r programming pdf book notes download for free


About Book

This book has 400+ pages that cover the basic to advanced topic of R programming with practical program and code examples. if you want to learn R programming then this book is for you. this book is developed by professional programmers for educational purposes.


Book contents

  1. Getting started with r programming
  2. variables
  3. arithmetic operators
  4. matrices
  5. formula
  6. reading and writing strings
  7. string manipulation with string packages
  8. classes
  9. lists
  10. hashmaps
  11. creating ectos
  12. date and time
  13. the date class
  14. date time classes
  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
  21. pipe operators
  22. linear models
  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 touch to vectorize code
  33. introduction to geographical maps
  34. set operations
  35. tidyverse
  36. pp
  37. random numbers generator
  38. parallel processing
  39. subsetting
  40. debugging
  41. installing packages
  42. inspecting packages
  43. creating packages with dev tools
  44. using pipe assignment in your own package
  45. aria 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. lubricate
  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. markdown and knitr presentation
  62. scope of variables
  63. performing a permutation test
  64. boost
  65. r code vectorization best practices
  66. missing values
  67. hierarchical linear modeling
  68. apply famil of functions
  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. profile 
  77. dplyr
  78. caret
  79. extracting and listing files in compressed archives
  80. probability distributions with r
  81. r in latex with knitting
  82. web crawling in r
  83. creating reports with markdown
  84. GPU accelerated computing
  85. heatmap and heatmap2
  86. network analysis with the igraph package
  87. functional programming
  88. get user input
  89. spark API
  90. meta documentation guidelines
  91. input and output
  92. input-output for foreign tables
  93. input-output for geographic data
  94. input-output for raster images
  95. input-output for r's binary format
  96. recycling
  97. expression
  98. regular expression syntax in r
  99. regular expressions
  100. combinators
  101. solving odes in r
  102. feature selection in r
  103. bibliography in red
  104. writing functions in r
  105. color schemes for graphics
  106. hierarchical clustering with cluster
  107. random forest algorithm
  108. restful r services
  109. machine learning
  110. using texreg to export models in a paper ready way
  111. publishing
  112. implement state machine pattern using s4 class
  113. reshape using tidyr
  114. modifying strings by substitution
  115. nonstandard evaluation ad standard evaluation
  116. randomization
  117. object-oriented programming in r
  118. coercion
  119. standardize analyses by writing standalone r scripts
  120. analyze tweets with r
  121. natural language processing
  122. r markdown notebooks
  123. aggregating data frames
  124. data acquisition
  125. r memento by examples
  126. updating r version