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* Reflect new part structure

* Mention all chapters

* Hide the ruler

* Crossref diagram

* Fix crossref

* Mention all import chapters

* Fix link to following chapter

* Fix title and summary

* Add intros

* Consistent chunk style?
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Mine Cetinkaya-Rundel
2022-12-16 01:41:10 -05:00
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data-import.qmd
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@@ -9,8 +9,13 @@ status("polishing")
## Introduction
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.
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 apply what you've learned to your own data.
In this chapter, you'll learn the basics of reading data files into R.
Specifically, this chapter will focus on reading plain-text rectangular files.
We'll start with some practical advice for handling features like column names and types and missing data.
You will then learn about reading data from multiple files at once and writing data from R to a file.
Finally, you'll learn how to hand craft data frames in R.
### Prerequisites
@@ -25,7 +30,7 @@ library(tidyverse)
## Reading data from a file
To begin we'll focus on the most rectangular data file type: the CSV, short for comma separate values.
To begin we'll focus on the most rectangular data file type: the CSV, short for comma-separated values.
Here is what a simple CSV file looks like.
The first row, commonly called the header row, gives the column names, and the following six rows give the data.
@@ -496,7 +501,7 @@ We'll use `tibble()` and `tribble()` later in the book to construct small exampl
In this chapter, you've learned how to load CSV files with `read_csv()` and to do your own data entry with `tibble()` and `tribble()`.
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-rectangling from JSON, and @sec-scraping from websites.
We'll come to data import a few times in this book: @sec-import-spreadsheets from Excel and googlesheets, @sec-import-databases will show you how to load data from databases, @sec-arrow from parquet files, @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.