Ateneo - UPO

The R software environment for graphical representation and statistical data analysis, with an introduction to the main packages in the tidyverse suite for data manipulation and visualization.

Recommended Textbook: R for Data Science (https://r4ds.hadley.nz/). Additional reference material and course notes will be posted on the DIR website.

Knowledge and understanding: The course aims to consolidate and broaden students’ theoretical knowledge of statistics and probability, providing appropriate tools for data processing, analysis, and visualization. To this end, R, a free and open-source software environment for statistical computing and graphics, will be used.

Applying knowledge and understanding: By the end of the course, students will be able to apply the acquired knowledge in multidisciplinary contexts, using R to analyze real-world data and produce effective, professional-quality graphical representations.

Making judgements: Students will be able to select and apply the most appropriate statistical methods, critically interpret the results obtained, and independently extend their knowledge when necessary.

Communication skills: Students will be able to present and communicate the results of a data analysis clearly, using both numerical summaries and appropriate graphical representations.

Learning skills: Students will develop the ability to explore the course topics independently, pursue personalized learning paths, and critically select the most appropriate resources, including those available online.

Basic knowledge of mathematics and statistics

The course will be held in a traditional classroom. Students are expected to bring their own laptop or tablet. Practical activities will be carried out using the R programming language through the RStudio environment, either installed on the student’s device or accessed via a web-based platform, depending on students’ needs and technical requirements. The course includes interactive lectures, live coding demonstrations, and guided hands-on exercises designed to promote the practical application of the concepts and tools presented during the course.

Reference material and additional informations are posted on the DIR website.
Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/services-students-physical-or-learning-disabilities
Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can contact me to define the examination modalities, concerning academic aspects.

Assessment will be based on an online final examination. The tests will take place on DIR and consist of a series of exercises aimed at addressing issues covered during the course. The exercises are of various types and may range from simple theoretical questions to more complex ones requiring the download of datasets and their analysis. The ongoing tests will serve as preparation and an intermediate benchmark for the final exam.

Introduction to the R software and the RStudio environment. Basic operations, objects, and functions. Data import and export. R packages and an introduction to the tidyverse. Data structures: vectors, matrices, arrays, lists, tibbles, and data frames. Data manipulation with dplyr. Control structures: for loops and conditional statements. Handling missing data. Data visualization using base R graphics and the ggplot2 package. Introduction to selected data analysis techniques, including regression models, analysis of variance (ANOVA), basic pharmacokinetic models, clustering techniques (e.g., K-means), simulations, and text data manipulation.

Upon successful completion of the course, students will be able to:

* use the R software and the RStudio environment to import, manage, analyze, and visualize data;
* apply fundamental data manipulation techniques and create effective graphical representations using base R and tidyverse packages;
* interpret and communicate the results of statistical analyses through appropriate numerical summaries and graphical displays;
* apply the acquired knowledge to data analysis problems in different contexts, independently selecting appropriate tools and learning resources, including online documentation and reference materials..