Introduction to R 2400-MIKRO-AIBF-IR

The course is delivered asynchronously through an online learning platform and is designed to prepare participants to use R during the SPINAKER summer school. It is suitable both for beginners and for participants who already have some experience with R but need to refresh or systematise their knowledge. The course begins with a placement test, which allows participants to assess their current level and determine how much preparatory work they should complete before the summer school.

The content is organised into 14 modules that gradually build participants’ knowledge and practical skills. The course starts with an introduction to R, RStudio and the broader programming environment, including the basic principles of how R works and how code is executed and organised. The following modules introduce the main data types and data structures used in R. Vectors, matrices and data frames are discussed step by step, allowing participants to understand how information is stored, accessed and transformed.
Further modules focus on importing data from different sources and on using R for basic statistical analysis and modelling. Participants learn how to perform common analytical operations and how to interpret the structure and results of simple statistical procedures. Two modules are devoted to creating graphics in base R, from basic plots to more advanced visual representations of data.
The course also introduces loops and functions as tools for organising code, reducing unnecessary repetition and making analytical workflows more efficient. R Markdown is presented as a framework for preparing clear, reproducible and well-structured analytical reports that combine code, results and explanations in a single document.
The final part of the course introduces the tidyverse, which is widely used as a standard framework for modern data processing and analysis in R. Participants learn the basic principles of transforming, organising and working with data using tidyverse tools. The course concludes with an introduction to clean code principles, with particular emphasis on readability, consistency, transparency and reproducibility as essential elements of high-quality analytical work.
Each module includes a recorded lecture, a slide presentation, an R script with examples and practical exercises, and tasks to be completed independently. After each module, participants complete a short graded quiz. The quizzes can be repeated, and the best result is recorded. After completing the learning modules, participants work on a final data-analysis case study that allows them to assess how confidently they can apply R to a complete analytical task.

The project ‘Application of Artificial Intelligence in Business and Finance’ (No. BPI/SPI/2024/1/00078) is implemented by the University of Warsaw as part of the Spinaker programme organised by the National Agency for Academic Exchange (within the project ‘Wsparcie tworzenia i realizacji międzynarodowych programów kształcenia’, funded by the European Funds for Social Development 2021–2027).