Book Image

Learning R Programming

By : Kun Ren
Book Image

Learning R Programming

By: Kun Ren

Overview of this book

R is a high-level functional language and one of the must-know tools for data science and statistics. Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language. Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as well as the most popular R packages to boost your productivity from the offset. Start with the basics of R, then dive deep into the programming techniques and paradigms to make your R code excel. Advance quickly to a deeper understanding of R's behavior as you learn common tasks including data analysis, databases, web scraping, high performance computing, and writing documents. By the end of the book, you'll be a confident R programmer adept at solving problems with the right techniques.

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Learning R Programming
Kun Ren
Oct, 2016 | 582 pages
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Table of Contents (21 chapters)
Learning R Programming
Learning R Programming
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Quick Start
Quick Start
Introducing R
The need for R
Installing R
RStudio
A quick example
Summary
2
Basic Objects
Basic Objects
Vector
Matrix
Array
Lists
Data frames
Functions
Summary
3
Managing Your Workspace
Managing Your Workspace
R's working directory
Inspecting the environment
Modifying global options
Managing the library of packages
Summary
4
Basic Expressions
Basic Expressions
Assignment expressions
Conditional expressions
Loop expressions
Summary
5
Working with Basic Objects
Working with Basic Objects
Using object functions
Using logical functions
Using math functions
Applying numeric methods
Using statistical functions
Using apply-family functions
Summary
6
Working with Strings
Working with Strings
Getting started with strings
Formatting date/time
Using regular expressions
Summary
7
Working with Data
Working with Data
Reading and writing data
Visualizing data
Analyzing data
Summary
8
Inside R
Inside R
Understanding lazy evaluation
Understanding the copy-on-modify mechanism
Understanding lexical scoping
Understanding how an environment works
Summary
9
Metaprogramming
Metaprogramming
Understanding functional programming
Computing on language
Summary
10
Object-Oriented Programming
Object-Oriented Programming
Introducing object-oriented programming
Working with the S3 object system
Working with S4
Working with the reference class
Working with R6
Summary
11
Working with Databases
Working with Databases
Working with relational databases
Working with NoSQL databases
Summary
12
Data Manipulation
Data Manipulation
Using built-in functions to manipulate data frames
Using SQL to query data frames via the sqldf package
Using data.table to manipulate data
Using dplyr pipelines to manipulate data frames
Using rlist to work with nested data structures
Summary
13
High-Performance Computing
High-Performance Computing
Understanding code performance issues
Profiling code
Boosting code performance
Summary
14
Web Scraping
Web Scraping
Looking inside web pages
Extracting data from web pages using CSS selectors
Learning XPath selectors
Analysing HTML code and extracting data
Summary
15
Boosting Productivity
Boosting Productivity
Writing R Markdown documents
Creating interactive apps
Summary

Reading and writing data

The first step in any kind of data analysis in R is to load data, that is, to import a dataset into the environment. Before that, we have to figure out the type of data file and choose appropriate tools to read the data.

Reading and writing text-format data in a file

Among all the file types used to store data, perhaps the most widely used one is CSV. In a typical CSV file, the first line is the header of columns, and each subsequent line represents a data record with columns separated by commas. Here is an example of student records written in this format:

Name,Gender,Age,Major
Ken,Male,24,Finance
Ashley,Female,25,Statistics
Jennifer,Female,23,Computer Science

Importing data via RStudio IDE

RStudio provides an interactive way to import data. You can navigate to Tools | Import Dataset | From Local File and choose a local file in a text format, such as .csv and .txt. Then, you can adjust the parameters and preview the resulting data frame:

Note that you should check Strings...

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