You learned the basics of regular expressions in Chapter \@ref(strings), but regular expressions are fairly rich language so it's worth spending some extra time on the details.
The chapter starts by expanding your knowledge of patterns, to cover six important new topics (escaping, anchoring, character classes, shorthand classes, quantifiers, and alternation).
Here we'll focus mostly on the language itself, not the functions that use it.
That means we'll mostly work with toy character vectors, showing the results with `str_view()` and `str_view_all()`.
You'll need to take what you learn here and apply it to data frames with tidyr functions or by combining dplyr and stringr functions.
Next we'll talk about the important concepts of "grouping" and "capturing" which give you new ways to extract variables out of strings using `tidyr::separate_group()`.
Grouping also allows you to use back references which allow you do things like match repeated patterns.
It's worth noting that the regular expressions used by stringr are very slightly different to those of base R.
That's because stringr is built on top of the [stringi package](https://stringi.gagolewski.com), which is in turn built on top of the [ICU engine](https://unicode-org.github.io/icu/userguide/strings/regexp.html), whereas base R functions (like `gsub()` and `grepl()`) use either the [TRE engine](https://github.com/laurikari/tre) or the [PCRE engine](https://www.pcre.org).
Fortunately, the basics of regular expressions are so well established that you'll encounter few variations when working with the patterns you'll learn in this book (and I'll point them out where important).
You only need to be aware of the difference when you start to rely on advanced features like complex Unicode character ranges or special features that use the `(?…)` syntax.
You can learn more about these advanced features in `vignette("regular-expressions", package = "stringr")`.
You learned the very basics of the regular expression pattern language in Chapter \@ref(strings), and now its time to dig into more of the details.
First, we'll start with **escaping**, which allows you to match characters that the pattern language otherwise treats specially.
Next you'll learn about **anchors**, which allow you to match the start or end of the string.
Then you'll learn about **character classes** and their shortcuts, which allow you to match any character from a set.
We'll finish up with **quantifiers**, which control how many times a pattern can match, and **alternation**, which allows you to match either *this* or *that.*
I'll concentrate on showing how these patterns work with `str_view()` and `str_view_all()` but remember that you can use them with any of the functions that you learned about in Chapter \@ref(strings), i.e.:
- `str_detect(x, pattern)` returns a logical vector the same length as `x`, indicating whether each element matches (`TRUE`) or doesn't match (`FALSE`) the pattern.
- `str_count(x, pattern)` returns the number of times `pattern` matches in each element of `x`.
- `str_replace_all(x, pattern, replacement)` replaces every instance of `pattern` with `replacement`.
In general, look at punctuation characters with suspicion; if your regular expression isn't matching what you think it should, check if you've used any of these characters.
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 (`$`).
In Chapter \@ref(strings) you learned about `?` (0 or 1 matches), `+` (1 or more matches), and `*` (0 or more matches).
For example, `colou?r` will match American or British spelling, `\d+` will match one or more digits, and `\s?` will optionally match a single whitespace.
The answer to these questions is determined by operator precedence, similar to the PEMDAS or BEDMAS rules you might have learned in school for what `a + b * c`.
You already know that `a + b * c` is equivalent to `a + (b * c)` not `(a + b) * c` because `*` has high precedence and `+` has lower precedence: you compute `*` before `+`.
In regular expressions, quantifiers have high precedence and alternation has low precedence.
That means `ab+` is equivalent to `a(b+)`, and `^a|b$` is equivalent to `(^a)|(b$)`.
Just like with algebra, you can use parentheses to override the usual order (because they have the highest precedence of all).
Technically the escape, character classes, and parentheses are all operators that also have precedence.
But these tend to be less likely to cause confusion because they mostly behave how you expect: it's unlikely that you'd think that `\(s|d)` would mean `(\s)|(\d)`.
3. Create regular expressions that match the British or American spellings of the following words: grey/gray, modelling/modeling, summarize/summarise, aluminium/aluminum, defence/defense, analog/analogue, center/centre, sceptic/skeptic, aeroplane/airplane, arse/ass, doughnut/donut.
7. 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.)
The following three sections help you practice the components of a pattern by discussing three general techniques: checking you work by creating simple positive and negative controls, combining regular expressions with Boolean algebra, and creating complex patterns using string manipulation.
### Check your work
First, let's find all sentences that start with "The".
It's typically much easier to come up with positive examples than negative examples, because it takes some time until you're good enough with regular expressions to predict where your weaknesses are.
Nevertheless they're still useful; even if you don't get them correct right away, you can slowly accumulate them as you work on your problem.
If you you later get more into programming and learn about unit tests, you can then turn these examples into automated test that ensure you never you never make the same mistake twice.)
Imagine we want to find words that only contain consonants.
One technique is to create a character class that contains all letters except for the vowels (`[^aeiou]`), then allow that to match any number of letters (`[^aeiou]+`), then force it to match the whole string by anchoring to the beginning and the end (`^[^aeiou]+$`):
This is a useful technique whenever you're dealing with logical combinations, particularly those involving "and" or "not".
For example, imagine if you want to find all words that contain "a" and "b".
There's no "and" operator built in to regular expressions so we have to tackle it by looking for all words that contain an "a" followed by a "b", or a "b" followed by an "a":
In general, if you get stuck trying to create a single regexp that solves your problem, take a step back and think if you could break the problem down into smaller pieces, solving each challenge before moving onto the next one.
### Creating a pattern with code
What if we wanted to find all `sentences` that mention a color?
The basic idea is simple: we just combine alternation with word boundaries.
In this example `cols` only contains numbers and letters so you don't need to worry about metacharacters.
But in general, when creating patterns from existing strings it's good practice to run through `str_escape()` which will automatically add `\` in front of otherwise special characters.
### Exercises
1. Construct patterns to find evidence for and against the rule "i before e except after c"?
2. `colors()` contains a number of modifiers like "lightgray" and "darkblue". How could you automatically identify these modifiers? (Think about how you might detect and removed what is being modified).
3. Create a regular expression that finds any use of base R dataset. You can get a list of these datasets via a special use of the `data()` function: `data(package = "datasets")$results[, "Item"]`. Note that a number of old datasets are individual vectors; these contain the name of the grouping "data frame" in parentheses, so you'll need to also strip these off.
Finally, if you're writing a complicated regular expression and you're worried you might not understand it in the future, `comments = TRUE` can be extremely useful.
It allows you to use comments and whitespace to make complex regular expressions more understandable.
Spaces and new lines are ignored, as is everything after `#`.
(Note that I'm using a raw string here to minimize the number of escapes needed)
