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.
Course information
·¡°ä°Õ³§: 2,5&²Ô²ú²õ±è;
Number of sessions: 4
Hours per session: 3
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 for: Data analysis with R
Session 1: Friday March 7, 13.30-16.30. Online via Microsoft Teams.
Session 2: Friday March 14, 13.30-16.30. Online via Microsoft Teams.
Session 3: Friday March 21, 13.30-16.30. Online via Microsoft Teams.
Session 4: Friday March 28, 13.30-16.30. Online via Microsoft 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 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 ÌÇÐÄÖ±²¥. 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 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