This module introduces the main data structures used in R for organizing and analyzing data. Learners will work with matrices, lists, data frames, and factors, and learn how to create, access, modify, inspect, and subset each structure. The module includes practical examples, exercises, and a quiz to help learners understand when to use each data structure, including data frames for real-world data analysis and factors for categorical variables.

This module introduces vectors in R and explains how to create, manipulate, and analyze them. Learners will practice creating vectors using c(), seq(), and rep(), performing vectorized arithmetic, understanding vector recycling, and accessing vector elements using positional, name-based, and logical indexing. The module also covers modifying vectors, useful vector functions, worked examples, and practical exercises and quizzes to reinforce the concepts.

This lesson introduces variables and core data types in R, including numeric, integer, character, logical, and complex data. It covers how to inspect objects using class(), typeof(), and str(), convert values between data types, understand special values such as NA, NULL, NaN, and Inf, and use arithmetic, relational, logical, and %in% operators in R.

This lesson introduces the basics of R and RStudio. It covers the RStudio interface, comments, text, basic arithmetic operations, and simple functions. Learners will practice addition, division, exponentiation, and calculating square roots using R.

This module introduces non-parametric statistical methods for small, skewed, ordinal, or non-normal data. It covers ranking, Wilcoxon, Mann-Whitney U, Kruskal-Wallis, and Spearman correlation, with practical R examples, visualizations, and exercises.

An introductory guide to parametric statistics, covering core assumptions, t-tests, ANOVA, and practical applications in statistical analysis.