R Snippets

This is not an R tutorial or book. This is a handy collection of snippets of R code for achieving various statistical analysis and visualization tasks. If you wish to learn R from beginning, try the books listed on www.bookdown.org. However, if you are familiar with R and want to quickly refresh your memory or look for code for specific tasks, you can refer to these notes.

• 1. R Scripting Environment
  • 1.1. Getting Help
  • 1.2. Workspace
  • 1.3. Arithmetic
  • 1.4. Variables
  • 1.5. Data Types
• 2. Core Data Types
  • 2.1. Vectors
  • 2.2. Matrices
  • 2.3. Arrays
  • 2.4. Lists
  • 2.5. Factors
  • 2.6. Data Frames
  • 2.7. Time Series
• 3. Language Facilities
  • 3.1. Operator precedence rules
  • 3.2. Expressions
  • 3.3. Flow Control
  • 3.4. Functions
  • 3.5. Packages
  • 3.6. R Scripts
  • 3.7. Logical Tests
  • 3.8. Introspection
  • 3.9. Coercion
  • 3.10. Sorting and Searching
  • 3.11. Basic Mathematical Functions
  • 3.12. Built-in Constants
  • 3.13. Converting Numerical Data to Factor
  • 3.14. Apply Family of Functions
  • 3.15. Missing Data
  • 3.16. Classes
  • 3.17. Defining our own operators
• 4. File System and Simple IO
  • 4.1. Printing
• 5. Text Processing
  • 5.1. Strings or Character Vectors
  • 5.2. Pattern Matching and Replacement
• 6. Data Manipulation
  • 6.1. Basic Data Manipulation
  • 6.2. dplyr
• 7. Linear Algebra
  • 7.1. Matrix Properties
  • 7.2. Linear Equations
  • 7.3. Eigen Value Decomposition
  • 7.4. Singular Value Decomposition
  • 7.5. QR Decomposition
  • 7.6. Cholesky Decomposition
• 8. Real Analysis
  • 8.1. Metric Spaces and Distances
• 9. Discrete Mathematics
  • 9.1. Permutations and Combinations
  • 9.2. References
• 10. Probability
  • 10.1. Random Numbers
  • 10.2. Coin Tossing
  • 10.3. Sampling Data
  • 10.4. Normal Distribution
  • 10.5. Hyper-Geometric Distribution
  • 10.6. Discrete Distributions
  • 10.7. Standard Probability Distributions
  • 10.8. Kernel Density Estimation
• 11. Statistics
  • 11.1. Basic Statistics
  • 11.2. Statistical Tests
  • 11.3. Maximum Likelihood Estimation
• 12. Graphics
  • 12.1. Simple Charts
  • 12.2. GGPlot2
  • 12.3. Special Charts
  • 12.4. Parameters
• 13. Optimization
  • 13.1. Minimization, Maximization
  • 13.2. Least Squares
• 14. Statistical Models
  • 14.1. Linear Models
• 15. Clustering
  • 15.1. K-Means Clustering
• 16. Utilities
  • 16.1. Date Time
• 17. Object Oriented Programming
  • 17.1. S3 Classes
  • 17.2. S4 Classes
• 18. R Packages
  • 18.1. Standard Packages
  • 18.2. Contributed Packages
• 19. Data Sets
  • 19.1. Standard Data Sets
  • 19.2. Public Data Sets
  • 19.3. Small Data Sets
  • 19.4. Test Data
• 20. Tips
  • 20.1. Exploratory data analysis
• 21. Kaggle
  • 21.1. References

Indices and tables

• Index
• Search Page