【2015新书】 R Programming for Data Science

Book 图书名称： R Programming for Data Science
Author 作者：Roger D. Peng
Publisher 出版社：Leanpub
Page 页数：132
Publishing Date 出版时间：April, 2015
Language 语言：English
Size 大小：MB
Format 格式：pdf文字版
ISBN:na/
Edition: 第1版搜索过论坛，没有该文档

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science.

== Table of contents ==
Preface
History and Overview of R
What is R?
What is S?
The S Philosophy
Back to R
Basic Features of R
Free Software
Design of the R System
Limitations of R
R Resources
Getting Started with R
Installation
Getting started with the R interface
R Nuts and Bolts
Entering Input
Evaluation
R Objects
Numbers
Attributes
Creating Vectors
Mixing Objects
Explicit Coercion
Matrices
Lists
Factors
Missing Values
Data Frames
Names
Summary
Getting Data In and Out of R
Reading and Writing Data
Reading Data Files with read.table()
Reading in Larger Datasets with read.table
Calculating Memory Requirements for R Objects
Using Textual and Binary Formats for Storing Data
Using dput() and dump()
Binary Formats
Interfaces to the Outside World
File Connections
Reading Lines of a Text File
Reading From a URL Connection
Subsetting R Objects
Subsetting a Vector
Subsetting a Matrix
Subsetting Lists
Subsetting Nested Elements of a List
Extracting Multiple Elements of a List
Partial Matching
Removing NA Values
Vectorized Operations
Vectorized Matrix Operations
Dates and Times
Dates in R
Times in R
Operations on Dates and Times
Summary
Control Structures
if-else
for Loops
Nested for loops
while Loops
repeat Loops
next, break
Summary
Functions
Functions in R
Your First Function
Argument Matching
Lazy Evaluation
The ... Argument
Arguments Coming After the ... Argument
Summary
Scoping Rules of R
A Diversion on Binding Values to Symbol
Scoping Rules
Lexical Scoping: Why Does It Matter?
Lexical vs. Dynamic Scoping
Application: Optimization
Plotting the Likelihood
Summary
Coding Standards for R
Loop Functions
Looping on the Command Line
lapply()
sapply()
split()
Splitting a Data Frame
tapply
apply()
Col/Row Sums and Means
Other Ways to Apply
mapply()
Vectorizing a Function
Summary
Debugging
Something’s Wrong!
Figuring Out What’s Wrong
Debugging Tools in R
Using traceback()
Using debug()
Using recover()
Summary
Profiling R Code
Using system.time()
Timing Longer Expressions
The R Profiler
Using summaryRprof()
Summary
Simulation
Generating Random Numbers
Setting the random number seed
Simulating a Linear Model
Random Sampling
Summary
Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S.
Synopsis
Loading and Processing the Raw Data
Results

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