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The R Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. ### Table of Contents

### Book Details

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This open book is licensed under a Creative Commons License (CC BY-SA). You can download R Notes for Professionals ebook for free in PDF format (6.6 MB).

Chapter 1

Getting started with R Language

Chapter 2

Variables

Chapter 3

Arithmetic Operators

Chapter 4

Matrices

Chapter 5

Formula

Chapter 6

Reading and writing strings

Chapter 7

String manipulation with stringi package

Chapter 8

Classes

Chapter 9

Lists

Chapter 10

Hashmaps

Chapter 11

Creating vectors

Chapter 12

Date and Time

Chapter 13

The Date class

Chapter 14

Date-time classes (POSIXct and POSIXlt)

Chapter 15

The character class

Chapter 16

Numeric classes and storage modes

Chapter 17

The logical class

Chapter 18

Data frames

Chapter 19

Split function

Chapter 20

Reading and writing tabular data in plain-text files (CSV, TSV, etc.)

Chapter 21

Pipe operators (%>% and others)

Chapter 22

Linear Models (Regression)

Chapter 23

data.table

Chapter 24

Pivot and unpivot with data.table

Chapter 25

Bar Chart

Chapter 26

Base Plotting

Chapter 27

boxplot

Chapter 28

ggplot2

Chapter 29

Factors

Chapter 30

Pattern Matching and Replacement

Chapter 31

Run-length encoding

Chapter 32

Speeding up tough-to-vectorize code

Chapter 33

Introduction to Geographical Maps

Chapter 34

Set operations

Chapter 35

tidyverse

Chapter 36

Rcpp

Chapter 37

Random Numbers Generator

Chapter 38

Parallel processing

Chapter 39

Subsetting

Chapter 40

Debugging

Chapter 41

Installing packages

Chapter 42

Inspecting packages

Chapter 43

Creating packages with devtools

Chapter 44

Using pipe assignment in your own package %<>%: How to ?

Chapter 45

Arima Models

Chapter 46

Distribution Functions

Chapter 47

Shiny

Chapter 48

spatial analysis

Chapter 49

sqldf

Chapter 50

Code profiling

Chapter 51

Control flow structures

Chapter 52

Column wise operation

Chapter 53

JSON

Chapter 54

RODBC

Chapter 55

lubridate

Chapter 56

Time Series and Forecasting

Chapter 57

strsplit function

Chapter 58

Web scraping and parsing

Chapter 59

Generalized linear models

Chapter 60

Reshaping data between long and wide forms

Chapter 61

RMarkdown and knitr presentation

Chapter 62

Scope of variables

Chapter 63

Performing a Permutation Test

Chapter 64

xgboost

Chapter 65

R code vectorization best practices

Chapter 66

Missing values

Chapter 67

Hierarchical Linear Modeling

Chapter 68

*apply family of functions (functionals)

Chapter 69

Text mining

Chapter 70

ANOVA

Chapter 71

Raster and Image Analysis

Chapter 72

Survival analysis

Chapter 73

Fault-tolerant/resilient code

Chapter 74

Reproducible R

Chapter 75

Fourier Series and Transformations

Chapter 76

.Rprofile

Chapter 77

dplyr

Chapter 78

caret

Chapter 79

Extracting and Listing Files in Compressed Archives

Chapter 80

Probability Distributions with R

Chapter 81

R in LaTeX with knitr

Chapter 82

Web Crawling in R

Chapter 83

Creating reports with RMarkdown

Chapter 84

GPU-accelerated computing

Chapter 85

heatmap and heatmap.2

Chapter 86

Network analysis with the igraph package

Chapter 87

Functional programming

Chapter 88

Get user input

Chapter 89

Spark API (SparkR)

Chapter 90

Meta: Documentation Guidelines

Chapter 91

Input and output

Chapter 92

I/O for foreign tables (Excel, SAS, SPSS, Stata)

Chapter 93

I/O for database tables

Chapter 94

I/O for geographic data (shapefiles, etc.)

Chapter 95

I/O for raster images

Chapter 96

I/O for R's binary format

Chapter 97

Recycling

Chapter 98

Expression: parse + eval

Chapter 99

Regular Expression Syntax in R

Chapter 100

Regular Expressions (regex)

Chapter 101

Combinatorics

Chapter 102

Solving ODEs in R

Chapter 103

Feature Selection in R -- Removing Extraneous Features

Chapter 104

Bibliography in RMD

Chapter 105

Writing functions in R

Chapter 106

Color schemes for graphics

Chapter 107

Hierarchical clustering with hclust

Chapter 108

Random Forest Algorithm

Chapter 109

RESTful R Services

Chapter 110

Machine learning

Chapter 111

Using texreg to export models in a paper-ready way

Chapter 112

Publishing

Chapter 113

Implement State Machine Pattern using S4 Class

Chapter 114

Reshape using tidyr

Chapter 115

Modifying strings by substitution

Chapter 116

Non-standard evaluation and standard evaluation

Chapter 117

Randomization

Chapter 118

Object-Oriented Programming in R

Chapter 119

Coercion

Chapter 120

Standardize analyses by writing standalone R scripts

Chapter 121

Analyze tweets with R

Chapter 122

Natural language processing

Chapter 123

R Markdown Notebooks (from RStudio)

Chapter 124

Aggregating data frames

Chapter 125

Data acquisition

Chapter 126

R memento by examples

Chapter 127

Updating R version

Subject

Computer Science

Publisher

GoalKicker

Published

2018

Pages

474

Edition

1

Language

English

PDF Size

6.6 MB

License

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