Abedin Jaynal. Modern R Programming Cookbook: Recipes to simplify your statistical applications

Packt Publishing, 2017. — 236 p. — ASIN B071VFZFN3.Key FeaturesDevelop strategies to speed up your R code
Tackle programming problems and explore both functional and object-oriented programming techniques
Learn how to address the core problems of programming in R with the most popular R packages for common tasksBook DescriptionR 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.What you will learnInstall 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