FREE DOWNLOAD

R Notes for Professionals book (FREE DOWNLOAD)

R Notes for Professionals book

R Notes for Professionals book (FREE DOWNLOAD)

DESCRIPTION

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

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