Lab: Introduction to R
A.Y. 2023/2024
Learning objectives
This Lab aims to provide a comprehensive introduction to the practical use of the R software for statistical data analysis and reporting. We aim to equip students with fundamental skills in two key areas:
a) Mastering Essential R Operations
Our primary focus is to establish a robust foundation in fundamental R operations. Commencing with the basics of data exploration, data manipulation and data management, we'll progressively acquaint students with introductory statistical analyses, encompassing key aspects such as frequencies, hypothesis testing and simple regression. Moreover, we'll delve into data visualization techniques and guide students in creating simple data products.
b) Problem Solving with R
Secondly, we place a significant emphasis on the role of R as a problem-solving tool. While the Lab is designed for beginners, we believe that learning by doing is essential. Through collaborative learning and group discussions, students will cultivate their problem-solving skills by applying R to real-world problems. This approach is tailored to augment students' critical thinking abilities, empowering them to address practical challenges using R.
a) Mastering Essential R Operations
Our primary focus is to establish a robust foundation in fundamental R operations. Commencing with the basics of data exploration, data manipulation and data management, we'll progressively acquaint students with introductory statistical analyses, encompassing key aspects such as frequencies, hypothesis testing and simple regression. Moreover, we'll delve into data visualization techniques and guide students in creating simple data products.
b) Problem Solving with R
Secondly, we place a significant emphasis on the role of R as a problem-solving tool. While the Lab is designed for beginners, we believe that learning by doing is essential. Through collaborative learning and group discussions, students will cultivate their problem-solving skills by applying R to real-world problems. This approach is tailored to augment students' critical thinking abilities, empowering them to address practical challenges using R.
Expected learning outcomes
By the end of this course, students are expected to:
-Develop the ability to read and write code in the R programming language.
-Apply R for data exploration, management, and visualization.
-Demonstrate a practical understanding of R for statistical data analysis.
-Perform basic statistical operations using R.
-Create data products such as reports and slides using R.
-Build a strong foundation to independently expand R skills for future needs.
-Develop the ability to read and write code in the R programming language.
-Apply R for data exploration, management, and visualization.
-Demonstrate a practical understanding of R for statistical data analysis.
-Perform basic statistical operations using R.
-Create data products such as reports and slides using R.
-Build a strong foundation to independently expand R skills for future needs.
Lesson period: First trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
This Lab is structured across ten sessions, providing a step-by-step introduction to the practical applications of the R software for statistical data analysis and reporting. Each session is meticulously designed to build a solid foundation in essential R operations.
**Course Plan:**
1. Introduction to R Environment
2. Data Exploration and Manipulation
3. Data Management
4. Descriptive Statistics
5. Inferential Statistics (Part 1)
6. Inferential Statistics (Part 2)
7. Univariate Regression
8. Univariate Regression - Assumptions
9. Data Visualization
10. Data Products and Wrap-Up
Please note that the progression and topics covered may be adjusted based on the learning pace and needs of the class.
**Course Plan:**
1. Introduction to R Environment
2. Data Exploration and Manipulation
3. Data Management
4. Descriptive Statistics
5. Inferential Statistics (Part 1)
6. Inferential Statistics (Part 2)
7. Univariate Regression
8. Univariate Regression - Assumptions
9. Data Visualization
10. Data Products and Wrap-Up
Please note that the progression and topics covered may be adjusted based on the learning pace and needs of the class.
Prerequisites for admission
There are no specific prerequisites for this course. It is designed for beginners with no prior experience in R.
Basic computer literacy and familiarity with data concepts are recommended but not mandatory.
Basic computer literacy and familiarity with data concepts are recommended but not mandatory.
Teaching methods
Lectures: In the Lab sessions, the instructor covers a wide range of R operations for analyzing political and social data, including data exploration, management, statistical analysis (frequencies, hypothesis testing, linear regression), data visualization, and creating data products.
Practical Application: The course emphasizes active learning through guided exercises and real-world applications.
Collaborative Learning: Group discussions are encouraged to foster knowledge sharing and collaborative problem-solving.
Q&A Opportunities: Dedicated moments in class are provided for seeking clarification and discussing any questions.
Self-Directed Study: Apart from class time, students engage in self-paced learning through assignments and independent study.
Practical Application: The course emphasizes active learning through guided exercises and real-world applications.
Collaborative Learning: Group discussions are encouraged to foster knowledge sharing and collaborative problem-solving.
Q&A Opportunities: Dedicated moments in class are provided for seeking clarification and discussing any questions.
Self-Directed Study: Apart from class time, students engage in self-paced learning through assignments and independent study.
Teaching Resources
All teaching materials, including slides, code files, and replication materials, will be provided in class.
Students interested in attending the lab are invited to fill in a form (link below) with their name and email address. This will ensure they receive instructions to set up R on their machines a few days before the start of the class.
https://docs.google.com/spreadsheets/d/1oiXa34LeHrPZqRdFq0FOc_Rwgj6cReE03q2eDPvKncs/edit?usp=drivesdk
Students interested in attending the lab are invited to fill in a form (link below) with their name and email address. This will ensure they receive instructions to set up R on their machines a few days before the start of the class.
https://docs.google.com/spreadsheets/d/1oiXa34LeHrPZqRdFq0FOc_Rwgj6cReE03q2eDPvKncs/edit?usp=drivesdk
Assessment methods and Criteria
Laboratory will be based on a 'pass' or 'fail' system, without the assignment of numerical grades. Students will be evaluated primarily on active participation in lectures and laboratory activities.
SPS/07 - GENERAL SOCIOLOGY - University credits: 3
Laboratories: 20 hours
Professor:
Pagano Giovanni
Professor(s)