Book Image

Modern R Programming Cookbook

By : Jaynal Abedin
Book Image

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.

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Table of Contents (10 chapters)
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
1
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
2
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
3
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
4
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
5
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
6
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
7
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
8
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
9
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)

Linear regression with larger data (rxFastLiner)

Linear regression is one of the most popular algorithms to predict the numeric outcome based on observed features. The default implementation in R for the linear regression is the lm() function. For a larger dataset with a large number of variables, this could take a very long time to run. The rxFastLinear() function for the RevoScaleR library offers a very fast implementation of linear regression with a larger dataset with many variables. In this recipe, you will build a linear regression model to predict arrival delay time as a function of the origin and destination airport along with the departure delay and the day of the week.

Getting ready

To build a linear regression model...

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