Professor involved: Davide Gentilini

Course learning outcomes/abstract: The “SummeR School: Introduction to R for Statistical Analysis” is designed to equip participants with foundational skills in R programming, focusing on data management, statistical analysis, and data visualization. R is a versatile, open-source environment widely utilized for statistical computing and graphics. This course aims to provide participants with the necessary and sufficient knowledge to introduce and use the language and potential of R in their work. Key Learning Outcomes: Understanding the R Environment: Gain proficiency in installing, configuring, and navigating the R environment, including its architecture, packages, and functions. Data Importation and Manipulation: Learn to import datasets of various formats into R, perform data cleaning, handle missing values, and manipulate data structures such as vectors, matrices, lists, and data frames. Data Visualization: Develop skills to create diverse graphical representations using R’s base graphics and the ggplot2 package, enabling effective data visualization and result presentation. Statistical Analysis: Acquire the ability to perform descriptive and inferential statistical analyses, including parametric and non-parametric tests, analysis of variance, linear and logistic regression, and principal component analysis. Application of Statistical Methods: Learn to select and apply appropriate statistical methods based on study design and data types, enhancing the validity and reliability of analytical outcomes. Practical Implementation: Engage in hands-on exercises and real-world examples to reinforce theoretical knowledge and develop practical skills in data analysis using R. By the end of this course, participants will be equipped to independently manage, analyze, and visualize data using R, facilitating their research and professional activities across various disciplines.

Goals:  The “SummeR School: Introduction to R for Statistical Analysis” is designed to provide participants with a comprehensive foundation in R programming, emphasizing data management, statistical analysis, and data visualization. The primary objectives of the course are: Develop Proficiency in R Programming: Equip participants with the skills to navigate the R environment effectively, including installation, configuration, and utilization of its core functions and packages. Enhance Data Management Capabilities: Teach methods for importing, cleaning, and manipulating diverse datasets, ensuring participants can handle various data types and structures within R. Strengthen Statistical Analysis Skills: Provide a solid understanding of both descriptive and inferential statistical methods, enabling participants to select and apply appropriate techniques for their specific research or professional needs. Advance Data Visualization Techniques: Introduce participants to R’s graphical capabilities, particularly through the ggplot2 package, to create clear and informative visual representations of data. Promote Reproducible Research Practices: Encourage the adoption of reproducible research methodologies by utilizing R scripts and R Markdown for documentation and reporting. Foster Practical Application: Engage participants in hands-on exercises and real-world examples, facilitating the direct application of theoretical knowledge to practical scenarios.

Number of hours and planning: The course will last at least 20 hours (5 CFU), and will be divided into 5 meetings. Individual meetings can also be attended.

Period: 21-25 july 2025

Delivery mode and location ( in presence, on line, ecc): Online

REGISTRATION FORM: https://forms.gle/UmXK6DMswL7ZdmsZ6

Language: english

Evaluation criterial: The degree of learning will be tested at the end of the course by subjecting the participants to a test. The verification test will consist of a dataset to analyze and a series of questions related to the dataset to answer.

Credits (CFU): 5

Depliant