Using R for Data Science

Keywords: R, data management, visualization

Introduction to the course topics: In this learning-by-doing course the participants will receive an introduction to R and the tidyverse, which is a collection of R packages designed for data science. After an introduction to each topic, the participants will work on hands-on exercises.

Topics:

• import/export data using readr
• data management using dplyr
• visualisation using ggplot2
• creating tidy tibbles with tidyr and tibble
• introduction to functional programming using purr
• describing data with the linear regression model using modelr

In addition, the course will show how to do reproducible research by using R. Therefore we will use the knitr and rmarkdown or quarto.

Course aims:

• Apply R code to read in and visualize data
• Analyze data by applying appropriate data transformations
• Describe data by fitting basic statistical models to the data

Teaching methods: Presentation of content with integrated exercise sessions

This course fits doctoral candidates in the following phase: at the beginning of the doctorate │ during the doctorate

Participation requirements: The course requires no prior R knowledge. General programming skills are helpful, but not mandatory.

Literature: Grolemund, G. and Wickham, H. (2016). R for Data Science. O'Reilly Media.

Technical requirements: Access to internet, laptop, camera, microphone, zoom

• install R from http://www.r-project.org
• install RStudio from http://www.rstudio.com
• check if you are able to install add-on packages by installing the tidyverse, a collection of packages; see https://vimeo.com/220490447

Additonal information, notes: none

What participants say about this course:

• I liked that we had the option to choose between online and face to face. The excercise sessions were really helpful.