R Programming Course

Lesson 1 • Writing and Running R Scripts

Teaches script creation, sourcing, and console interaction. Connects syntax knowledge to reproducible, file-based workflows.

Lesson 2 • Getting Help and Using Documentation

Demonstrates help(), ?, and online resources for self-directed learning. Equips students to resolve issues independently throughout the course.

Lesson 3 • Installing and Configuring R

Set up R and RStudio on major operating systems. Establishes the technical baseline every subsequent chapter depends on.

Lesson 4 • Core R Syntax and Operators

Covers assignment, arithmetic, logical, and comparison operators. Provides the syntactic vocabulary used in every R expression.

Lesson 1 • Matrices and Arrays

Builds two- and multi-dimensional structures using matrix() and array(). Prepares students for numerical computing and tabular operations.

Lesson 2 • Data Frames: The Core Tabular Structure

Teaches data.frame() creation, column access, and row filtering. Data frames are the standard format for all data analysis workflows.

Lesson 3 • Lists and Nested Structures

Introduces heterogeneous containers via list() and nested indexing. Lists are essential for storing mixed-type outputs and model results.

Lesson 4 • Factors and Ordered Categories

Explains factor() for categorical data and ordered factors for ordinal variables. Correct factor usage is critical for modeling and plotting.

Lesson 1 • Conditional Statements

Covers if, else if, else, and ifelse() for branching logic. Conditional control is the foundation of all decision-driven R programmes.

Lesson 2 • Writing Custom Functions

Defines functions with arguments, default values, and return values. Custom functions are the primary tool for reusable, testable code.

Lesson 3 • Scope and Environments

Explains local vs. global scope and the R environment hierarchy. Understanding scope prevents subtle bugs in function-heavy scripts.

Lesson 4 • Loops: for, while, and repeat

Teaches iteration patterns and loop control with next and break. Loops automate repetitive operations across data structures.

Lesson 1 • Handling Missing and Inconsistent Data

Teaches NA detection, imputation strategies, and duplicate removal. Clean data is a prerequisite for valid statistical and visual outputs.

Lesson 2 • Exporting and Saving Results

Demonstrates write.csv(), saveRDS(), and export to Excel. Proper export ensures reproducibility and shareability of analytical outputs.

Lesson 3 • Data Type Conversion and Validation

Covers as.numeric(), as.Date(), and input validation patterns. Type mismatches are a leading cause of silent errors in R pipelines.

Lesson 4 • Connecting to Databases and APIs

Introduces DBI, RODBC, and httr for database and web data access. Expands data sourcing beyond local files to live systems.

Lesson 1 • String Manipulation with stringr

Introduces str_detect(), str_replace(), and str_extract() for text columns. String cleaning is a frequent requirement in real-world datasets.

Lesson 2 • Grouping and Summarising Data

Covers group_by() and summarize() for aggregated statistics. Grouped summaries are the backbone of exploratory and reporting workflows.

Lesson 3 • Reshaping Data with tidyr

Covers pivot_longer(), pivot_wider(), and separate(). Reshaping converts data between wide and long formats required by different functions.

Lesson 4 • Core dplyr Verbs

Teaches filter(), select(), mutate(), arrange(), and rename(). These five verbs handle the majority of row and column transformations.

Lesson 1 • ggplot2 Grammar and Aesthetics

Explains the ggplot() call, aes() mappings, and the layer model. Understanding the grammar is required before building any specific chart type.

Lesson 2 • Faceting and Multi-Panel Plots

Introduces facet_wrap() and facet_grid() for small-multiple displays. Faceting reveals group-level patterns that single plots obscure.

Lesson 3 • Common Chart Types

Covers scatter, bar, histogram, box, and line plots. Each geom is matched to the data type and analytical question it best answers.

Lesson 4 • Scales, Axes, and Coordinate Systems

Teaches scale_*() functions, axis labels, and coord_flip(). Proper scaling ensures accurate visual encoding of quantitative information.

Lesson 1 • Probability Distributions

Covers d, p, q, r functions for normal, binomial, and other distributions. Distribution functions underpin simulation, testing, and modeling.

Lesson 2 • Linear Regression

Builds and interprets simple and multiple linear models with lm(). Regression is the most widely used predictive and explanatory technique.

Lesson 3 • Hypothesis Testing

Teaches t-tests, chi-square tests, and ANOVA using base R functions. Hypothesis tests provide evidence-based answers to analytical questions.

Lesson 4 • Descriptive Statistics

Computes central tendency, spread, and distribution shape using base R. Descriptive summaries are the first step in any analytical workflow.

Lesson 1 • Building a Basic R Package

Walks through devtools, DESCRIPTION, and roxygen2 documentation. Packaging code enables reuse, testing, and distribution across teams.

Lesson 2 • Project Organization and Best Practices

Covers RStudio Projects, directory structure, and naming conventions. Consistent organization makes code navigable for collaborators and future selves.

Lesson 3 • Version Control with Git and GitHub

Integrates Git commits, branches, and pull requests into R workflows. Version control is the industry standard for collaborative and auditable code.

Lesson 4 • Package Management and renv

Teaches library(), install.packages(), and renv for dependency locking. Reproducible environments ensure code runs identically across machines.