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

What we didn't cover


In an effort to spend more time laying a foundation and facilitating a deeper understanding of one of the most popular intermediate methods of forecasting (and, even as it is, I couldn't go into nearly as much detail as I would have liked to for want of space), we necessarily had to miss out on a few topics that would have been nice and helpful to cover. Particularly, the primary topic that comes to mind is ARIMA, or autoregressive integrated moving average, models.

ARIMA models, like exponential smoothing methods as of the late last and early this century, are often expressed as state space models. In addition, many of the exponential smoothing methods we used in this chapter can be translated to equivalent ARIMA models (this is, in fact, how many software programs provided prediction intervals for exponential smoothing forecasts before the state-space/ETS framework opened up the door to do it directly). Due to this translatability for certain popular models, I felt...