Introduction
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 specialised 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.
Key Facts & Figures
- Type
- Course
- Instruction language
- English
- Mode of instruction
- Online
What will you achieve?
- After completion of this course, you will be able to understand basic R functionality for reading and manipulating data sets.
- You will be able to explore data with descriptive statistics and graphics.
- You will be able to use R for more advanced analyses (such as, linear regression, mediation and moderation).
Start dates
This edition is fully booked!
Session 1
February 26 (Monday) 2024
13.30-16.30
Online (Teams)
Session 2
February 28 (Wednesday) 2024
13.30-16.30
Online (Teams)
Session 3
March 4 (Monday) 2024
13.30-16.30
Online (Teams)
Session 4
March 8 (Friday) 2024
13.30-16.30
Online (Teams)
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.
How to prepare
- Bring your laptop to all sessions
- Download and install RStudio
Installation instruction:
- 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
Session description
Understanding R
Using R for data analysis
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.Email address
Contact
- Enrolment-related questions: enrolment@egsh.eur.nl
- Course-related questions: gruber@ese.eur.nl
- Telephone: +31 (0)10 4082607
Facts & Figures
- Fee
- free for PhD candidates of the Graduate School
- € 575,- for non-members
- consult our enrolment policy for more information
- Tax
- Not applicable
- Offered by
- Erasmus Graduate School of Social Sciences and the Humanities
- Course type
- Course
- Instruction language
- English
- Mode of instruction
- Online
- External link
- Register here