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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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@ -10,25 +10,34 @@ There are three main parts to data wrangling:
knitr::include_graphics("diagrams/data-science-wrangle.png") knitr::include_graphics("diagrams/data-science-wrangle.png")
``` ```
<!--# TO DO: Redo the diagram without highlighting import. -->
This part of the book proceeds as follows: 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". 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. - 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.
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. 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. 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: 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. 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. -->