Fundamentals of R

Aims

The R statistical software is a powerful and widely used tool for statistical data science. This introductory course aims to equip students with a solid foundation to implement essential data analytic tasks with R. The course covers the basics of R programming using the RStudio environment and introduces R Markdown for generating dynamic reports.

Students will learn how to use R to:

• Perform simple operations
• Write basic R codes for simple procedures
• Implement data management tasks
• Create simple data descriptions as tables or plots

The course emphasizes practical application, utilising RStudio, Markdown, the tidyverse suite, and ggplot2 for data visualisation. By the end of the course, students will be able to implement a workflow from importing raw data into R to generating reproducible reports.

This course is ideal for graduate students and applied researchers in quantitative disciplines who wish to develop foundational skills in R for their data analysis needs.

Content

• Installing and setting up R and RStudio.
• Introduction to basic R syntax for simple operations and basic coding.
• Data types, data frames and control structures.
• Reading data into R, accessing R packages, writing simple R functions.
• Data management and manipulation using tidyverse packages such as dplyr.
• Basics of R Markdown for reproducible research through dynamic documents and reports.
• Principles of visualisation and creating plots with ggplot2

Methods

The course will consist of a mixture of instructor-led sessions to introduce concepts and demonstrate techniques, hands-on exercises for putting learning into practice, and home assignments to practice skills.

Target Group

All Doctoral Candidates

Requirements

The course requires no prior knowledge of R, nor programming experience. Due to the course's practical nature, students should bring a laptop with R (https://www.r-project.org/) and RStudio (https://posit.co/products/open-source/rstudio/) installed.

About the Trainer

The instructor for this workshop is Giusi Moffa, assistant professor of Statistics at the Department of Mathematics and Computer Science of the University of Basel, with consolidated experience in data analyses supporting research across diverse disciplines, including clinical and biomedical studies and epidemiology.

Workload

Total of 30 hours (2h preparation, 2x 8h attendance, 12h homework)

Feature

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.