About R Programming Language

R is a programming language and environment for statistical computing and graphics. It is a free and open-source software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among data scientists, statisticians, and other researchers for data analysis and developing statistical software.

Articles

R Programming for Data Science

Introduction

• Build R
• R and CRAN
• Use
• Installation
• Directory
• History of R
• Features
• Free Software
• Drawbacks
• Design 

Rstudio

• About
• Installation
• Guide

Package

• R package
• Repositories
• Right Packages
• Installations
• Load Packages
• Updating, removing, unloading packages
• Package functions
• readr Package

Project

• R Project
• Benefits
• Create
• Open
• Quit

Markdown

• R Markdown
• Use
• Installation
• Start
• Create
• Knitting Documents
• R Markdown commands

S language

• About
• Dialect

Basic

• Entering Input and Comment
• Evaluation and Integer Sequences
• Objects and Empty Vector
• Numbers
• Attributes
• Data Types
• Creating Vectors
• Mixing Objects
• Explicit Coercion
• Matrices
• Lists
• Factors
• Missing Values
• Data Frames
• Names

Data

• Reading and Writing Data
• Reading Data Files with read.table()
• Reading in Larger Datasets with read.table
• Calculating Memory Requirements for R Objects

Coding Standards

• Coding Standards for R
• Indenting

Textual and Binary Formats

• Textual and Binary Formats for Storing Data
• dput() and dump()
• Binary Formats

Connections 

• Interfaces to the Outside World
• File Connections
• Reading Lines of a Text File
• Reading From a URL Connection

Subsetting 

• R Objects
• Vector
• Matrix
• Lists
• Nested Elements of a List
• Extracting Multiple Elements of a List
• Partial Matching
• Removing NA Values

Vectorized

• Vectorized Operations
• Vectorized Matrix Operations

Data and Time

• Dates and Times
• Dates in R
• Times in R
• Operations on Dates and Times

Control Structures

• Control Structures
• if-else
• For loops
• Nested for loops
• While loops
• repeat Loops
• next, break
• return

Functions

• Functions
• Functions in R
• Your First Function
• Argument Matching
• Lazy Evaluation
• The ... Argument
• Arguments Coming After the ... Argument

Scoping Rules

• Scoping Rules of R
• A Diversion on Binding Values to Symbol
• Lexical Scoping
• Lexical vs. Dynamic Scoping
• Application of lexical scoping in optimization
• Plotting the Likelihood
• Binding Values to Symbol
• A Function Closure
• Lexical Scoping in Other Languages
• Maximizing a Normal Likelihood
• Estimating Parameters

Loop

• Loop Functions
• Looping on the Command Line
• lapply()
• sapply()
• split()
• Splitting a Data Frame
• tapply()
• apply()
• Col/Row Sums and Means
• mapply()
• Vectorizing a Function
• Instant Vectorization Instant
• Splitting on More than One Level

Debugging

• Something’s Wrong!
• Figuring Out What’s Wrong
• Debugging Tools in R
• traceback()
• debug()
• recover()

 Profiling R Code

• Why is My Code So Slow in R?
• On Optimizing Your Code
• General Principles of Optimization
• system.time()
• Timing Longer Expressions
• The R Profiler
• summaryRprof()

Simulation

• Simulation
• Generating Random Numbers
• Setting the random number seed
• Simulating a Linear Model
• Random Sampling
• str Function

Data Frames

• Managing with the dplyr package
• Data Frames
• dplyr Package
• dplyr Grammar
• Common dplyr Function Properties
• Installation
• select()
• filter()
• arrange()
• rename()
• mutate()
• group_by()
• %>%

Regular Expressions

• Regular Expressions
• Primary R Functions
• grep()
• grepl()
• regexpr()
• sub() and gsub()
• regexec()
• stringr Package

Parallel Computation

• Parallel Computation
• Hidden Parallelism
• Embarrassing Parallelism
• Parallel Package
• Bootstrapping a Statistic
• Building a Socket Cluster