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 (19 chapters)

RefresheR

Before we dive into the (other) fun stuff (sampling multi-dimensional probability distributions, using convex optimization to fit data models, and so on), it would be helpful if we review those aspects of R that all subsequent chapters will assume knowledge of.

If you fancy yourself an R guru, you should still, at least, skim through this chapter, because you'll almost certainly find the idioms, packages, and style introduced here to be beneficial for following the rest of the material.

If you don't care much about R (yet), and are just in this for the statistics, you can heave a heavy sigh of relief that, for the most part, you can run the code given in this book in the interactive R interpreter with very little modification and just follow along with the ideas. However, it is my belief (read: delusion) that by the end of this book, you'll cultivate a newfound appreciation for R alongside a robust understanding of methods in data analysis.

Fire up your R interpreter and let's get started!