#### 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

## Exercises

Try out the following exercises to revise the concepts learned so far:

• Look at the documentation on cor with `help("cor").` You can see, in addition to `"pearson"` and `"spearman"`, there is an option for `"kendall"`. Learn about Kendall's tau. Why, and under what conditions, is it considered better than Spearman's rho?
• For each species of iris, find the correlation coefficient between the sepal length and width. Are there any differences? How did we just combine two different types of the broad categories of bivariate analyses to perform a complex multivariate analysis?
• Download a dataset from the web, or find another built-into-R dataset that suits your fancy (using `library(help = "datasets")`). Explore relationships between the variables that you think might have some connection.
• Gustave Flaubert is well understood to be a classist misogynist and this, of course, influenced how he developed the character of Emma Bovary. However, it is not uncommon for the readers to identify and empathize...