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

What this book covers

What you need for this book

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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Installing and configuring RStudio IDE in Windows

In the first two recipes, the steps were related to installing base R. To use R with an IDE where syntax highlighting features are available along with other options, you will need to install a preferred IDE software. RStudio is one of the most popular IDEs for R. In this recipe, you will learn from where, and how, to download RStudio and install it onto your computer.

Getting ready

To install an IDE, the first step is to install base R; that is, you need to make sure R has been installed prior to installing RStudio IDE. RStudio is an open source and enterprise-ready professional software for R. Now, let's take a look at the following few steps to download and install RStudio IDE:

To download open source components of RStudio IDE, visit the web page at https://www.rstudio.com/products/rstudio/.

From this link, choose RStudio for desktop and download the executable file. The latest version of RStudio is 1.0.136 at the time of the preparation of this recipe. Select an appropriate executable file from the list that matches with your operating system:

How to do it…

The following steps will help you go through the installation process:

Go to the folder where you have stored the executable file of the latest release of RStudio:

Right-click on it and select Run as administrator. The installation Wizard will open. Then, click on Next > on this screen.

At this stage, you will be asked to give the location where you want to install it. The default location usually shows as C:\Program Files\RStudio. However, if you want to install into a different location, then you can do this from here and click on Next >:

Now, you will be asked to give a name of the start menu folder and then click on Install.

How it works…

Once you run the executable file of RStudio, it then checks whether there is any base R installed in the computer. If you do not already have R installed, then you will be given a message stating that base R is required to install RStudio. Once you have made sure that base R is already installed, it will install the IDE without any errors by following the steps mentioned in the How to do it… section.

There's more…

The following screenshot is the initial user interface of RStudio where you will see four major components:

Let's take a look at the following four major components shown in the preceding screenshot:

In the upper-left section is the R script writing section where you will usually write R code to solve your data analytics, data management, or data visualization problems.
In the lower-left section is the R console where you will be able to see immediate output and error messages along the line of code that has been run already.
The upper-right section contains the history of R sessions and the current global environment. The environment section displays all current active R objects, their type, and other information.
The lower-right corner contains several subcomponents, for example, the Help documentation window, graphical output (the Plots tab), the Files tab showing the list of files and folders in the selected directory, and also the list of R packages (the Packages tab).

Modern R Programming Cookbook

By : Jaynal Abedin

Modern R Programming Cookbook

By: Jaynal Abedin

Overview of this book

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