#### 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

## Summary

There were many new ideas introduced in this chapter, so kudos to you for making it through! You're well on the way to being able to tackle some extraordinarily interesting problems on your own!

To summarize, in this chapter, you learned that the relationships between two variables can be broken down into three broad categories.

For categorical/continuous variables, you learned how to use the `by` function to retrieve the statistics on the continuous variable for each category. We also saw how we can use box-and-whisker plots to visually inspect the distributions of the continuous variable across categories.

For categorical/categorical configurations, we used contingency and proportions tables to compare frequencies. We also saw how mosaic plots can help spot interesting aspects of the data that might be difficult to detect when just looking at the raw numbers.

For categorical/continuous data we discovered the concepts of covariance and correlations, and explored different correlation...