UsefulLinks
Statistics
Statistics with R
1. Introduction to R and Statistical Computing
2. R Fundamentals and Basic Operations
3. R Package System
4. R Data Structures
5. Data Import and Export
6. Data Cleaning and Preprocessing
7. Data Manipulation with dplyr
8. Descriptive Statistics
9. Data Visualization Fundamentals
10. Advanced Data Visualization with ggplot2
11. Probability Theory
12. Probability Distributions
13. Sampling and Sampling Distributions
14. Statistical Inference Foundations
15. Hypothesis Testing Framework
16. One-Sample Tests
17. Two-Sample Tests
18. Chi-squared Tests
19. Analysis of Variance (ANOVA)
20. Correlation Analysis
21. Simple Linear Regression
22. Regression Diagnostics
23. Multiple Linear Regression
24. Generalized Linear Models
25. Nonparametric Statistics
26. Introduction to Time Series Analysis
27. Introduction to Machine Learning
28. Reproducible Research
29. Statistical Computing Best Practices
3.
R Package System
3.1.
Understanding Packages
3.1.1.
What are R Packages
3.1.2.
Package Structure
3.1.3.
Package Dependencies
3.1.4.
Package Namespaces
3.2.
Installing Packages
3.2.1.
install.packages() Function
3.2.2.
Installing from CRAN
3.2.3.
Installing from GitHub
3.2.4.
Installing from Local Files
3.2.5.
Package Version Management
3.3.
Loading and Using Packages
3.3.1.
library() Function
3.3.2.
require() Function
3.3.3.
Differences Between library() and require()
3.3.4.
Package Conflicts
3.3.5.
Detaching Packages
3.4.
Package Management
3.4.1.
Updating Packages
3.4.2.
Removing Packages
3.4.3.
Checking Installed Packages
3.4.4.
Package Dependencies
3.5.
The Tidyverse Ecosystem
3.5.1.
Overview of Tidyverse Philosophy
3.5.2.
Core Tidyverse Packages
3.5.3.
Installing Tidyverse
3.5.4.
Loading Tidyverse
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4. R Data Structures