Introduction to R
Aims
The open-source software environment R (www.R-project.org) is a powerful platform for data analysis and statistical graphics that has become the global standard in statistical computing. R houses an ever-growing extensive collection of tools for statistics and data analysis – an attractive basis for thorough exploration of your data. Furthermore, it provides a powerful programming language coupled with flexible graphical capabilities.
In this introductory course, you will learn the basics of working with data in R, such as getting data in and out of R, creating powerful visualizations, and data wrangling (recoding, transformations, aggregation, combining data sets, ...). In addition, you will learn how to create dynamic reports and presentations with R Markdown. If time permitting, it will also be illustrated how to apply some widely used statistical techniques.
The application of these techniques will be illustrated by practical R examples, and there will be time for students to gain practical experience with R by conducting analyses as exercises, with the lecturers being available for assistance. Upon completion of the course, you will be able to incorporate the power of R into your research.
Information
- An overview of R and the RStudio interface
- Getting data in and out of R
- Data visualization
- Data wrangling
- Exploring data with descriptive statistics and summaries
- Creating dynamic reports and presentations with R Markdown
Assessment
- Homework in the form of practical analyses in R
- Presentation and discussion of the homework in the form of an R Markdown report.
Materials
Course notes will be provided.
There are no prescribed books. The following book is recommended to interested students as further reading material:
- Wickham, H., & Grolemund, G. (2016). R for Data Science. O'Reilly
Additional info
The course will be taught in English and is limited to 25 participants. Students are required to bring their own laptops to class (R and RStudio can be installed for free).
The timetable for this course can be found in the EUR course guide.
ERIM PhD candidates and Research Master students can register for this course via Osiris Student.
External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to the ERIM Doctoral Office by four weeks prior to the start of the course. For external participants, the course fee is 260 euro per ECTS credit.
