Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Using another R implementation


R is both a language and an implementation of that language. So far, when we've been talking about the R environment/platform, we've been talking about the GNU Project started by R. Ihaka and R. Gentleman at the University of Auckland in 1993, and hosted at http://www.r-project.org. Since R has no standard specification, this canonical implementation serves as R's de facto specification. If a project is able to implement this specification, and rewrite the GNU-R functionality-for-functionality and bug-for-bug-any valid R code can be run on that implementation.

Some time around 2009, various other implementations of R started to crop up. Among these were: 

  • Renjin (running on the Java Virtual Machine)
  • pqR (which stands for Pretty Quick R, and written in a mix of C, R, and Fortran), FastR (which is written in Java)
  • Riposte (which is written mainly in C++)

These alternative implementations promise compelling improvements to GNU-R, such as automatic multithreading (parallelization...