Data Wrangling with R

Target Group: Master’s students, doctoral candidates, and all researchers with a doctorate

Language: English

Register: Data Wrangling with R

This workshop takes place in cooperation with the GMZ.

This workshop introduces packages from the Tidyverse for data wrangling in R. Although all steps in a data analysis project can be carried out with base R functions, the Tidyverse offers a compelling alternative which is often faster, more concise, and more consistent. Many people also think that the Tidyverse workflow is easier to learn and understand than base R. Topics covered include:

• Visualization (ggplot2)
• Transformation (dplyr)
• Tibbles (tibble)
• Import data (readr)
• Tidy data (tidyr)

Students are expected to be familiar with basic R concepts such as package management and data types (vectors, data frames). Knowing how to work with RStudio is also helpful (e.g. writing and running scripts, getting and setting the working directory, viewing plots, ...). Therefore, a working installation of both R and RStudio is necessary to follow along in the workshop.

This workshop is based on selected chapters of the book "R for Data Science" by Hadley Wickham and Garrett Grolemund, which is available for free at https://r4ds.had.co.nz/index.html.

Clemens Brunner is a senior postdoc with a background in electrical/biomedical engineering and computer engineering. He works at the Educational Neuroscience group at the Institute of Psychology, University of Graz, Austria. His research interests include neurophysiological substrates of number processing and arithmetic, EEG oscillations and connectivity analysis, biomedical signal processing, applied machine learning and statistics, brain-computer interfaces, and software development. He is a strong proponent of open-source software and believes that science should be open as well, including data and analysis scripts. Python is his favorite language, but he also enjoys performing data analysis and statistical modeling with R (and he is also interested in Julia). He maintains and develops MNELAB (a graphical user interface for processing EEG/MEG data using MNE), the Qt/C++ based biosignal visualization tool SigViewer, SCoT (a Python package for EEG-based source connectivity estimation), and XDF.jl (a Julia package for reading XDF files). He is part of the MNE and pyXDF development teams and has contributed to scikit-learn, pandas, Matplotlib, PsychoPy, pybv, and BioSig. More information is available on his website at cbrnr.github.io.

Registration & Fees: 

Please log in on the left-hand side on the homepage under ‘Login’ before registering for the course.

• Students and staff: €20
• External participants: €100