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

Summary


For bothdata.tableanddplyr/tidyr we went on a very substantial walk-through of the functions and abilities they offer, but not of all of them. Feel free to read the documentation on all of these packages to learn about some of the niceties that we didn’t have the space to mention here.

I hope that you, dear reader, got a lot out of this chapter. Even if you don’t eventually end up using data.table or the tidyverse, I hope you’ve gained a better sense of how flexible R is, learned that R is just as powerful at manipulating data as it is analyzing it, and that we don’t have to settle for base R computation speeds if we don’t want to. Most of the functions we’ve seen in this chapter—in both sections—are a full order of magnitude faster than their base R equivalents. This can be (and often is for very large data sets) the difference between your data wrangling taking a few hours and it taking a few minutes or seconds.