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Modern R Programming Cookbook

By : Jaynal Abedin

Modern R Programming Cookbook

By: Jaynal Abedin

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

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

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

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

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

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

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

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

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

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

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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Installing and configuring base R in Linux

It is not necessarily the case that every user will use Windows platform. In this recipe, you will see how to install and configure R on the Linux platform. Each of the steps is described with an appropriate screenshot so that you can easily understand the steps.

Getting ready

To install R in the Linux operating system, first, you need to know the version of Linux you are using. To know the version of Linux installed on your computer, there are many options. One of the options is the following command:

lsb_release -a

This command will give the release information as shown in the following screenshot:

In this recipe, you will see the example from the Ubuntu 14.04 release.

How to do it...

Once you have the version number, you simply need to install base R onto your computer. The step-by-step procedure is as follows:

Update etc/apt/source.list by adding an entry as follows:

        deb <cran mirror address>/bin/linux/ubuntu trusty/

Replace <cran mirror address> by putting an actual mirror link from the list of available mirrors from https://cran.r-project.org/mirrors.html. Remember that you must have administrator privileges to edit etc/apt/source.list.
Once you have performed the preceding step, type the following command to install R:

      sudo apt-get update
      sudo apt-get install r-base

The preceding two commands will complete the installation process and you can check whether R has been installed correctly or not, by typing R and hitting Enter on the Terminal window. If the R console opens, then you are ready to go:

Some of the users may need to compile R packages from the source code. In that case, you will need to install the following component of R:

      sudo apt-get install r-base-dev

Your computer must connect to the internet to install R.

There's more...

In this recipe, you have installed base R into Ubuntu 14.04 release. If your operating system has another release of Linux, then visit https://cran.r-project.org/, select Download R for Linux, and follow the steps outlined there.

See also

To understand how to install an IDE into either Windows or Linux, follow the next recipe.

Visually different images

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Programming languages

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