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

Exercises


The following are a few exercises for you to strengthen your grasp of the concepts learned in this chapter:

  • Normally, when there is missing data for a question such as "What is your income?", we strongly suspect an MNAR mechanism, because we live in a dystopia that equates wealth with worth. As a result, the participants with the lowest income may be embarrassed to answer that question. In the relevant section, we assumed that because the question was poorly worded and we could account for whether English was the first language of the participant, the mechanism is MAR. If we were wrong about this reason, and it was really because the lower income participants were reticent to admit their income, what would the missing data mechanism be now? If, however, the differences in income were fully explained by whether English was the first language of the participant, what would the missing data mechanism be in that case?
  • Find a dataset on the web with missing data. What does it use to denote...