This module introduces the fundamental data structures in R, including numeric, logical, and character vectors, as well as factors. Learners will gain hands-on experience creating, manipulating, and extracting data from these structures, and understand their importance in data analysis. Key functions and operators for basic data manipulation and organization are also covered.

This module introduces the structure and management of data frames in R, emphasizing techniques for accessing, modifying, reshaping, and aggregating data. Learners will explore practical functions for handling variables, importing data from other statistical packages, and preparing data for analysis. By the end, participants will be equipped to efficiently manage tabular data in social science research contexts.

This module introduces learners to efficient data management in R using both the data.table package and the Tidyverse suite. You will compare their unique approaches, explore key packages like tibble and tidyr, and practice tidying real-world datasets for analysis.

This module guides learners through the process of importing, recoding, and preparing social science survey data for analysis in R. You will learn how to handle data from formats like SPSS and Stata, interpret codebooks, and transform variables to suit analytical needs.

This module introduces techniques for handling data from complex survey samples, focusing on the creation and management of survey design objects and the application of weighting adjustments such as post-stratification, raking, and calibration. Learners will gain practical skills in preparing data for accurate statistical inference in social science research.

This module introduces learners to the fundamentals of working with temporal data in R, including the distinctions between regular and irregular time series. Learners will gain hands-on experience with date and time classes, and learn to manage irregular time series using the zoo package.

This module introduces the fundamentals of spatial and geographical data analysis in the social sciences using R's sf package. Learners will explore spatial data structures, coordinate systems, and key spatial relationships, as well as practical approaches to handling geographical data files and projections.

This module introduces key techniques for manipulating and analyzing textual data in R. Learners will explore essential string functions and discover how to manage text corpora using the tm package. By the end, you'll be equipped to process and analyze character vectors for text-based data analysis.

This module introduces learners to the key sources and citation practices used in political science research. You will explore how to properly reference major datasets and scholarly works, ensuring academic integrity and credibility in your own projects.