R Notes for Professionals book

R Notes for Professionals book

R Notes

The R Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. Text content is released under Creative Commons BY-SA. See credits at the end of this book whom contributed to the various chapters. Images may be copyright of their respective owners unless otherwise specified

475 pages, published on April 2018

Chapters

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