#### 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

## Frequency distributions

A common way of describing univariate data is with a frequency distribution. We've already seen an example of a frequency distribution when we looked at the preferences for soy ice cream at the end of the last chapter. For each flavor of ice cream (categorical variable), it depicted the count or frequency of the occurrences in the underlying dataset.

To demonstrate examples of other frequency distributions, we need to find some data. Fortunately, for the convenience of useRs everywhere, R comes preloaded with almost one hundred datasets. You can view a full list if you execute `help (package="datasets")`. There are also hundreds more available from add-on packages.

The first dataset that we are going to use is `mtcars`--data on the design and performance of 32 automobiles, which was extracted from the 1974 Motor Trend US magazine. (To find out more information about this dataset, execute `?mtcars`).

Take a look at the first few lines of this dataset using the `head` function...