— with Roland Toth (WI)
When: April 10-11, 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 work. 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 in a hands-on experience in R. In order to encourage and enable reproducible research and structured reporting of data analyses, Markdown will be introduced right away. Together with R, it allows for efficiently producing 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, we will engage in the basics of programming, Markdown, and data wrangling. On the second day, we will proceed with data analysis, and you will get the chance to work with a data set and answer a research question on your own. On both days, there will be sections in which you are presented with concepts and coding techniques, but also sections in which you are asked to solve small tasks. Building on the success of last year, this is the second 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).