Data analysis with R
Methodology courses and philosophy of science
Number of sessions: 4
Hours per sessions: 3
- free for PhD candidates of the Graduate School
- € 525,- for non-members
- consult our enrolment policy for more information
- Enrolment-related questions: firstname.lastname@example.org
- Course-related questions: email@example.com
Telephone: +31 (0)10 4082607
In the academic year 2022-2023 this course will take place online.
February 27 2023
March 3 2023
March 6 2023
March 9 2023
R is probably the most widely used open-source software environment for data analysis and statistical graphics in academia and business. It contains a full-fledged programming language as well as thousands of add-on libraries offering specialized statistical capabilities. This combination of the power of programming with an extensive toolkit of statistical and graphical methods makes R perfect for thorough exploration of your data.
Aims and working method
The instructor will illustrate the application of R with practical examples. Participants will gain practical experience with R by conducting analyses on provided datasets or data from the participants’ PhD project.
After completion of this course, you will be able to:
- Understand basic R functionality for reading and manipulating data sets;
- Explore data with descriptive statistics and graphics, and
- to use R for more advanced analyses (such as, linear regression, mediation and moderation).
How to prepare
- Bring your laptop to all sessions
- Download and install RStudio
- Log in to your remote desktop and open the application catalog
- Search for 'RStudio'
- Download and install R 3.4.1 / RStudio 1.0.143
- Search for 'application catalog' and/or 'remote desktop' in myeur.nl for more information.
- Please do this well in advance and notify the course instructor if there are any problems
Using R for data analysis
About the instructor
Kathrin Gruber is assistant professor at the Department of Econometrics of Erasmus University Rotterdam. Her fields of expertise are quantitative marketing, psychometric methods and computational statistics. Her research mainly focuses on Bayesian as well as approximate methods for individual-level inference in large-scale problems. She obtained her PhD from Vienna University of Economics and Business, home to the comprehensive R Archive Network.