— with Roland Toth (WI)
When: March 12-13, 10am-5pm
Where: WI Flexroom (A1_04); in-person only
Level: Beginner/Intermediate
Category: Data analysis
Seats: 20
Abstract: Data analysis is an essential skill for quantitative scientific research. While SPSS, Stata etc. is statistical software, R is a programming language and enables flexible data wrangling, analysis, visualization, and documentation, virtually without limits. R is available for free, open-source, and does not require purchasing or renting a license. Thousands of free-to-download packages allow statistical analyses of all kinds. In this workshop, you will be introduced to R/RStudio, programming, data wrangling, and data analysis. To achieve this, the most important basics of programming and popular univariate, bivariate, and multivariate analysis methods will be applied through hands-on experience with R. First and foremost, Markdown will be instructed to encourage reproducible research and structured reporting of data analyses. In addition to R, it facilitates the efficient production of whole manuscripts and interactive data analysis documentations in various formats (LaTeX, PDF, HTML, …).
The workshop will take place over the course of two days. On the first day, attendees will engage in the basics of programming, Markdown, and data wrangling. During the second day, we encourage participants to work on independent research projects, applying their acquired data analysis techniques to relevant datasets. Both days will involve interactive exercises, where participants are presented concepts and coding strategies, while also being encouraged to tackle smaller tasks. Building on the success of the past two years, this is the third edition of Introduction to Data Analysis and Programming with R.
Roland Toth is a Data Scientist at the “Methods Lab” at the Weizenbaum Institute, where he supports the research groups at the institute methodologically. He is also a PhD candidate at Freie Universität Berlin. His research focuses on the measurement of mobile media use, research design, and quantitative methodology (i.e., surveys, experience sampling, logging).