Modern R Programming Cookbook: Recipes to simplify your statistical applications

Key Features

• Develop 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 tasks

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.

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

About the Author

Jaynal Abedin is currently doing research as a PhD student at Unit for Biomedical Data Analytics (BDA) of INSIGHT at the National University of Ireland Galway. His research work is focused on the sports science and sports medicine area in a targeted project with ORRECO --an Irish startup company that provides evidence-based advice to individual athletes through biomarker and GPS data. Before joining INSIGHT as a PhD student he was leading a team of statisticians at an international public health research organization (icddr,b). His primary role there was to develop internal statistical capabilities for researchers who come from various disciplines. He was involved in designing and delivering statistical training to the researchers. He has a bachelors and masters degree in statistics, and he has written two books in R programming: Data Manipulation with R and R Graphs Cookbook (Second Edition) with Packt. His current research interests are predictive modeling to predict probable injury of an athlete and scoring extremeness of multivariate data to get an early signal of an anomaly. Moreover, he has an excellent reputation as a freelance R programmer and statistician in an online platform such as upwork.

Table of Contents

1. Installing and Configuring R and its Libraries
2. Data Structures in R
3. Writing Customized Functions
4. Conditional and Iterative Operations
5. R Objects and Classes
6. Querying, Filtering, and Summarization
7. R for Text Processing
8. R and Databases
9. Parallel Processing in R