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

Chapter 16. Working with Popular R Packages

In this book thus far, we’ve used over 30 packages with functionalities ranging from data visualization to MCMC to connecting R to other languages. In this chapter, we will be focusing on a few very popular R packages that we haven't mentioned before, or that we only mentioned in passing. These packages—namely data.table, dplyr, and tidyr—are very widely used and revered in R circles.

These packages, which stress speed of computation and ease of use (at least after an initial learning curve) are essential components to many useRs' workflows. Note, however, that R was an extraordinarily effective and popular tool before these packages came on the scene and even if these packages were to somehow magically disappear from public memory, R would live on. Therefore, this chapter may be thought of as optional material. I strongly encourage you, however, to at least skim through this chapter; the problems illustrated and solutions demonstrated in this chapter...