(PART) Wrangle

Introduction

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!

The term data wrangling is widely used in data science and is even used in popular culture – see Wikipedia article, https://en.wikipedia.org/wiki/Data_wrangling

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 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.