{https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/}.
\href{https://zenodo.org/badge/latestdoi/152492192}{\includegraphics{https://zenodo.org/badge/152492192.svg}}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\textbf{To get started, proceed to the next chapter!}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{rstudio-basics}{%
\chapter{RStudio \& Basics}\label{rstudio-basics}}
\includegraphics{chap2.jpg}
\begin{rmdinfo}
Before we start with our meta-analysis, we have to download and prepare
a \textbf{computer program} which allows us to use \emph{R} for
programming.
Probably the best option for this at the moment is \textbf{RStudio}.
This program gives us a user interface which makes it easier to overlook
our data, packages and output. The best part is that RStudio is
\textbf{completely free} and can be downloaded anytime.
In this Chapter, we'll focus on how you can install RStudio on your
computer. We'll also provide some general information on R, and how you
can get help if you get error messages.
If you already have RStudio installed on your computer, and if you're an
experienced R user already, all of this might be nothing new for you.
You may \textbf{skip} this chapter then.
Especially if you have \textbf{never used R before, we would like to
consider this Chapter essential}, as it gives you some input on how R
works, and how we can use it for our data analyses.
\end{rmdinfo}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{RStudio}{%
\section{Getting RStudio to run on your computer}\label{RStudio}}
\includegraphics{Doing_Meta_Analysis_in_R_files/figure-latex/unnamed-chunk-6-1.pdf}
As a prerequisite for this guide, you need to have \textbf{RStudio} and a few essential \textbf{R packages} installed.
\textbf{You have to follow these steps to get your RStudio set.}
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
Download RStudio on the \textbf{RStudio} Website (\href{https://www.rstudio.com/products/rstudio/download/}{Link}). It's free!
\item
If you do not have \textbf{R} installed yet, you will have to install the latest R Version before you can use RStudio. You can get the latest R version \href{https://cran.r-project.org/bin/windows/base/}{here}.
\item
Once RStudio is running, open the \textbf{Console} on the bottom left corner of your screen.
\item
We will now install a few packages using R Code. Here's an overview of the packages, and why we need them:
\end{enumerate}
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
metaforest
This package is used to explore heterogeneity using the random forests algorithm. It also installs the R-package `metafor', which is one of the most commonly used meta-analysis packages.
5. To install these packages, we use the \texttt{install.packages()} function in R. One package after another, our code should look like this:
\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"tidyverse"}\NormalTok{)}
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"meta"}\NormalTok{)}
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"metaforest"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{rmdachtung}
Don't forget to put the packages in \texttt{""}.
Otherwise, you will get an error message.
\end{rmdachtung}
\textbf{Continue to the next chapter.}
\emph{Additional, non-essential information on RStudio and troubleshooting below.}
\hypertarget{running-r-code}{%
\subsection{Running R Code}\label{running-r-code}}
In 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 \textbf{\emph{Hands-On Programming with R}} \citep{grolemund2014hands} to be a useful primer.
There are three things you need to run the code: \textbf{R}, \textbf{RStudio}, and collection of \textbf{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 \texttt{install.packages()} the same way as you did before.
Throughout the guide, a consistent set of conventions is used to refer to code:
\begin{itemize}
\tightlist
\item
Functions are in a code font and followed by parentheses, like
\texttt{sum()} or \texttt{mean()}.
\item
Other R objects (like data or function arguments) are in a code
font, without parentheses, like \texttt{seTE} or \texttt{method.tau}.
\item
Sometimes, we'll use the package name followed by two colons, like
\texttt{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.
\end{itemize}
\hypertarget{getting-help}{%
\subsection{Getting Help}\label{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.
\begin{itemize}
\tightlist
\item
If you get stuck, start with \textbf{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 \texttt{Sys.setenv(LANGUAGE\ =\ "en")} and re-run the code; you're
more likely to find help for English error messages.)
\item
If Google doesn't help, try \href{https://stackoverflow.com}{stackoverflow}. Start by spending a little time searching for an existing answer; including {[}R{]} restricts your search to questions and answers that use R.
\item
Lastly, if you stumble upon an error (or typos!) in this guide's text or R syntax, feel free to contact \textbf{Caspar van Lissa} at \textbf{\href{mailto:c.j.vanlissa@uu.nl}{\nolinkurl{c.j.vanlissa@uu.nl}}}.
\end{itemize}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{getting-your-data-into-r}{%
\chapter{Getting your data into R}\label{getting-your-data-into-r}}
\includegraphics{chappool.jpg}
\begin{rmdinfo}
This chapter will tell you about how you can \textbf{import} your effect
size data in RStudio. We will also show you a few commands which make it
easier to \textbf{manipulate data} directly in R.
Data preparation can be tedious and exhausting at times, but it is the
backbone of all later steps when doing meta-analyses in R. We therefore
have to pay \textbf{close attention} to preparing the data correctly
before we can proceed.
\end{rmdinfo}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{data-preparation-in-excel}{%
\section{Data preparation in Excel}\label{data-preparation-in-excel}}
\hypertarget{excel_preparation}{%
\subsection{Setting the columns of the excel spreadsheet}\label{excel_preparation}}
To conduct Meta-Analyses in R, you need to have your study data prepared. For a standard meta-analysis, the following information is needed for every study.
\begin{itemize}
\tightlist
\item
The \textbf{names} of the individual studies, so that they can be easily identified later on. Usually, the first author and publication year of a study is used for this (e.g. ``Ebert et al., 2018'')
\item
The \textbf{Mean} of both the Intervention and the Control group at the same assessment point
\item
The \textbf{Standard Deviation} of both the Intervention and the Control group at the same assessment point
\item
The \textbf{number of participants (N)} in each group of the trial
\item
If you want to have a look at differences between various study subgroups later on, you also need a \textbf{subgroup code} for each study which signifies to which subgroup it belongs. For example, if a study was conducted in children, you might give it the subgroup code ``children''.
\end{itemize}
As per usual, such data is stored in \textbf{EXCEL spreadsheets}. We recommend to store your data there, because this makes it very easy to import data into RStudio.
However, it is very important how you \textbf{name the columns of your spreadsheet}. If you name the columns of your sheet adequately in EXCEL already, you can save a lot of time because your data doesn't have to be transformed in RStudio later on.
\textbf{Here is how you should name the data columns in your EXCEL spreadheet containing your Meta-Analysis data}
Column
Description
Author
This signifies the column for the study label (i.e., the first author)
Me
The Mean of the experimental/intervention group
Se
The Standard Deviation of the experimental/intervention group
Mc
The Mean of the control group
Sc
The Standard Deviation of the control group
Ne
The number of participants in the experimental/intervention group
Nc
The number of participats in the control group
Subgroup
This is the label for one of your Subgroup codes. It's not that important how you name it, so you can give it a more informative name (e.g.~population). In this column, each study should then be given an subgroup code, which should be exactly the same for each subgroup, including upper/lowercase letters. Of course, you can also include more than one subgroup column with different subgroup codings, but the column name has to be unique
Note that it \textbf{doesn't matter how these columns are ordered in your EXCEL spreadsheet}. They just have to be labeled correctly.
There's also no need to \textbf{format} the columns in any way. If you type the column name in the first line of you spreadsheet, R will automatically detect it as a column name.
\begin{rmdachtung}
It's also important to know that the import \textbf{will distort letters
like ä,ü,ö,á,é,ê, etc}. So be sure to transform them to ``normal''
letters before you proceed.
\end{rmdachtung}
\hypertarget{setting-the-columns-of-your-sheet-if-you-have-calculated-the-effect-sizes-of-each-study-already}{%
\subsection{Setting the columns of your sheet if you have calculated the effect sizes of each study already}\label{setting-the-columns-of-your-sheet-if-you-have-calculated-the-effect-sizes-of-each-study-already}}
If you have \textbf{already calculated the effect sizes for each study on your own}, for example using \emph{Comprehensive Meta-Analysis} or \emph{RevMan}, there's another way to prepare your data which makes things a little easier. In this case, you only have to include the following columns:
Column
Description
Author
This signifies the column for the study label (i.e., the first author)
TE
The calculated effect size of the study (either Cohen's d or Hedges' g, or some other form of effect size
seTE
The Standard Error (SE) of the calculated effect
Subgroup
This is the label for one of your Subgroup codes. It's not that important how you name it, so you can give it a more informative name (e.g.~population). In this column, each study should then be given an subgroup code, which should be exactly the same for each subgroup, including upper/lowercase letters. Of course, you can also include more than one subgroup column with different subgroup codings, but the column name has to be unique
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{import_excel}{%
\section{Importing the Spreadsheet into Rstudio}\label{import_excel}}
To get our data into R, we need to \textbf{save our data in a format and at a place where RStudio can open it}.
\hypertarget{saving-the-data-in-the-right-format}{%
\subsection{Saving the data in the right format}\label{saving-the-data-in-the-right-format}}
Generelly, finding the right format to import EXCEL can be tricky.
\begin{itemize}
\tightlist
\item
If you're using a PC or Mac, it is advised to save your EXCEL sheet as a \textbf{comma-separated-values (.csv) file}. This can be done by clicking on ``save as'' and then choosing (.csv) as the output format in EXCEL.
\item
With some PCs, RStudios might not be able to open such files, or the files might be distorted. In this case, you can also try to save the sheet as a normal \textbf{.xslx} EXCEL-file and try if this works.
\end{itemize}
\hypertarget{saving-the-data-in-your-working-directory}{%
\subsection{Saving the data in your working directory}\label{saving-the-data-in-your-working-directory}}
To function properly, you have to set a working directory for RStudio first. The working directory is a \textbf{folder on your computer from which RStudio can use data, and in which output it saved}.
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
Therefore, create a folder on your computer and give it a meaningful name (e.g. ``My Meta-Analysis'').
\item
Save your spreadsheet in the folder
\item
Set the folder as your working directory. This can be done in RStudio on the \textbf{bottom-right corner of your screen}. Under the tile \textbf{``Files''}, search for the folder on your computer, and open it.
\item
Once you've opened your folder, the file you just saved there should be in there.
\item
Now that you've opened the folder, click on the \textbf{little gear wheel on top of the pane}
\end{enumerate}
\includegraphics{Doing_Meta_Analysis_in_R_files/figure-latex/unnamed-chunk-14-1.pdf}
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{5}
\tightlist
\item
Then click on ``\textbf{Set as working directory}''
\end{enumerate}
\textbf{Your file, and the working directory, are now where they should be!}
\hypertarget{loading-the-data}{%
\subsection{Loading the data}\label{loading-the-data}}
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
To import the data, simply \textbf{click on the file} in the bottom-right pane. Then click on \textbf{import dataset\ldots{}}
\item
An \textbf{import assistant} should now pop up, which is also loading a preview of your data. This can be time-consuming sometimes, so you can skip this step if you want to, and klick straight on \textbf{``import''}
\end{enumerate}
\includegraphics{Doing_Meta_Analysis_in_R_files/figure-latex/unnamed-chunk-15-1.pdf}
As you can see, the on the top-right pane \textbf{Environment}, your file is now listed as a data set in your RStudio environment.
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{2}
\tightlist
\item
I also want to give my data a shorter name (``madata''). To rename it, i use the following code:
\end{enumerate}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{madata<-Meta_Analysis_Data}
\end{Highlighting}
\end{Shaded}
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{3}
\tightlist
\item
Now, let's have a look at the \textbf{structure of my data} using the \texttt{str()} function
\end{enumerate}
\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{str}\NormalTok{(madata)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## Classes 'tbl_df', 'tbl' and 'data.frame': 18 obs. of 17 variables:
## $ Author : chr "Call et al." "Cavanagh et al." "DanitzOrsillo" "de Vibe et al." ...
## $ TE : num 0.709 0.355 1.791 0.182 0.422 ...
## $ seTE : num 0.261 0.196 0.346 0.118 0.145 ...
## $ RoB : chr "low" "low" "high" "low" ...
## $ Control : chr "WLC" "WLC" "WLC" "no intervention" ...
## $ intervention duration: chr "short" "short" "short" "short" ...
## $ intervention type : chr "mindfulness" "mindfulness" "ACT" "mindfulness" ...
## $ population : chr "undergraduate students" "students" "undergraduate students" "undergraduate students" ...
## $ type of students : chr "psychology" "general" "general" "general" ...
## $ prevention type : chr "selective" "universal" "universal" "universal" ...
## $ gender : chr "female" "mixed" "mixed" "mixed" ...
## $ mode of delivery : chr "group" "online" "group" "group" ...
## $ ROB streng : chr "high" "low" "high" "low" ...
## $ ROB superstreng : chr "high" "high" "high" "low" ...
## $ compensation : chr "none" "none" "voucher/money" "voucher/money" ...
## $ instruments : chr "DASS" "PSS" "DASS" "other" ...
## $ guidance : chr "f2f" "self-guided" "f2f" "f2f" ...
\end{verbatim}
Although this output looks kind of messy, it's already very informative. It shows the structure of my data. In this case, i used data for which the effect sizes were already calculated. This is why the variables \textbf{TE} and \textbf{seTE} appear. I also see plenty of other variables, which correspond to the subgroups which were coded for this dataset.
\textbf{Here's a (shortened) table created for my data}
Author
TE
seTE
RoB
Control
intervention duration
intervention type
Call et al.
0.7091362
0.2608202
low
WLC
short
mindfulness
Cavanagh et al.
0.3548641
0.1963624
low
WLC
short
mindfulness
DanitzOrsillo
1.7911700
0.3455692
high
WLC
short
ACT
de Vibe et al.
0.1824552
0.1177874
low
no intervention
short
mindfulness
Frazier et al.
0.4218509
0.1448128
low
information only
short
PCI
Frogeli et al.
0.6300000
0.1960000
low
no intervention
short
ACT
Gallego et al.
0.7248838
0.2246641
high
no intervention
long
mindfulness
Hazlett-Stevens \& Oren
0.5286638
0.2104609
low
no intervention
long
mindfulness
Hintz et al.
0.2840000
0.1680000
low
information only
short
PCI
Kang et al.
1.2750682
0.3371997
low
no intervention
long
mindfulness
Kuhlmann et al.
0.1036082
0.1947275
low
no intervention
short
mindfulness
Lever Taylor et al.
0.3883906
0.2307689
low
WLC
long
mindfulness
Phang et al.
0.5407398
0.2443133
low
no intervention
short
mindfulness
Rasanen et al.
0.4261593
0.2579379
low
WLC
short
ACT
Ratanasiripong
0.5153969
0.3512737
high
no intervention
short
mindfulness
Shapiro et al.
1.4797260
0.3152817
low
WLC
long
mindfulness
SongLindquist
0.6125782
0.2266834
high
WLC
long
mindfulness
Warnecke et al.
0.6000000
0.2490000
low
information only
long
mindfulness
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{data-manipulation}{%
\section{Data manipulation}\label{data-manipulation}}
Now that we have the Meta-Analysis data in RStudio, let's do a \textbf{few manipulations with the data}. These functions might come in handy when were conducting analyses later on.
Going back to the output of the \texttt{str()} function, we see that this also gives us details on the type of column data we have stored in our data. There a different abbreviations signifying different types of data.
Abbreviation
Type
Description
num
Numerical
This is all data stored as numbers (e.g.~1.02)
chr
Character
This is all data stored as words
log
Logical
These are variables which are binary, meaning that they signify that a condition is either TRUE or FALSE
factor
Factor
Factors are stored as numbers, with each number signifying a different level of a variable. A possible factor of a variable might be 1 = low, 2 = medium, 3 = high
\hypertarget{convertfactors}{%
\subsection{Converting to factors}\label{convertfactors}}
Let's say we have the subgroup \textbf{Risk of Bias} (in which the Risk of Bias rating is coded), and want it to be a factor with two different levels: ``low'' and ``high''.
To do this, we need to the variable \texttt{ROB} to be a factor. However, this variable is currently stored as a character (\texttt{chr}). We can have a look at this variable by typing the name of our dataset, then adding the selector \texttt{\$} and then adding the variable we want to have a look at.
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{madata}\OperatorTok{$}\NormalTok{ROB}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## [1] "high" "low" "high" "low" "low" "low" "high" "low" "low" "low"
## [11] "high" "low" "low" "low" "high" "high" "high" "low"
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{str}\NormalTok{(madata}\OperatorTok{$}\NormalTok{ROB)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## chr [1:18] "high" "low" "high" "low" "low" "low" "high" "low" "low" ...
\end{verbatim}
We can see now that \texttt{ROB}is indeed a \textbf{character} type variable, which contains only two words: ``low'' and ``high''. We want to convert this to a \textbf{factor} variable now, which has only two levels, low and high. To do this, we use the \texttt{factor()} function.
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{madata}\OperatorTok{$}\NormalTok{ROB<-}\KeywordTok{factor}\NormalTok{(madata}\OperatorTok{$}\NormalTok{ROB)}
\NormalTok{madata}\OperatorTok{$}\NormalTok{ROB}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## [1] high low high low low low high low low low high low low low
## [15] high high high low
## Levels: high low
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{str}\NormalTok{(madata}\OperatorTok{$}\NormalTok{ROB)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## Factor w/ 2 levels "high","low": 1 2 1 2 2 2 1 2 2 2 ...
\end{verbatim}
We now see that the variable has been \textbf{converted to a factor with the levels ``high'' and ``low''}.
\hypertarget{converting-to-logicals}{%
\subsection{Converting to logicals}\label{converting-to-logicals}}
Now lets have a look at the \textbf{intervention type} subgroup variable. This variable is currently stores as a character \texttt{chr} too.
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{madata}\OperatorTok{$}\StringTok{`}\DataTypeTok{intervention type}\StringTok{`}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## [1] "mindfulness" "mindfulness" "ACT" "mindfulness" "PCI"
## [6] "ACT" "mindfulness" "mindfulness" "PCI" "mindfulness"
## [11] "mindfulness" "mindfulness" "mindfulness" "ACT" "mindfulness"
## [16] "mindfulness" "mindfulness" "mindfulness"
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{str}\NormalTok{(madata}\OperatorTok{$}\StringTok{`}\DataTypeTok{intervention type}\StringTok{`}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## chr [1:18] "mindfulness" "mindfulness" "ACT" "mindfulness" "PCI" ...
\end{verbatim}
Let's say we want a variable which only contains information if a study way a mindfulness intervention or not. A logical is very well suited for this. To convert the data to logical, we use the \texttt{as.logical} function. We will create a new variable containing this information called \texttt{intervention.type.logical}. To tell R what to count as \texttt{TRUE} and what as \texttt{FALSE}, we have to define the specific intervention type using the \texttt{==} command.
\begin{Shaded}
\begin{Highlighting}[]