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Advanced R Programming
About
Location:
Online via Zoom
Online via Zoom
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 385 €
Academics: 578 €
Commercial: 1155 €
Keywords
Additional links
Lecturer(s): Tom Paskhalis
Course description
Note that this is a blended learning course consisting of a self-learning phase (estimated workload: 3 hours) from 14.07.-20.07.2025 and live sessions from 23.-25.07.2025. For more detailed information, see section “Organizational structure of the course”.
R is the major language for statistical programming and data analysis. In its more than 30-year history it went from an open-source implementation of its commercial predecessor S to being the staple of any data analyst's toolbox. The versatility of R lies in its immense extendibility, something we will discuss in this workshop.
This workshop is aimed at participants who already have some experience with R but would like to further hone their skills in this language. We will cover advanced topics in R, such as control flow, functional programming, debugging, testing, and parallelization. The workshop's ultimate goal is to provide the participants with a solid foundation in R programming, which will allow them to tackle more complex problems and design efficient workflows for data analysis.
Organizational Structure of the Course
This workshop is organized as a blended learning course. In order to ensure that you have the necessary background knowledge, the course will start with a self-learning phase that contains a recap of the key concepts of R programming in the form of short, pre-recorded videos and exercises. The estimated workload for the self-learning phase is 3 hours and you will be expected to work through these materials before the start of the workshop (by 20 July 2025 at the latest). If you have any questions during the self-learning phase, you can post them in the course forum. Queries will be addressed within two working days at the latest.
The rest of the workshop will be organized as live teaching via Zoom and be composed of lectures interspersed with hands-on programming exercises. While lectures are designed to give an overview of the topic, key concepts, and their implementations in R, the exercises are supposed to give you the opportunity to apply these ideas in practice.
Thematic Sections and Learning Units
Course contents are organized into thematic sections, each of which contains several learning units. Learning units should not comprise more than 3 hours of workload. Please list all thematic sections and learning units, including a brief description of each learning unit's content and an estimate of the workload in minutes for each learning unit. This estimate should cover both watching the videos, reading any explanatory texts or readings, and working on the exercises.
Target group
You will find the course useful if:
- you have been working with R for a while and are looking to take your R skills to the next level to facilitate more complex programming tasks.
- you wish to write more efficient R code.
- you want to learn (more) about the power of functional programming.
- you want to learn more about debugging and testing
Learning objectives
By the end of the course you will:
- be able to describe core programming concepts, language structures, and design principles.
- demonstrate deep familiarity with the R programming languages.
- have the ability to write, execute, and debug R scripts.
- understand the foundations of functional programming in R.
- design efficient workflows for data analysis.
Prerequisites
This course covers intermediate and advanced programming in R, and basic familiarity with the key R concepts, such as objects and operators, data types, and data manipulation, is assumed.
You should, therefore, have reasonable prior experience with R (e.g. working with R for more than 6 months, up to a couple of years) to get the most out of this workshop. Alternatively, extensive knowledge of another statistical software (e.g. Stata/SAS) and some introductory knowledge of R syntax should also suffice. A short overview of some R fundamentals will be provided at the beginning of the workshop. Some participants with prior experience in statistical analysis/programming who recently took an introductory R workshop (e.g. an “Introduction to R” workshop offered by GESIS) and have since gathered experience in working with R on a regular basis might consider enrolling in this workshop as a follow-up.
Software requirements
In this course we will use R (4+) and RStudio integrated development environment. Alternatively, you can use Visual Studio Code with R Extension installed.
While it is possible to follow most of the course materials using cloud platforms for hosting Jupyter Notebooks such as Kaggle Code, the experience for certain topics, such as debugging and testing, can be considerably different.
