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Improve cross-references

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* Fix broken links
* Update chapter links
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Hadley Wickham
2022-09-29 10:49:03 -05:00
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data-import.qmd
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@@ -11,7 +11,6 @@ status("polishing")
Working with data provided by R packages is a great way to learn the tools of data science, but at some point you want to stop learning and start working with your own data.
In this chapter, you'll learn how to read plain-text rectangular files into R.
Here, we'll only scratch the surface of data import, but many of the principles will translate to other forms of data, which we'll come back to in @sec-wrangle.
### Prerequisites
@@ -116,7 +115,7 @@ There are two cases where you might want to tweak this behavior:
read_csv("1,2,3\n4,5,6", col_names = FALSE)
```
(`"\n"` is a convenient shortcut for adding a new line. You'll learn more about it and other types of string escape in [Chapter -@sec-strings].)
(`"\n"` is a convenient shortcut for adding a new line. You'll learn more about it and other types of string escape in @sec-strings.)
Alternatively you can pass `col_names` a character vector which will be used as the column names:
@@ -171,7 +170,7 @@ Another common task after reading in data is to consider variable types.
For example, `meal_type` is a categorical variable with a known set of possible values.
In R, factors can be used to work with categorical variables.
We can convert this variable to a factor using the `factor()` function.
You'll learn more about factors in [Chapter -@sec-factors].
You'll learn more about factors in @sec-factors.
```{r}
students <- students |>
@@ -184,7 +183,7 @@ students
Note that the values in the `meal_type` variable has stayed exactly the same, but the type of variable denoted underneath the variable name has changed from character (`<chr>`) to factor (`<fct>`).
Before you move on to analyzing these data, you'll probably want to fix the `age` column as well: currently it's a character variable because of the one observation that is typed out as `five` instead of a numeric `5`.
We discuss the details of fixing this issue in [Chapter -@sec-import-spreadsheets] in further detail.
We discuss the details of fixing this issue in @sec-import-spreadsheets in further detail.
### Compared to base R
@@ -331,7 +330,7 @@ file.remove("students.rds")
In this chapter, you've learned how to use readr to load rectangular flat files from disk into R.
You've learned how csv files work, some of the problems you might encounter, and how to overcome them.
We'll come to data import a few times in this book: @sec-import-databases will show you how to load data from databases, @sec-import-spreadsheets from Excel and googlesheets, @sec-import-rectangling from JSON, and @sec-import-scraping from websites.
We'll come to data import a few times in this book: @sec-import-databases will show you how to load data from databases, @sec-import-spreadsheets from Excel and googlesheets, @sec-rectangling from JSON, and @sec-scraping from websites.
Now that you're writing a substantial amount of R code, it's time to learn more about organizing your code into files and directories.
In the next chapter, you'll learn all about the advantages of scripts and projects, and some of the many tools that they provide to make your life easier.