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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
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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)
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3.
Statistics
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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
Time Series Analysis
Time Series Forecasting
More Time Series Forecasting
Quiz
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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
R Tutorial (Classic)
ggplot2 Short Tutorial
ggplot2 Tutorial 1 - Intro
ggplot2 Tutorial 2 - Theme
ggplot2 Tutorial 3 - Masterlist
Association Mining
Multi Dimensional Scaling
Optimization
InformationValue Package
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8.
Practice Exercises
▼
Mastery Quizzes (Certificate)
Quiz
Quiz
Quiz
Quiz
Quiz
Quiz
Quiz
Quiz
Quiz
Quiz
Quiz
▼
R Fundamentals
R Basics (15 problems)
R Vectors (12 problems)
R Data Frames (15 problems)
R Lists (10 problems)
R Control Flow (12 problems)
R Functions (10 problems)
R Strings (10 problems)
R Date & Time (10 problems)
R apply Family (12 problems)
R Subsetting (10 problems)
Functional Programming (10 problems)
OOP in R (8 problems)
▼
Data Wrangling
Data Import (10 problems)
dplyr (15 problems)
dplyr filter() & select() (12 problems)
dplyr group_by() & summarise() (10 problems)
dplyr Joins (10 problems)
data.table (12 problems)
purrr (10 problems)
tidyr Reshaping (10 problems)
Missing Data in R (10 problems)
▼
Visualization
ggplot2 (15 problems)
ggplot2 Geoms (12 problems)
ggplot2 Aesthetics (10 problems)
ggplot2 Customization (10 problems)
ggplot2 Facets (8 problems)
R Visualization Project (5 charts)
▼
Statistics
Probability in R Exercises
R Probability Distributions (12 problems)
Binomial Distribution Exercises
Poisson Distribution Exercises
Central Limit Theorem Exercises
Hypothesis Testing Exercises
t-Test Exercises (12 problems)
Chi-Square Exercises (10 problems)
Confidence Interval (10 problems)
Power Analysis Exercises (8 problems)
Nonparametric Exercises (10 problems)
Multiple Testing (8 problems)
Multiple Regression Exercises
Logistic Regression Exercises (10 problems)
Regression Diagnostics Exercises
Ridge & Lasso Exercises
GLM Exercises (10 problems)
ANOVA Exercises (15 problems)
Post-Hoc Tests Exercises (8 problems)
Repeated Measures (8 problems)
Experimental Design Exercises (8 problems)
A/B Testing Exercises (8 problems)
Linear Regression (15 problems)
PCA Exercises (10 problems)
Clustering Exercises (10 problems)
SEM Exercises (8 problems)
A/B Testing Exercises
API Calls Exercises
ARIMA Exercises
Apply Family Exercises
Bayesian Statistics Exercises
Clustering Exercises
Correlation Exercises
Cross Validation Exercises
Data Cleaning Exercises
Data Viz Exercises
Data Wrangling Exercises
Decision Tree Exercises
EDA Exercises
GAM Exercises
Machine Learning Exercises
Mixed Effects Exercises
Network Analysis Exercises
Parallel Computing Exercises
Poisson Regression
Probability Distributions
R Beginner Exercises
R Debugging Exercises
R Markdown Exercises
R Package Development
R Performance Exercises
R for Biostatistics
R for Data Science Exercises
R for Finance Exercises
R for Genomics
R for Healthcare Exercises
R for Marketing Analytics
R for Sports Analytics
Random Forest Exercises
Regex Exercises
Sampling Methods Exercises
Shiny Exercises
Spatial Analysis Exercises
Survey Analysis Exercises
Survival Analysis Exercises
Text Mining Exercises
Time Series Exercises
Web Scraping Exercises
XGBoost Exercises
broom Exercises
caret Exercises
data.table Exercises
dbplyr / SQL Exercises
dplyr Exercises
dplyr group_by Exercises
dplyr Joins Exercises
dplyr Window Functions Exercises
forcats Exercises
ggplot2 Bar Chart Exercises
ggplot2 Color Scales Exercises
ggplot2 Exercises
ggplot2 Facets Exercises
ggplot2 Heatmap Exercises
ggplot2 Themes Exercises
gt Tables Exercises
leaflet Exercises
lubridate Exercises
plotly Exercises
purrr Exercises
readr Exercises
stringr Exercises
testthat Exercises
tidymodels Exercises
tidyr Exercises
tidyr Nest/Unnest Exercises
tidyr Pivot Exercises
Tidyverse Exercises
Date-Time Manipulation Exercises
Loops vs Vectorization Exercises
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Calculators
A/B Test Calculator
t-Test Calculator
Chi-Square Test
Confidence Interval
Bootstrap CI
Effect Size Converter
Power Analysis
Survival Power
Type I / II Error
Z-Score & Percentile
Equivalence / NI
Outlier Detection
ROC / AUC
▼
Bayesian
Bayes Theorem
Bayes Factor
▼
Interpreters
lm() Output
glm() Output
ANOVA Output
VIF / Multicollinearity
Confusion Matrix
Diagnostic Plots
▼
Pickers
Normality Test
Non-Parametric Test
Multiple Testing
▼
Time series
TS Stationarity
▼
Utilities
DAG Confounder Picker
Reprex Builder
Assessing clusterability
Methods such as k-Means and
Hierarchical clustering
would cluster any random data even if there isn’t any inherent clusters present in the data.
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