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Wrangle proofing

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# Introduction {#wrangle-intro}
In this part of the book, you'll learn about data wrangling, the art of getting your data into R in a useful form. Data wrangling encompasses three main pieces:
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 modelling. Data wrangling is very important: without it you can't work with your own data! There are three main parts to data wrangling:
```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-wrangle.png")
```
Data wrangling is import because it allows you to work with your own data. You'll learn:
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 the art of data import: how to get your
data from disk and into R. We'll focus on plain-text rectangular
formats, but will give you pointers to packages that help with other
types of data.
* In [data import], you'll learn how to get your data from disk and into R.
We'll focus on plain-text 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. You've already learned the basics, and now you'll learn new skills for specific types of data:
Data wrangling also encompasses data transformation, which you've already learn a little about. Now we'll focus new skills for three specific types of data you will frequently encounter in practice:
* [Dates and times] will give you the key tools for working with
dates, and date-times.
dates and date-times.
* [Strings] will introduce regular expressions, a powerful tool for
manipulating strings.
* [Relational data] will give you tools for working with multiple
interrelated datasets.