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Update links and add blurb about new chapters

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Mine Çetinkaya-Rundel 2021-02-22 11:36:53 +00:00
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wrangle.Rmd
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@ -10,25 +10,34 @@ There are three main parts to data wrangling:
knitr::include_graphics("diagrams/data-science-wrangle.png")
```
<!--# TO DO: Redo the diagram without highlighting import. -->
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**.
- In Chapter \@ref(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.
- 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.
You'll learn the underlying principles, and how to get your data into a tidy form.
- In Chapter \@ref(rectangle-data), you'll learn about hierarchical data formats and how to turn them into rectangular data via unnesting.
- Chapter \@ref(column-wise-operations) will give you tools for performing the same operation on multiple columns.
- Chapter \@ref(row-wise-operations) will give you tools for performing operations over rows.
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.
- Chapter \@ref(relational-data) will give you tools for working with multiple interrelated datasets.
- [Strings] will introduce regular expressions, a powerful tool for manipulating strings.
- Chapter \@ref(list-columns) will give you tools for working with list columns --- data stored in columns of a tibble as lists.
- [Factors] are how R stores categorical data.
- Chapter \@ref(strings) will give you tools for working with strings and introduce regular expressions, a powerful tool for manipulating strings.
- Chapter \@ref(factors) will introduce factors --- 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.
- Chapter \@ref(dates-and-times) will give you the key tools for working with dates and date-times.
<!--# TO DO: Revisit bullet points about new chapters. -->