learnR

Introduction

learnR is a tutorial for R programming language from basic to advanced levels. It includes basic concepts, motivations, principles and their application in data transformation, data analysis, data mining, statistical computing, financial computing, etc.

Prerequesites

1. R
2. Rtools
3. Rstudio

Contents

A: Basic R Programming

1. Introduction to R programming language
2. RStudio IDE
3. Basic objects
   • Vector (Numeric, Integer, Complex, Logical, Character)
   • Matrix
   • Array
   • List
   • Data frame
   • Function
   • Formula
4. Basic expressions
   • Assignment expression (<-, <<-)
   • Conditional expression (if else)
   • Loop expression (for, while)
5. Basic functions
   • Environment functions
   • Package functions
   • Object functions
   • Logical functions
   • Character functions
   • Math functions
   • Statistical functions
   • Data manipulation (data read/write, transformation)
   • Higher-order functions
   • Optimization functions
   • Anonymous functions
   • Meta-functions
   • Plot functions
6. Debugging in RStudio
7. Essential statistics
   • Preparing data
   • Descriptive statistics
   • Linear regression
   • Statistical hypothesis testing
   • Model analysis
   • Time series model fit
8. Essential data mining
   • Using models
   • Cross validation
9. Design patterns

B: Advanced R Programming

1. R language mechanism
   • Lazy evaluation
   • Dynamic scoping
   • Object searching
   • Memory management
   • ...
   • Functions
   • Environment
   • Expression
   • Call
2. Data structures
   • S3 object
   • S4 object
3. Database
   • SQL
   • Read/Write Excel Workbook via {RODBC}
   • Read/Write SQLite database via {RSQLite}
   • Use SQL to query data frames
4. Parallel computing
   • {parallel}
   • {parallelMap}
   • {doParallel} + {foreach}
   • {doParallel} + {plyr}
5. Functional programming
   • Anonymous functions
   • Closures
   • Higher order functions
6. Profiling
   • Computing time tracking
   • Memory use tracking

C: Popular packages

1. Popular packages
2. Read/Write JSON ({jsonlite})
3. Process strings ({stringr})
4. Transform data frame between long and wide formats ({reshape2})
5. Iterate over vector, list, and data frame ({plyr})
6. Handy data frame transformation ({dplyr})
7. Nonlinear root finding ({rootSolve})
8. Nonlinear Optimization ({Rsolnp})
9. Integrate R with C++ ({Rcpp})
10. R Markdown Documenting ({rmarkdown})

D: Data Visualization

1. Basic plots
   • Scatter/line/bar/pie charts
   • Composing plots
   • Partitioning plots
   • Graphics devices
   • Interactive graphics
2. {ggplot2}

E1: Exercises

E2: Appendix