R Basics for Social Sciences

• Session 1 (17/1/2024): Introduction to base R & RStudio
• Session 2 (31/1/2024): Data Visualization using ggplot2 (1)
• Session 3 (14/2/2024): Data Visualization using ggplot2 (2)
• Session 4 (28/2/2024): Data Manipulation using dplyr (1)
• Session 5 (13/3/2024): Data Manipulation using dplyr (2)
• Session 6 (10/4/2024): Cleaning messy data with tidyr
• Session 7 (24/4/2024): Working with (generalized) linear models in R
• Session 8 (8/5/2024): Creating regression tables for latex, markdown, and word
• Session 9 (22/5/2024): Simulating & visualizing quantities of interest (1)
• Session 10 (5/6/2024): Simulating & visualizing quantities of interest (2)

An introduction to R course typically aims to provide students with a solid foundation in the R programming language and its applications in data analysis and statistics. This course is intended to help faculty in the Social Sciences Department to learn the R language to be able to teach their courses using this software. The main objectives of such a course nclude:

• Familiarity with R Environment: Help students become comfortable with the R programming environment, including the R console and RStudio.
• Basic Syntax: Teach the fundamental syntax and data structures in R, such as vectors, data frames, and lists.
• Data Import and Export: Instruct students on how to import data from various file formats (e.g., CSV, Excel, and text) and export results.
• Data Manipulation: Cover data manipulation techniques, including subsetting, filtering, merging, and reshaping data.
• Statistical Analysis: Introduce basic statistical concepts and demonstrate how to perform common statistical tests and analyses using R.
• Data Visualization: Teach students how to create informative and visually appealing data visualizations using R packages like ggplot2.
• R Packages: Familiarize students with the concept of packages and how to install and use them to extend R's capabilities for specific tasks.
• Reproducible Research: Emphasize the importance of writing well-documented and reproducible R scripts and reports using tools like R Markdown.
• Basic Programming Concepts: Introduce students to programming concepts in R, such as loops, conditional statements, and functions.
• In-Class Applications: Provide practical examples and possible exercises to do in class with their students and how to solve them.

These objectives are designed to give the faculty a solid foundation in R that allows them to teach basic statistics, either descriptive or inferential, and incentivize them to apply this language in their research activities.

20 horas.

Fechas: 17, 31 enero 14, 28 febrero, 13 marzo, 10, 24 abril, 8, 22 mayo y 5 junio

Horario: de 10.30h a 12.30h

Aula 10.2.7.

• Miércoles. 17 y 31/01. 28/02. 13/03. 10 y 24/04. 8/05. 5/06.

Aula 14.0.10.

• Miércoles 22/05.