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

Types of missing data


The VIM package allowed us to visualize the missing data patterns. A related term, the missing data mechanism, describes the process that determines each data point's likelihood of being missing. There are three main categories of missing data mechanisms: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). Discrimination based on missing data mechanism is crucial, since it informs us about the options for handling the missingness.

The first mechanism, MCAR, occurs when data's missingness is unrelated to the data. This would occur, for example, if rows were deleted from a database at random, or if a gust of wind took a random sample of a surveyor's survey forms off into the horizon. The mechanism that governs the missingness of drat, mpg, cyl, wt, and vs is MCAR, because we randomly selected elements to go missing. This mechanism, while being the easiest to work with, is seldom tenable in practice.

MNAR, on the other hand, occurs...