r eBook

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Chapters

Chapter 1: Getting started with R Language
Chapter 2: *apply family of functions (functionals)
Chapter 3: .Rprofile
Chapter 4: Aggregating data frames
Chapter 5: Analyze tweets with R
Chapter 6: ANOVA
Chapter 7: Arima Models
Chapter 8: Arithmetic Operators
Chapter 9: Bar Chart
Chapter 10: Base Plotting
Chapter 11: Bibliography in RMD
Chapter 12: boxplot
Chapter 13: caret
Chapter 14: Classes
Chapter 15: Cleaning data
Chapter 16: Code profiling
Chapter 17: Coercion
Chapter 18: Color schemes for graphics
Chapter 19: Column wise operation
Chapter 20: Combinatorics
Chapter 21: Control flow structures
Chapter 22: Creating packages with devtools
Chapter 23: Creating reports with RMarkdown
Chapter 24: Creating vectors
Chapter 25: Data acquisition
Chapter 26: Data frames
Chapter 27: data.table
Chapter 28: Date and Time
Chapter 29: Date-time classes (POSIXct and POSIXlt)
Chapter 30: Debugging
Chapter 31: Distribution Functions
Chapter 32: dplyr
Chapter 33: Expression: parse + eval
Chapter 34: Extracting and Listing Files in Compressed Archives
Chapter 35: Factors
Chapter 36: Fault-tolerant/resilient code
Chapter 37: Feature Selection in R -- Removing Extraneous Features
Chapter 38: Formula
Chapter 39: Fourier Series and Transformations
Chapter 40: Functional programming
Chapter 41: Generalized linear models
Chapter 42: Get user input
Chapter 43: ggplot2
Chapter 44: GPU-accelerated computing
Chapter 45: Hashmaps
Chapter 46: heatmap and heatmap.2
Chapter 47: Hierarchical clustering with hclust
Chapter 48: Hierarchical Linear Modeling
Chapter 49: I/O for database tables
Chapter 50: I/O for foreign tables (Excel, SAS, SPSS, Stata)
Chapter 51: I/O for geographic data (shapefiles, etc.)
Chapter 52: I/O for raster images
Chapter 53: I/O for R's binary format
Chapter 54: Implement State Machine Pattern using S4 Class
Chapter 55: Input and output
Chapter 56: Inspecting packages
Chapter 57: Installing packages
Chapter 58: Introduction to Geographical Maps
Chapter 59: Introspection
Chapter 60: JSON
Chapter 61: Linear Models (Regression)
Chapter 62: Lists
Chapter 63: lubridate
Chapter 64: Machine learning
Chapter 65: Matrices
Chapter 66: Meta: Documentation Guidelines
Chapter 67: Missing values
Chapter 68: Modifying strings by substitution
Chapter 69: Natural language processing
Chapter 70: Network analysis with the igraph package
Chapter 71: Non-standard evaluation and standard evaluation
Chapter 72: Numeric classes and storage modes
Chapter 73: Object-Oriented Programming in R
Chapter 74: Parallel processing
Chapter 75: Pattern Matching and Replacement
Chapter 76: Performing a Permutation Test
Chapter 77: Pipe operators (%>% and others)
Chapter 78: Pivot and unpivot with data.table
Chapter 79: Probability Distributions with R
Chapter 80: Publishing
Chapter 81: R code vectorization best practices
Chapter 82: R in LaTeX with knitr
Chapter 83: R Markdown Notebooks (from RStudio)
Chapter 84: R memento by examples
Chapter 85: Random Forest Algorithm
Chapter 86: Random Numbers Generator
Chapter 87: Randomization
Chapter 88: Raster and Image Analysis
Chapter 89: Rcpp
Chapter 90: Reading and writing strings
Chapter 91: Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
Chapter 92: Recycling
Chapter 93: Regular Expression Syntax in R
Chapter 94: Regular Expressions (regex)
Chapter 95: Reproducible R
Chapter 96: Reshape using tidyr
Chapter 97: Reshaping data between long and wide forms
Chapter 98: RESTful R Services
Chapter 99: RMarkdown and knitr presentation
Chapter 100: RODBC
Chapter 101: roxygen2
Chapter 102: Run-length encoding
Chapter 103: Scope of variables
Chapter 104: Set operations
Chapter 105: Shiny
Chapter 106: Solving ODEs in R
Chapter 107: Spark API (SparkR)
Chapter 108: spatial analysis
Chapter 109: Speeding up tough-to-vectorize code
Chapter 110: Split function
Chapter 111: sqldf
Chapter 112: Standardize analyses by writing standalone R scripts
Chapter 113: String manipulation with stringi package
Chapter 114: strsplit function
Chapter 115: Subsetting
Chapter 116: Survival analysis
Chapter 117: Text mining
Chapter 118: The character class
Chapter 119: The Date class
Chapter 120: The logical class
Chapter 121: tidyverse
Chapter 122: Time Series and Forecasting
Chapter 123: Updating R and the package library
Chapter 124: Updating R version
Chapter 125: Using pipe assignment in your own package %<>%: How to ?
Chapter 126: Using texreg to export models in a paper-ready way
Chapter 127: Variables
Chapter 128: Web Crawling in R
Chapter 129: Web scraping and parsing
Chapter 130: Writing functions in R
Chapter 131: xgboost

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Learning R Language eBook (PDF)