R Notes for Professionals

GoalKicker.com, 2018. — 474 p.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.
Book created for educational purposes and is not affiliated with R group(s), company(s) nor Stack Overflow. All trademarks belong to their respective company owners.Getting started with R Language
Variables
Arithmetic Operators
Matrices
Formula
Reading and writing strings
String manipulation with stringi package
Classes
Lists
Hashmaps
Creating vectors
Date and Time
The Date class
Date-time classes (POSIXct and POSIXlt)
The character class
Numeric classes and storage modes
The logical class
Data frames
Split function
Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
Pipe operators (%>% and others)
Linear Models (Regression)
data.table
Pivot and unpivot with data.table
Bar Chart
Base Plotting
boxplot
ggplot2
Factors
Pattern Matching and Replacement
Run-length encoding
Speeding up tough-to-vectorize code
Introduction to Geographical Maps
Set operations
tidyverse
Rcpp
Random Numbers Generator
Parallel processing
Subsetting
Debugging
Installing packages
Inspecting packages
Creating packages with devtools
Using pipe assignment in your own package %<>%: How to?
Arima Models
Distribution Functions
Shiny
spatial analysis
sqldf
Code profiling
Control flow structures
Column wise operation
JSON
RODBC
lubridate
Time Series and Forecasting
strsplit function
Web scraping and parsing
Generalized linear models
Reshaping data between long and wide forms
RMarkdown and knitr presentation
Scope of variables
Performing a Permutation Test
xgboost
R code vectorization best practices
Missing values
Hierarchical Linear Modeling
*apply family of functions (functionals)
Text mining
ANOVA
Raster and Image Analysis
Survival analysis
Fault-tolerant/resilient code
Reproducible R
Fourier Series and Transformations
.Rprofile
dplyr
caret
Extracting and Listing Files in Compressed Archives
Probability Distributions with R
R in LaTeX with knitr
Web Crawling in R
Creating reports with RMarkdown
GPU-accelerated computing
heatmap and heatmap.2
Network analysis with the igraph package
Functional programming
Get user input
Spark API (SparkR)
Meta: Documentation Guidelines
Input and output
I/O for foreign tables (Excel, SAS, SPSS, Stata)
I/O for database tables
I/O for geographic data (shapefiles, etc.)
I/O for raster images
I/O for R's binary format
Recycling
Expression: parse + eval
Regular Expression Syntax in R
Regular Expressions (regex)
Combinatorics
Solving ODEs in R
Feature Selection in R -- Removing Extraneous Features
Bibliography in RMD
Writing functions in R
Color schemes for graphics
Hierarchical clustering with hclust
Random Forest Algorithm
RESTful R Services
Machine learning
Using texreg to export models in a paper-ready way
Publishing
Implement State Machine Pattern using S4 Class
Reshape using tidyr
Modifying strings by substitution
Non-standard evaluation and standard evaluation
Randomization
Object-Oriented Programming in R
Coercion
Standardize analyses by writing standalone R scripts
Analyze tweets with R
Natural language processing
R Markdown Notebooks (from RStudio)
Aggregating data frames
Data acquisition
R memento by examples
Updating R version