R Programming Tutorial  9. Wrangle
In this part of the book, you’ll learn about data wrangling, the art of getting your data into R in a useful form for visualisation and modeling. Data wrangling is very important: without it you can’t work with your own data! There are three main parts to data wrangling:
This part of the book proceeds as follows:
 In tibbles, you’ll learn about the variant of the data frame that we use in this book: the tibble. You’ll learn what makes them different from regular data frames, and how you can construct them “by hand”.
 In data import, you’ll learn how to get your data from disk and into R. We’ll focus on plaintext rectangular formats, but will give you pointers to packages that help with other types of data.
 In tidy data, you’ll learn about tidy data, a consistent way of storing your data that makes transformation, visualisation, and modelling easier. You’ll learn the underlying principles, and how to get your data into a tidy form.
Data wrangling also encompasses data transformation, which you’ve already learned a little about. Now we’ll focus on new skills for three specific types of data you will frequently encounter in practice:
 Relational data will give you tools for working with multiple interrelated datasets.
 Strings will introduce regular expressions, a powerful tool for manipulating strings.
 Factors are how R stores categorical data. They are used when a variable has a fixed set of possible values, or when you want to use a nonalphabetical ordering of a string.

Dates and times will give you the key tools for working with dates and datetimes.