#### 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.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
RefresheR
The Shape of Data
Describing Relationships
Probability
Using Data To Reason About The World
Testing Hypotheses
Bayesian Methods
The Bootstrap
Predicting Continuous Variables
Predicting Categorical Variables
Predicting Changes with Time
Sources of Data
Dealing with Missing Data
Dealing with Messy Data
Dealing with Large Data
Working with Popular R Packages
Reproducibility and Best Practices
Other Books You May Enjoy
Index

## A tale of two interpretations

Though it may seem strange to hear, there is actually a hot philosophical debate about what probability really is. Though there are others, the two primary camps into which virtually all mathematicians fall are the frequentist camp and the Bayesian camp.

The frequentist interpretation describes probability as the relative likelihood of observing an outcome in an experiment when you repeat the experiment multiple times. Flipping a coin is a perfect example; the probability of heads converges to 50% as the number of times it is flipped goes to infinity.

The frequentist interpretation of probability is inherently objective; there is a true probability out there in the world, which we are trying to estimate.

The Bayesian interpretation, however, views probability as our degree of belief about something. Because of this, the Bayesian interpretation is subjective; when evidence is scarce, there are sometimes wildly different degrees of belief among different people.

Described...