▸1. Learn R
- ▼ Getting Started
- Is R Worth Learning?
- Install R & RStudio
- RStudio IDE Tour
- ▼ R Fundamentals
- R Syntax 101
- R Data Types
- R Vectors
- R Matrices
- R Factors
- R Data Frames
- R Lists
- R Control Flow
- R Special Values
- R Type Coercion
- Writing R Functions
- Quiz
- ▼ Working Effectively
- R Subsetting
- Getting Help in R
- R Project Structure
- ▼ R Career & Resources
- R vs Python
- How to Learn R
- R for Excel Users
- R Interview Questions
- Quiz
- R Cheat Sheet
▸2. Data Wrangling
- ▼ Import & Setup
- Importing Data
- Pipe Operator
- Tidy Data
- ▼ dplyr Essentials
- dplyr filter & select
- dplyr mutate & rename
- dplyr group_by & summarise
- dplyr arrange & slice
- dplyr across()
- dplyr case_when()
- Quiz
- ▼ Join & Reshape
- R Joins
- pivot_longer & pivot_wider
- separate() & unite()
- Quiz
- ▼ Clean & Quality
- Missing Values (NA)
- Data Quality Checking
- janitor Package
- ▼ Strings & Dates
- stringr
- Regex Patterns
- lubridate
- ▼ Scale & Connect
- DBI & Databases
- DuckDB & duckplyr
- Web Scraping (rvest)
- REST APIs (httr2)
- Data Wrangling with dplyr (Course)
- Join & Reshape (Course)
- data.table (Course)
- Report-Ready Tables (Course)
- Communicate & Automate (Course)
▸3. Statistics
- ▼ EDA & Data Quality
- Automated EDA
- Missing Data Viz (naniar)
- Outlier Detection
- ▼ Probability
- Probability Axioms
- Conditional Probability
- Random Variables
- Binomial vs Poisson
- Normal, t, F, Chi-Squared
- Central Limit Theorem
- Sampling Distributions
- LLN vs CLT
- Probability (Simulation-First)
- Expected Value and Variance
- ▼ Inference & Estimation
- Maximum Likelihood Estimation
- Hypothesis Testing
- Sample Size Planning
- Choosing the Right Test
- Statistical Tests
- Measures of Association
- Point Estimation
- Confidence Intervals
- Type I and II Errors
- Power Analysis
- Effect Size
- t-Tests
- Proportion Tests
- Normality & Variance Tests
- Chi-Square Tests
- Wilcoxon, Mann-Whitney & Kruskal-Wallis
- Multiple Testing Correction
- Quiz
- ▼ Regression
- Linear Regression
- Logistic Regression
- Feature Selection
- Model Selection
- Missing Value Treatment
- Outlier Analysis
- Advanced Regression Models
- Quiz
- ▼ Reporting & Communication
- Statistical Consulting
- Statistical Report Writing
- Bootstrap Confidence Intervals
- Reporting Statistics
- Regression Tables (3 packages)
- ▼ Regression in Practice
- Simple Linear Regression
- Multiple Regression
- Correlation (Pearson, Spearman, Kendall)
- Linear Regression Assumptions
- Dummy Variables in R
- Interaction Effects
- Regression Diagnostics
- Variable Selection
- Polynomial & Splines
- Ridge & Lasso Regression
- Robust Regression (rlm)
- Quantile Regression
- ▼ ANOVA & Experiments
- One-Way ANOVA
- Post-Hoc Tests After ANOVA
- Two-Way ANOVA
- Repeated Measures ANOVA
- ANCOVA
- Experimental Design in R
- Factorial Designs (2^k)
- A/B Testing
- MANOVA
- Mixed ANOVA
- ▼ GLMs & Categorical Data
- Categorical Data (Tables & Mosaic)
- Chi-Square Test of Independence
- Chi-Square Goodness-of-Fit
- Fisher's Exact Test
- Odds Ratios & Relative Risk
- Logistic Regression (glm + ROC)
- Logistic Regression (Diagnostics)
- Poisson Regression
- Poisson & Negative Binomial Regression
- Multinomial & Ordinal Logistic Regression
- ▼ Multivariate Methods
- Multivariate Distances & Hotelling's T²
- PCA with prcomp()
- Interpreting PCA Output
- factoextra (PCA + Clusters)
- Exploratory Factor Analysis
- SEM and CFA (lavaan)
- LDA (Linear Discriminant Analysis)
- Clustering (k-Means / HC / DBSCAN)
- Correspondence Analysis
- t-SNE and UMAP
- ▼ Nonparametric & Resampling
- When to Use Nonparametric Tests
- Wilcoxon Signed-Rank Test
- Mann-Whitney U Test
- Kruskal-Wallis Test
- Friedman Test
- Spearman & Kendall Correlation
- Bootstrap (boot package)
- ▼ Linear Algebra for Statistics
- Matrix Operations in R
- Solving Linear Systems in R
- Eigenvalues & Eigenvectors in R
- Singular Value Decomposition in R
- Projections & the Hat Matrix
- QR Decomposition in R
- Quadratic Forms
- Matrix Derivatives & Hessian
- ▼ Statistical Theory
- Exponential Family Distributions
- Sufficient Statistics
- Complete & Ancillary Statistics
- UMVUE (Rao-Blackwell & Lehmann-Scheffé)
- Cramér-Rao Lower Bound
- Asymptotic Theory
- Neyman-Pearson Lemma
- Likelihood Ratio & Pivotal Methods
- Decision Theory
- Asymptotic Relative Efficiency
- ▼ Bayesian Foundations
- Bayes' Theorem
- Bayesian Statistics
- Conjugate Priors
- Grid Approximation
- ▼ MCMC & Stan
- MCMC in R
- Gibbs Sampling
- Hamiltonian Monte Carlo
- Stan
- brms
- ▼ Bayesian Modeling
- Choosing Priors
- Prior Predictive Checks
- Compare Bayesian Models
- Posterior Predictive Checks
- Bayesian Linear Regression
- Bayesian Logistic Regression
- Bayesian Hierarchical Models
- Multilevel Models
- Bayesian ANOVA
- ▼ Machine Learning
- Random Forests (Course)
- Gradient Boosting (Course)
- tidymodels (Course)
- Quiz
- The t-test (Lesson)
▸4. Visualization
- ▼ ggplot2 Foundations
- Grammar of Graphics
- ggplot2 Getting Started
- ggplot2 Aesthetics (aes)
- ggplot2 Colours
- ggplot2 Scales
- ggplot2 Themes
- Labels & Annotations
- ggplot2 Facets
- Quiz
- ▼ Core Charts
- Scatter Plots
- Line Charts
- Bar Charts
- Distribution Charts
- Error Bars
- geom_smooth()
- ▼ Distributions & Groups
- Violin Plot
- Ridgeline Plot
- Lollipop Chart
- ▼ Relationships
- Bubble Chart
- Heatmap in R
- Correlation Matrix
- ▼ Advanced Charts
- Pie & Donut Chart
- Treemap
- Waffle Chart
- ▼ Exploratory Analysis
- EDA (7-Step Framework)
- Univariate EDA
- Bivariate EDA
- Descriptive Statistics
- Correlation Analysis
- ▼ Interactive & Maps
- ggplot2 + plotly Interactive
- Leaflet Interactive Maps
- Spatial Data (sf)
- Choropleth Maps (sf)
- ▼ Customization & Reference
- ggplot2 Legends
- Secondary Axis
- Log Scale
- patchwork (Combine Plots)
- Publication-Ready Figures
- ggplot2 Quickref
- Advanced ggplot2 (Course)
- ggplot2 (Course)
- Interactive Dashboards (Course)
▸5. Time Series
▸6. Advanced R
- ▼ Functional Programming
- Functional Programming
- Quiz
- purrr map() Variants
- R Anonymous Functions
- R Function Factories
- R Function Operators
- Reduce, Filter, Map
- Memoization in R
- Composable R Code
- ▼ OOP in R
- OOP in R: S3/S4/R6
- S3 Classes
- S3 Method Dispatch
- S4 Classes
- S4 Methods & Dispatch
- R6 Classes
- R6 Advanced
- Operator Overloading
- ▼ How R Works
- R Names & Values
- R Assignment Deep Dive
- R Memory & lobstr
- R Environments
- Lexical Scoping
- R Closures
- ▼ Debugging & Performance
- Conditions System
- Debugging R Code
- 50 Common R Errors
- Parallel Computing
- Speedup R Code
- Quiz
▸7. Classic Tutorials
▸8. Practice Exercises

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