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

R scripting


The absolute first thing you should know about standard R workflows is that programs are not generally written directly at the interactive R interpreter. Instead, R programs are usually written in a text file (with an .r or .R file extension). These are usually referred to as R scripts. When these scripts are completed, the commands in this text file are usually executed all at once - we'll get to see how, soon. During development of the script, however, the programmer usually executes portions of the script interactively to get feedback and confirm proper behavior. This interactive component to R scripting allows for building each command or function iteratively.

I've known some serious R programmers who copy and paste from their favorite text editor into an interactive R session to achieve this effect. To most people, particularly beginners, the better solution is to use an editor that can send R code from the script that is actively being written to an interactive R console...