r4ds/wrangle.Rmd

# (PART) Wrangle {-}

# 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 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")
```

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 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, 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 non-alphabetical
    ordering of a string.
    
*   [Dates and times] will give you the key tools for working with 
    dates and date-times.