R Statistics Course

Lesson 1 • Installing and Configuring R and RStudio

Covers R and RStudio installation, pane layout, and global options. Establishes the workspace every subsequent chapter depends on.

Lesson 2 • Core R Syntax and Operators

Introduces assignment, arithmetic, logical, and comparison operators. Provides the syntactic foundation for all scripting tasks ahead.

Lesson 3 • Control Flow and Functions

Teaches if-else, for loops, while loops, and custom function creation. Enables students to write reusable, logic-driven code blocks.

Lesson 4 • R Data Types and Structures

Explains vectors, matrices, lists, and data frames with type coercion rules. Students gain the structural vocabulary needed for data analysis.

Lesson 1 • Reshaping Data with tidyr

Covers pivot_longer, pivot_wider, separate, and unite for structural reshaping. Enables tidy data principles essential for modeling and visualization.

Lesson 2 • Importing Data from Multiple Sources

Covers CSV, Excel, JSON, and database imports using base R and tidyverse packages. Connects raw data access to downstream cleaning workflows.

Lesson 3 • Data Wrangling with dplyr

Applies filter, select, mutate, group_by, and summarize verbs to transform data. Builds the core tidyverse manipulation skill set.

Lesson 4 • Handling Missing and Inconsistent Data

Teaches NA detection, imputation strategies, and duplicate removal. Directly reduces data quality issues before analysis begins.

Lesson 1 • EDA with Base R and ggplot2 Previews

Uses hist(), boxplot(), and basic ggplot2 calls to visualize distributions quickly. Bridges EDA findings to the full visualization chapter ahead.

Lesson 2 • Univariate Distribution Analysis

Examines central tendency, spread, and shape for single variables. Establishes baseline understanding of each variable before multivariate work.

Lesson 3 • Outlier Detection and Treatment

Applies IQR, z-score, and robust methods to identify and handle outliers. Prevents extreme values from distorting subsequent statistical models.

Lesson 4 • Bivariate and Multivariate Relationships

Explores correlations, cross-tabulations, and covariance between variable pairs. Identifies candidate predictors for later modeling chapters.

Lesson 1 • Grammar of Graphics Fundamentals

Explains aesthetics, geometries, and layers as ggplot2 building blocks. Provides the conceptual model underlying every chart in this chapter.

Lesson 2 • Exporting and Embedding Visualizations

Saves plots in raster and vector formats and embeds them in reports. Ensures charts are reproducible and ready for publication or presentation.

Lesson 3 • Common Chart Types and Use Cases

Covers bar, line, scatter, histogram, and box plots with appropriate data contexts. Equips students to select the right chart for each analytical question.

Lesson 4 • Themes, Colors, and Annotations

Customizes plot appearance with themes, color palettes, and text annotations. Produces charts that meet professional and accessibility standards.

Lesson 1 • Probability Distributions in R

Covers normal, binomial, Poisson, and t distributions using d, p, q, r functions. Builds the probabilistic foundation required for all inference methods.

Lesson 2 • Hypothesis Testing Framework

Establishes null and alternative hypotheses, p-values, and Type I/II errors. Provides the decision-making logic applied in every subsequent test.

Lesson 3 • Sampling, Estimation, and Confidence Intervals

Explains sampling distributions, standard error, and confidence interval construction. Connects theoretical probability to practical estimation of population parameters.

Lesson 4 • Parametric Tests: t-Tests and ANOVA

Applies one-sample, two-sample, and paired t-tests and one-way ANOVA in R. Covers assumption checking and post-hoc comparisons for group differences.

Lesson 1 • Model Comparison and Validation

Uses train-test splits, k-fold cross-validation, and RMSE/MAE metrics to validate models. Builds rigorous evaluation habits for predictive modeling.

Lesson 2 • Logistic Regression for Binary Outcomes

Fits logistic models with glm(), interprets odds ratios, and evaluates classification performance. Extends regression skills to categorical response variables.

Lesson 3 • Variable Selection and Regularization

Applies stepwise selection, AIC/BIC criteria, ridge, and lasso regularization. Reduces overfitting and identifies parsimonious predictor sets.

Lesson 4 • Simple and Multiple Linear Regression

Fits OLS regression models with one and multiple predictors using lm(). Establishes the core modeling workflow used throughout this chapter.

Lesson 1 • Principal Component and Factor Analysis

Applies PCA and exploratory factor analysis to reduce dimensionality and identify latent structure. Prepares high-dimensional data for modeling and interpretation.

Lesson 2 • Cluster Analysis Techniques

Implements k-means, hierarchical, and density-based clustering to segment observations. Enables unsupervised pattern discovery in unlabeled datasets.

Lesson 3 • Analysis of Variance Extensions

Covers two-way ANOVA, interaction effects, and repeated-measures ANOVA. Extends group comparison skills to factorial and longitudinal designs.

Lesson 4 • Time Series Analysis

Covers decomposition, stationarity testing, ARIMA modeling, and forecasting in R. Addresses temporal data structures distinct from cross-sectional analysis.

Lesson 1 • Project Structure and Package Management

Organizes R projects with consistent folder structures and renv for dependency locking. Ensures portability and reproducibility across computing environments.

Lesson 2 • Version Control with Git and GitHub

Applies Git commits, branches, and pull requests to R project management. Enables collaborative, traceable development of analytical code.

Lesson 3 • Sharing and Publishing Analytical Outputs

Deploys reports and dashboards via Quarto, Shiny, and public hosting platforms. Completes the pipeline from raw data to accessible, shareable deliverables.

Lesson 4 • Workflow Automation with targets

Builds dependency-aware pipelines using the targets package to skip up-to-date steps. Reduces recomputation time in large, multi-step analytical workflows.