Wolfgang Viechtbauer

Marginally significant (p = .07)

User Tools

Site Tools



Twitter Google+ Facebook GitHub Stack Exchange

The metafor Package

If you are looking for information about the metafor package for R, please go to the package website.

Google Scholar

A list of my publications and citation information/indices can be found on Google Scholar.

Cross Validated

Occasionally, I contribute to Cross Validated, a question and answer website for people interested in statistics, machine learning, data analysis, data mining, and data visualization (link to my profile).


Introduction to R Course

General Information

Course Dates to be announced
Course Location Maastricht, The Netherlands (see details below)
Registration Deadline to be announced
Course Fee see below

Course Description

R is a programming language and software environment for carrying out computations, manipulating and analyzing data, and creating various types of plots and graphics. R has become the 'lingua franca of statistics' and the software of choice for analyzing data in various disciplines. However, for many researchers, getting up and running with R remains a hurdle due to the command-driven nature of the software. The purpose of this course is to lay the necessary foundation for becoming a fully proficient R user.

We start with a bit of background history on the development of R and then cover basic data structures and functions for computations and data manipulation. Next, we describe how to import and export data files and then delve into commands for inspecting data and obtaining summary statistics. One of the strengths of R are its powerful graphing and plotting capabilities, which we cover next. A variety of statistical methods for analyzing continuous, categorical, and time-to-event data (e.g., linear and logistic regression, ANOVA, Cox models) will then be discussed and their implementation in R demonstrated (emphasis here is more on the general model syntax as used in R and less on the statistical details of the various procedures).

At first, the large number of functions and add-on packages available for R can be overwhelming for the new user. We therefore discuss not only commands for downloading and installing additional packages, but also strategies for finding the right tools. On the last day, we then cover programming structures and the generation of random data. In this context, we provide a gentle introduction to Monte Carlo simulations as a way of investigating the properties of statistical procedures. How to extend the capabilities of R with self-written functions will be described next. We then conclude the course with a selection of advanced and miscellaneous topics and pointers for becoming a self-sufficient R user.

The course is aimed at researchers, (Master and PhD level) students, data analysts/scientist, and essentially anybody interested in learning how to work with R.

While R can be used much more widely than 'just' as a statistical software and data analysis tool, statistical computing and graphing remain its primary function and we also mainly describe the use of R in this context. Therefore, some familiarity with basic statistical concepts and methods as used in the health, social, and natural sciences is helpful when following the course.

The course is introductory in the sense that no prior background or experience with R is assumed. So we really start from scratch, but cover quite a bit of ground and even get into some more advanced topics within those three days (by going through the contents at an appropriately swift but non-hurried pace).

It will generally be a bit easier to learn R if you have any kind of programming experience or have used other statistical software packages in a command-driven manner (instead of using a "point-and-click" approach). However, this is not a prerequisite and as long as you are as excited to learn R as we are teaching it, we'll do our best to accommodate any kind of background or experience level.

Also, the course consists of a mixture of lectures and hands-on exercises, so there are ample opportunities for practicing the concepts taught.

Course Instructors

The course is taught by Wolfgang Viechtbauer (Department of Psychiatry and Neuropsychology) and Jan Serroyen (Department of Methodology and Statistics), both at Maastricht University. We each have over a decade of experience working with R and use R daily as part of our work as statisticians. While we have repeatedly professed the advantages of R to colleagues and other researchers, we have noticed a general hesitancy to pick up a new (statistical) programming language, especially among those mostly used to point-and-click interfaces. Because we have found R to be such a powerful and flexible tool, we decided to pass on our experience to others who are interested in becoming R users and would like some help in getting started.

Course Schedule

Note: Despite the level of detail, this schedule is tentative. The starting and ending times of the course are definite, but everything in between is subject to change. Also, breaks are not explicitly indicated in the schedule below, but are planned in throughout the days.

Day 1
09:30-10:00 Introduction and Logistics
10:00-10:15 Lecture 1: R and its History
10:15-11:45 Lecture 2: Data Structures, Functions, Basic Arithmetic, Data Manipulation
11:45-12:15 Lecture 3: Interacting with and using R
12:15-13:15 Lunch
13:15-14:45 Exercise 1
14:45-15:30 Lecture 4: Data Import and Export
15:30-16:30 Exercise 2
16:30-17:00 Lecture 5: Data Inspection and Summary Statistics
17:00-18:00 Exercise 3
Day 2
09:00-10:00 Lecture 6: Graphs, Plots, and Tabulations
10:00-11:00 Exercise 4
11:00-12:00 Lecture 7: Statistical Methods: Continuous Data (Part 1)
12:00-13:00 Lunch
13:00-14:00 Lecture 7: Statistical Methods: Continuous Data (Part 2)
14:00-15:00 Exercise 5
15:00-16:30 Lecture 8: Statistical Methods: Categorical and Time-to-Event Data
16:30-17:30 Exercise 6
Day 3
09:00-10:00 Lecture 9: Finding, Installing, and Loading Additional Packages
10:00-11:00 Exercise 7
11:00-11:30 Lecture 10: Programming Structures and Flow Control
11:30-12:30 Exercise 8
12:30-13:30 Lunch
13:30-14:30 Lecture 11: Generic, Method, and User-Written Functions
14:30-16:00 Lecture 12: Advanced and Miscellaneous Topics
16:00-17:00 Q&A

What to Bring to the Course

Please bring a laptop to the course. Moreover, you should have the current version of R installed on the laptop. You can download R from the Comprehensive R Archive Network (CRAN) (if you use Windows, choose "Download R for Windows", then "base", and then download and run the setup program; versions for MacOS and Linux are also available).

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. We recommend RStudio, which is available for Windows, MacOS, and Linux.

Software to open/view PDF files should also be installed on the laptop (e.g., Adobe Reader or see Wikipedia for a comprehensive list of PDF software). In all likelihood, such software is already installed on your laptop anyway.

You can also pair up with another course participant for the computer exercises, so technically not everybody needs to bring a laptop. However, if at all possible, please bring your own laptop so we don't end up with the unfortunate situation that nobody brings one!

Course Fee

The course fee is 600 Euros and includes lunch on all three days, refreshments (e.g., coffee, tea, water) during the breaks, but not dinner or accommodations.

Course Location

The course will be held at Hotel van der Valk, Maastricht, the Netherlands.

Hotel Location

Nijverheidsweg 35
6227 AL Maastricht
The Netherlands
Google Maps Link

Arriving by Car

From the A2/E25, take exit N278. Turn left at the traffic lights in the direction "Cadier en Keer, Vaals". Then turn right into 1 Juliweg and again right into Nijverheidsweg. The parking lot of the hotel is immediately to your left.

Arriving by Train

If you arrive by train, you have a couple options. If you leave the train at the main station in Maastricht, then you can take bus number 4 in the direction "AZM (Academisch Ziekenhuis)" (the bus leaves twice every hour, at 17 and at 47 minutes past the hour). Take the bus to the Nijverheidsweg stop (takes about 8 minutes). The hotel is right down the road from the bus stop (100m).

Alternatively, if you leave the train at Randwyck train station, you can take a leisurely stroll as shown on the map below (approximately 10 minutes). You can also take bus number 4 in the direction "Villapark" and again exit at the Nijverheidsweg stop.

For a map corresponding to these instructions, click here.

Hotel Accommodations

If you need hotel accommodations, the most convenient place to stay will be at Hotel van der Valk, which is also the course location. In general, a useful website for hotel accommodations in and around Maastricht can be found here.

Miscellaneous Information

Instructional Language English
Min/Max Number of Participants 10/30
Certificate for Participation Yes
Number of European Credit Points 1
course_r.txt · Last modified: 2017/02/06 16:30 by Wolfgang Viechtbauer