44 lines
2.3 KiB
Plaintext
44 lines
2.3 KiB
Plaintext
# (PART) Wrangle {.unnumbered}
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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.
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Data wrangling is very important: without it you can't work with your own data!
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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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<!--# TO DO: Redo the diagram without highlighting import. -->
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This part of the book proceeds as follows:
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- In Chapter \@ref(tibbles), you'll learn about the variant of the data frame that we use in this book: the **tibble**.
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You'll learn what makes them different from regular data frames, and how you can construct them "by hand".
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- In Chapter \@ref(tidy-data), you'll learn about tidy data, a consistent way of storing 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 tidy form.
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- In Chapter \@ref(rectangle-data), you'll learn about hierarchical data formats and how to turn them into rectangular data via unnesting.
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- Chapter \@ref(column-wise-operations) will give you tools for performing the same operation on multiple columns.
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- Chapter \@ref(row-wise-operations) will give you tools for performing operations over rows.
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Data wrangling also encompasses data transformation, which you've already learned a little about.
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Now we'll focus on new skills for three specific types of data you will frequently encounter in practice:
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- Chapter \@ref(relational-data) will give you tools for working with multiple interrelated datasets.
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- Chapter \@ref(list-columns) will give you tools for working with list columns --- data stored in columns of a tibble as lists.
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- Chapter \@ref(strings) will give you tools for working with strings and introduce regular expressions, a powerful tool for manipulating strings.
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- Chapter \@ref(factors) will introduce factors --- how R stores categorical data.
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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.
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- Chapter \@ref(dates-and-times) will give you the key tools for working with dates and date-times.
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<!--# TO DO: Revisit bullet points about new chapters. -->
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