The focus of this chapter will be on regular expressions, or regexps for short.
Regular expressions are useful because strings usually contain unstructured or semi-structured data, and regexps are a concise language for describing patterns in strings.
When you first look at a regexp, you'll think a cat walked across your keyboard, but as your understanding improves they will soon start to make sense.
Regexps are a very terse language that allow you to describe patterns in strings.
They take a little while to get your head around, but once you understand them, you'll find them extremely useful.
To learn regular expressions, we'll use `str_view()` and `str_view_all()`.
These functions take a character vector and a regular expression, and show you how they match.
We'll start with very simple regular expressions and then gradually get more and more complicated.
Once you've mastered pattern matching, you'll learn how to apply those ideas with various stringr functions.
### Prerequisites
This chapter will focus on the **stringr** package for string manipulation, which is part of the core tidyverse.
```{r setup, message = FALSE}
library(tidyverse)
```
## Basic matches
The simplest patterns match exact strings:
```{r}
x <- c("apple", "banana", "pear")
str_view(x, "an")
```
The next step up in complexity is `.`, which matches any character (except a newline):
```{r}
str_view(x, ".a.")
```
But if "`.`" matches any character, how do you match the character "`.`"?
You need to use an "escape" to tell the regular expression you want to match it exactly, not use its special behaviour.
Like strings, regexps use the backslash, `\`, to escape special behaviour.
So to match an `.`, you need the regexp `\.`.
Unfortunately this creates a problem.
We use strings to represent regular expressions, and `\` is also used as an escape symbol in strings.
So to create the regular expression `\.` we need the string `"\\."`.
```{r}
# To create the regular expression, we need \\
dot <- "\\."
# But the expression itself only contains one:
writeLines(dot)
# And this tells R to look for an explicit .
str_view(c("abc", "a.c", "bef"), "a\\.c")
```
If `\` is used as an escape character in regular expressions, how do you match a literal `\`?
Well you need to escape it, creating the regular expression `\\`.
To create that regular expression, you need to use a string, which also needs to escape `\`.
That means to match a literal `\` you need to write `"\\\\"` --- you need four backslashes to match one!
```{r}
x <- "a\\b"
writeLines(x)
str_view(x, "\\\\")
```
In this book, I'll write regular expression as `\.` and strings that represent the regular expression as `"\\."`.
### Exercises
1. Explain why each of these strings don't match a `\`: `"\"`, `"\\"`, `"\\\"`.
2. How would you match the sequence `"'\`?
3. What patterns will the regular expression `\..\..\..` match?
How would you represent it as a string?
## Anchors
By default, regular expressions will match any part of a string.
It's often useful to *anchor* the regular expression so that it matches from the start or end of the string.
You can use:
- `^` to match the start of the string.
- `$` to match the end of the string.
```{r}
x <- c("apple", "banana", "pear")
str_view(x, "^a")
str_view(x, "a$")
```
To remember which is which, try this mnemonic which I learned from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): if you begin with power (`^`), you end up with money (`$`).
To force a regular expression to only match a complete string, anchor it with both `^` and `$`:
```{r}
x <- c("apple pie", "apple", "apple cake")
str_view(x, "apple")
str_view(x, "^apple$")
```
You can also match the boundary between words with `\b`.
I don't often use this in R, but I will sometimes use it when I'm doing a search in RStudio when I want to find the name of a function that's a component of other functions.
For example, I'll search for `\bsum\b` to avoid matching `summarise`, `summary`, `rowsum` and so on.
### Exercises
1. How would you match the literal string `"$^$"`?
2. Given the corpus of common words in `stringr::words`, create regular expressions that find all words that:
a. Start with "y".
b. End with "x"
c. Are exactly three letters long. (Don't cheat by using `str_length()`!)
d. Have seven letters or more.
Since this list is long, you might want to use the `match` argument to `str_view()` to show only the matching or non-matching words.
When you have complex logical conditions (e.g. match a or b but not c unless d) it's often easier to combine multiple `str_detect()` calls with logical operators, rather than trying to create a single regular expression.
For example, here are two ways to find all words that don't contain any vowels:
```{r}
# Find all words containing at least one vowel, and negate
no_vowels_1 <- !str_detect(words, "[aeiou]")
# Find all words consisting only of consonants (non-vowels)
no_vowels_2 <- str_detect(words, "^[^aeiou]+$")
identical(no_vowels_1, no_vowels_2)
```
The results are identical, but I think the first approach is significantly easier to understand.
If your regular expression gets overly complicated, try breaking it up into smaller pieces, giving each piece a name, and then combining the pieces with logical operations.
1. Create regular expressions to find all words that:
a. Start with a vowel.
b. That only contain consonants. (Hint: thinking about matching "not"-vowels.)
c. End with `ed`, but not with `eed`.
d. End with `ing` or `ise`.
2. Empirically verify the rule "i before e except after c".
3. Is "q" always followed by a "u"?
4. Write a regular expression that matches a word if it's probably written in British English, not American English.
5. Create a regular expression that will match telephone numbers as commonly written in your country.
## Repetition / Quantifiers
The next step up in power involves controlling how many times a pattern matches:
- `?`: 0 or 1
- `+`: 1 or more
- `*`: 0 or more
```{r}
x <- "1888 is the longest year in Roman numerals: MDCCCLXXXVIII"
str_view(x, "CC?")
str_view(x, "CC+")
str_view(x, 'C[LX]+')
```
Note that the precedence of these operators is high, so you can write: `colou?r` to match either American or British spellings.
That means most uses will need parentheses, like `bana(na)+`.
You can also specify the number of matches precisely:
- `{n}`: exactly n
- `{n,}`: n or more
- `{1,m}`: at most m
- `{n,m}`: between n and m
```{r}
str_view(x, "C{2}")
str_view(x, "C{2,}")
str_view(x, "C{1,3}")
str_view(x, "C{2,3}")
```
By default these matches are "greedy": they will match the longest string possible.
You can make them "lazy", matching the shortest string possible by putting a `?` after them.
This is an advanced feature of regular expressions, but it's useful to know that it exists:
```{r}
str_view(x, 'C{2,3}?')
str_view(x, 'C[LX]+?')
```
Collectively, these operators are called **quantifiers** because they quantify how many times a match can occur.
### Exercises
1. Describe the equivalents of `?`, `+`, `*` in `{m,n}` form.
2. Describe in words what these regular expressions match: (read carefully to see if I'm using a regular expression or a string that defines a regular expression.)
a. `^.*$`
b. `"\\{.+\\}"`
c. `\d{4}-\d{2}-\d{2}`
d. `"\\\\{4}"`
3. Create regular expressions to find all words that:
a. Start with three consonants.
b. Have three or more vowels in a row.
c. Have two or more vowel-consonant pairs in a row.
A word of caution before we continue: because regular expressions are so powerful, it's easy to try and solve every problem with a single regular expression.
In the words of Jamie Zawinski:
> Some people, when confronted with a problem, think "I know, I'll use regular expressions." Now they have two problems.
As a cautionary tale, check out this regular expression that checks if a email address is valid:
This is a somewhat pathological example (because email addresses are actually surprisingly complex), but is used in real code.
See the Stack Overflow discussion at <http://stackoverflow.com/a/201378> for more details.
Don't forget that you're in a programming language and you have other tools at your disposal.
Instead of creating one complex regular expression, it's often easier to write a series of simpler regexps.
If you get stuck trying to create a single regexp that solves your problem, take a step back and think if you could break the problem down into smaller pieces, solving each challenge before moving onto the next one.
1. In the previous example, you might have noticed that the regular expression matched "flickered", which is not a colour. Modify the regex to fix the problem.
2. Find all words that come after a "number" like "one", "two", "three" etc. Pull out both the number and the word.
3. Find all contractions. Separate out the pieces before and after the apostrophe.
