Workshops
Information about the Summer-School on Internet-Based-Data-Collection
Donnerstag 14.09.2023 - 9:00 - 16:30
Workshop I: Introduction to Bayes
Description:
This workshop covers the basics of Bayesian statistics. We start with an overview of the basic principles and concepts. After a solid understanding of the theory, participants will have the opportunity to apply their knowledge through hands-on experience with R. This workshop provides a comprehensive and interactive learning environment that offers both theoretical insights and practical skills.
Prior knowledge:
Basic training in (frequentist) statistics: e.g. from psychology studies.
Basic knowledge in R: installing packages, reading and manipulating data, regression analysis with lm()
Language:
The workshop language is German, the materials are written in English.
Workshop II: Programming in R
Descritpion:
In various scientific fields, R has become as a popular tool for data processing, analysis and visualization. Yet in addition to a software package for statistical modeling and data analysis, R is also a fully functioning programming language. Moreover, one of the main design principles of R is to "turn ideas into software, quickly and faithfully" (John Chambers). However, for many R users with a limited programming background, a large proportion of R’s potential remains untapped.
This workshop explores the possibilities and benefits of (also) using R as a programming language, and aims to provide experienced R users with more advanced programming skills. The workshop focuses on both tools that automate R-tasks, writing efficient code and helpful functions, as well as on obtaining a better understanding of how R works.
After the course, participants should be able to read, understand, and adapt code from others, write their own functions, and should be able to use the R more efficiently.
Content:
After setting up an RStudio environment that supports writing efficient and elegant R-code, the following topics will be explained and illustrated using examples and exercises:
- Good programming practices
- iteration, and the Split-Apply-Combine paradigm
- Writing efficient functions
- Debugging
- scoping
- (and more)
Required Knowledge and Experience:
Experience with R is required.
Participants should be able to:
- efficiently read and write data in R
- manipulate data with base R functions
- fit statistical models
- make visualizations of data
Participants should have basic knowledge about:
- data types in R (e.g., know the difference between a data.frame, an array, a list),
- programming in R (e.g., use "if" and "if else").
Language:
The workshop and workshop materials are in English.
Software Requirements:
R [>= 4.2.2],
RStudio [>= 2022.12.0]
Literature:
Wickham, H. (2019). Advanced R. CRC press.
Chambers, J. (2008). Software for data analysis: Programming with R. Springer Science & Business Media.