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

Communicating results


Unless an analysis is performed solely for the personal edification of the analyst, the results are going to be communicated, either to teammates, your company, your lab, or the general public. Some very advanced technologies are in place for R programmers to communicate their results accurately and attractively.

Following the pattern of some of the other sections in this chapter, we will talk about a range of approaches, starting with a bad alternative, and giving an explanation for why it's inadequate.

The terrible solution to the creating of a statistical report is to copy R output into a Word document (or PowerPoint presentation) mixed with prose. "Why is this terrible?" you ask. Because if one little thing about your analysis changes, you will have to re-copy the new R output into the document, manually. If you do this enough times, it's not a matter of if, but a matter of when you will mess up and copy the wrong thing, or forget to copy the new output, and so on...