Introduction to R

Lesson 1 • Navigating the RStudio Interface

Identifies the four RStudio panes and their functions. Students practice running code interactively and saving scripts.

Lesson 2 • What R Is and Why It Matters

R's origins, strengths, and common use cases in data science and statistics. Establishes motivation before any syntax is introduced.

Lesson 3 • Getting Help in R

Covers built-in help functions, documentation pages, and community resources. Teaches self-sufficiency when encountering unfamiliar functions.

Lesson 1 • Atomic Data Types in R

Introduces numeric, integer, character, logical, and complex types. Students use typeof() and class() to inspect values.

Lesson 2 • Basic Syntax and Expressions

Covers arithmetic, logical, and comparison operators with correct R syntax. Forms the grammatical foundation for all subsequent code.

Lesson 3 • Variables and the Workspace

Explains variable naming rules, assignment, and workspace management. Students practice listing, removing, and inspecting objects.

Lesson 1 • Vectorized Operations

Demonstrates element-wise arithmetic and recycling rules. Students understand why R avoids explicit loops for most numeric tasks.

Lesson 2 • Matrices: Creation and Operations

Constructs matrices with matrix() and performs row/column operations. Connects 2D structure to linear algebra and tabular data concepts.

Lesson 3 • Creating and Indexing Vectors

Builds vectors with c(), seq(), and rep() and accesses elements by position and name. Vectors underpin nearly every R operation.

Lesson 1 • Subsetting and Filtering Data Frames

Applies logical conditions and index notation to extract rows and columns. Directly prepares students for data cleaning tasks.

Lesson 2 • Modifying and Merging Data Frames

Covers adding columns, renaming variables, and joining two data frames. Students produce analysis-ready datasets from multiple sources.

Lesson 3 • Understanding and Creating Lists

Defines lists as ordered collections of arbitrary objects. Students create nested lists and access elements with [, [[, and $.

Lesson 1 • Conditional Statements

Implements if, else if, and else logic and the vectorized ifelse(). Enables data-driven branching within scripts.

Lesson 2 • Loops in R

Covers for, while, and repeat loops with break and next controls. Students recognize when loops are appropriate versus vectorized alternatives.

Lesson 3 • Scope and the Apply Family

Explains lexical scoping and replaces loops with lapply(), sapply(), and vapply(). Students write cleaner, more idiomatic R code.

Lesson 1 • Data Transformation with dplyr

Applies the five core dplyr verbs to filter, select, mutate, arrange, and summarize. Students chain operations with the pipe operator.

Lesson 2 • Reshaping Data with tidyr

Converts between wide and long formats using pivot_longer() and pivot_wider(). Students recognize tidy data principles and apply them.

Lesson 3 • Data Cleaning Fundamentals

Addresses duplicates, inconsistent strings, and type mismatches. Establishes systematic cleaning habits before any analysis begins.

Lesson 4 • Exporting Cleaned Data

Writes processed data to CSV, RDS, and Excel formats for sharing. Completes the import-clean-export workflow students will use repeatedly.

Lesson 1 • Faceting and Multi-Panel Plots

Uses facet_wrap() and facet_grid() to display subgroup comparisons. Students reveal patterns across categories without cluttering a single plot.

Lesson 2 • Common Chart Types

Implements scatter plots, bar charts, histograms, and box plots. Students select the appropriate chart type for each data question.

Lesson 3 • Saving and Exporting Plots

Exports plots to PNG, PDF, and SVG at specified dimensions and resolution. Ensures visuals meet quality standards for reports and presentations.

Lesson 4 • Grammar of Graphics Foundations

Explains ggplot2's layered model: data, aesthetics, and geoms. Students construct their first plots and understand how layers combine.

Lesson 1 • Introduction to R Markdown

Creates reproducible documents that weave code, output, and prose. Students render HTML and PDF reports from a single source file.

Lesson 2 • Building a Complete EDA Report

Integrates cleaning, analysis, and visualization into one R Markdown report. Students practice the full analytical workflow on a real dataset.

Lesson 3 • Grouped Summaries and Comparisons

Aggregates data by category and compares distributions across groups. Directly applies dplyr skills to answer business and research questions.

Lesson 4 • Identifying Patterns and Outliers

Uses visual and statistical methods to detect anomalies and relationships. Prepares students to ask better questions before modeling.