Modern R Programming Cookbook

Recipes for emerging developers in R programming and data scientists to simplify their R programming capabilities
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Modern R Programming Cookbook

Jaynal Abedin

Recipes for emerging developers in R programming and data scientists to simplify their R programming capabilities
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Book Details

ISBN 139781787129054
Paperback236 pages

Book Description

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.

Table of Contents

Chapter 1: 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
Chapter 2: 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 frames
Converting matrix to data frame and data frame to matrix
Chapter 3: Writing Customized Functions
Chapter 4: Conditional and Iterative Operations
Chapter 5: R Objects and Classes
Chapter 6: Querying, Filtering, and Summarizing
Chapter 7: R for Text and Data Processing
Chapter 8: R and Databases
Chapter 9: I/O Issues Handling for Larger Data
Chapter 10: Parallel Processing in R

What You Will Learn

  • Install R and its various IDE for a given platform along with installing libraries from different repositories and version control
  • Learn about basic data structures in R and how to work with them
  • Write customized R functions and handle recursions, exceptions in R environments
  • Create the data processing task as a step by step computer program and execute using dplyr
  • Extract and process unstructured text data
  • Interact with database management system to develop statistical applications
  • Formulate and implement parallel processing in R

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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 frames
Converting matrix to data frame and data frame to matrix
Chapter 3: Writing Customized Functions
Chapter 4: Conditional and Iterative Operations
Chapter 5: R Objects and Classes
Chapter 6: Querying, Filtering, and Summarizing
Chapter 7: R for Text and Data Processing
Chapter 8: R and Databases
Chapter 9: I/O Issues Handling for Larger Data
Chapter 10: Parallel Processing in R

Book Details

ISBN 139781787129054
Paperback236 pages
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