4. Basic Expressions | Learning R Programming

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Learning R Programming

By : Ren

Learning R Programming

By: 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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Quick Start

1. Quick Start

Introducing R

The need for R

Installing R

RStudio

A quick example

Summary

2. Basic Objects

2. Basic Objects

Vector

Matrix

Array

Lists

Data frames

Functions

Summary

3. Managing Your Workspace

3. Managing Your Workspace

R's working directory

Inspecting the environment

Modifying global options

Managing the library of packages

Summary

4. Basic Expressions

4. Basic Expressions

Assignment expressions

Conditional expressions

Loop expressions

Summary

5. Working with Basic Objects

5. 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

6. Working with Strings

Getting started with strings

Formatting date/time

Using regular expressions

Summary

7. Working with Data

7. Working with Data

Reading and writing data

Visualizing data

Analyzing data

Summary

8. Inside R

8. Inside R

Understanding lazy evaluation

Understanding the copy-on-modify mechanism

Understanding lexical scoping

Understanding how an environment works

Summary

9. Metaprogramming

9. Metaprogramming

Understanding functional programming

Computing on language

Summary

10. Object-Oriented Programming

10. 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

11. Working with Databases

Working with relational databases

Working with NoSQL databases

Summary

12. Data Manipulation

12. 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

13. High-Performance Computing

Understanding code performance issues

Profiling code

Boosting code performance

Summary

14. Web Scraping

14. 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

15. Boosting Productivity

Writing R Markdown documents

Creating interactive apps

Summary

Assignment expressions

Assignment may be one of the most fundamental expressions in all programming languages. What it does is assign or bind a value to a symbol so that we can refer to the value by that symbol later.

Despite the similarity, R adopts the <- operator to perform assignment. This is a bit different from many other languages using = although this is also allowed in R:

x <- 1 
y <- c(1, 2, 3) 
z <- list(x, y)

We don't have to declare the symbol and its type before assigning a value to it. If a symbol does not exist in the environment, the assignment will create that symbol. If a symbol already exists, the assignment will not end up in conflict, but will rebind the new value to that symbol.

Alternative assignment operators

There are some alternate yet equivalent operators we can use. Compared to x <- f(z), which binds the value of f(z) to symbol x, we can also use -> to perform assignment in the opposite direction:

2 -> x1

We can even chain...

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