## 9.1 Introduction

Topics and terms covered in this chapter.

Terminology:

• Text objects are called strings or text strings. We will use the terms text and strings interchangeably.

• Text contains characters (i.e., letters, numbers, and other symbols). In R, its data type and mode are also called character.

• Larger units of text are often called documents. Loading, reading or searching through text strings is also referred to as parsing.

### 9.1.1 Objectives

After working through this chapter, you should be able to:

1. understand that text consists of strings,
2. combine and dissect strings of text,
3. read and write regular expressions,
4. use stringr commands to find and replace text strings.

### 9.1.2 Data

Data used in this chapter.

### 9.1.3 Preparation

#### Preliminaries

This chapter assumes that you have read and worked through Chapter 14: Strings of the r4ds textbook (Wickham & Grolemund, 2017). Based on this background, we examine essential commands of base R and the stringr package (Wickham, 2019) in the context of examples and exercises.

• Create an R Markdown (.Rmd) document (for instructions, see Appendix E and the templates linked in Section E.2).

• Structure your document by inserting headings and empty lines between different parts. Here’s an example how your initial file could look:

---
title: "Chapter 09: Text data"
date: "2020 March 24"
output: html_document
---

Add text or code chunks here.

# Exercises (09: Text data)

## Exercise 1

## Exercise 2

etc.

<!-- The end (eof). -->
• Create an initial code chunk below the header of your .Rmd file that loads the R packages of the tidyverse (and see Section E.3.3 if you want to get rid of the messages and warnings of this chunk in your HTML output).

• Save your file (e.g., as nr_name.Rmd in the R folder of your current project) and remember saving and knitting it regularly as you keep adding content to it.

### References

Wickham, H. (2019). stringr: Simple, consistent wrappers for common string operations. Retrieved from https://CRAN.R-project.org/package=stringr

Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz