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Modern R Programming Cookbook

By : Jaynal Abedin

Buy this Book

Modern R Programming Cookbook

By: Jaynal Abedin

Buy this Book

Overview of this book

R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 2,000 available add-ons, and R is a serious rival to all commercial statistical packages. The objective of this book is to show how to work with different programming aspects of R. The emerging R developers and data science could have very good programming knowledge but might have limited understanding about R syntax and semantics. Our book will be a platform develop practical solution out of real world problem in scalable fashion and with very good understanding. You will work with various versions of R libraries that are essential for scalable data science solutions. You will learn to work with Input / Output issues when working with relatively larger dataset. At the end of this book readers will also learn how to work with databases from within R and also what and how meta programming helps in developing applications.

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

Installing and Configuring R and its Libraries

Introduction

Installing and configuring base R in Windows

Installing and configuring base R in Linux

Installing and configuring RStudio IDE in Windows

Installing and configuring RStudio IDE in Linux

Installing and configuring R tools for Visual Studio in Windows

Installing R libraries from various sources

Installing a specific version of R library

Data Structures in R

Introduction

Creating a vector and accessing its properties

Creating a matrix and accessing its properties

Creating a data frame and accessing its properties

Creating an array and accessing its properties

Creating a list from a combination of vector, matrix, and data frame

Converting a matrix to a data frame and a data frame to a matrix

Writing Customized Functions

Introduction

Writing your first function in R

Writing functions with multiple arguments and use of default values

Handling data types in input arguments

Producing different output types and return values

Making a recursive call to a function

Handling exceptions and error messages

Conditional and Iterative Operations

Introduction

The use of the if conditional statement

The use of the if…else conditional operator

The use of the ifelse vectorised conditional operator

Writing a function using the switch operator

Comparing the performance of switch and series of the if…else statements

Using for loop for iterations

Vectorised operation versus for loop

R Objects and Classes

Introduction

Defining a new S3 class

Defining methods for the S3 class

Creating a generic function and defining a method for the S3 class

Defining a new S4 class

Defining methods for an S4 class

Creating a function to return an object of the S4 class

Querying, Filtering, and Summarizing

Introduction

Using the pipe operator for data processing

Efficient and fast summarization using the dplyr verbs

Using the customized function within the dplyr verbs

Using the select verb for data processing

Using the filter verb for data processing

Using the arrange verb for data processing

Using mutate for data processing

Using summarise to summarize dataset

R for Text Processing

Introduction

Extracting unstructured text data from a plain web page

Extracting text data from an HTML page

Extracting text data from an HTML page using the XML library

Extracting text data from PubMed

Importing unstructured text data from a plain text file

Importing plain text data from a PDF file

Pre-processing text data for topic modeling and sentiment analysis

Creating a word cloud to explore unstructured text data

Using regular expression in text processing

R and Databases

Introduction

Installing the PostgreSQL database server

Creating a new user in the PostgreSQL database server

Creating a table in a database in PostgreSQL

Creating a dataset in PostgreSQL from R

Interacting with the PostgreSQL database from R

Creating and interacting with the SQLite database from R

Parallel Processing in R

Introduction

Creating an XDF file from CSV input

Processing data as a chunk

Comparing computation time with data frame and XDF

Linear regression with larger data (rxFastLiner)

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Using the select verb for data processing

The dataset usually contains a large number of variables that are not relevant for every type of analysis. Working with the entire dataset consumes more memory, and it is recommended that you use only the smaller number of variables for the analysis that is required to achieve the task. Taking the smaller number of variables from the entire dataset is usually known as subsetting, but when the term subset has been used, the user could interpret this in two ways: subset of dataset with a smaller number of variables and also subset by taking fewer rows from the entire dataset. In the dplyr library, these two aspects are covered by the select() and filter() verbs. In this recipe, you will subset a dataset by taking only a handful of variables by using the select() verb.

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