40 lines
1.8 KiB
Plaintext
40 lines
1.8 KiB
Plaintext
# (PART) Wrangle {-}
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# Introduction {#wrangle-intro}
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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:
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```{r echo = FALSE, out.width = "75%"}
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knitr::include_graphics("diagrams/data-science-wrangle.png")
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```
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This part of the book proceeds as follows:
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* In [tibbles], you'll learn about the variant of the data frame that we use
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in this book: the __tibble__. You'll learn what makes them different
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from regular data frames, and how you can construct them "by hand".
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* In [data import], you'll learn how to get your data from disk and into R.
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We'll focus on plain-text rectangular formats, but will give you pointers
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to packages that help with other types of data.
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* In [tidy data], you'll learn about tidy data, a consistent way of storing
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your data that makes transformation, visualisation, and modelling easier.
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You'll learn the underlying principles, and how to get your data into a
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tidy form.
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Data wrangling also encompasses data transformation, which you've already learn a little about. Now we'll focus on new skills for three specific types of data you will frequently encounter in practice:
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* [Relational data] will give you tools for working with multiple
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interrelated datasets.
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* [Strings] will introduce regular expressions, a powerful tool for
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manipulating strings.
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* [Factors] are how R stores categorical data. They are used when a variable
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has a fixed set of possible values, or when you want to non-alphabetical
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ordering of a string.
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* [Dates and times] will give you the key tools for working with
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dates and date-times.
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