# Strings

```{r, results = "asis", echo = FALSE}
status("restructuring")
```

## Introduction

So far, we've used a bunch of strings without really talking about how they work or the powerful tools you have to work with them.
This chapter begins by diving into the details of creating strings, and from strings, character vectors.
You'll then learn a grab bag of handy string functions before we dive into creating strings from data, then extracting data from strings.
We'll then cover the basics of regular expressions, a powerful, but very concise and sometimes cryptic, language for describing patterns in string.
The chapter concludes with a brief discussion of where your exceptions of English might steer you wrong when working with text from other languages.

This chapter is paired with two other chapters.
Regular expression are a big topic, so we'll come back to them again in Chapter \@ref(regular-expressions).
We'll come back to strings again in Chapter \@ref(programming-with-strings) where we'll think about them about more from a programming perspective than a data analysis perspective.

### Prerequisites

In this chapter, we'll use functions from the stringr package.
The equivalent functionality is available in base R (through functions like `grepl()`, `gsub()`, and `regmatches()`) but we think you'll find stringr easier to use because it's been carefully designed to be as consistent as possible.
We'll also work with the babynames dataset since it provides some fun data to apply string manipulation to.

```{r setup, message = FALSE}
library(tidyverse)
library(babynames)
```

You can easily tell when you're using a stringr function because all stringr functions start with `str_`.
This is particularly useful if you use RStudio, because typing `str_` will trigger autocomplete, allowing you jog your memory of which functions are available.

```{r, echo = FALSE}
knitr::include_graphics("screenshots/stringr-autocomplete.png")
```

## Creating a string

To begin, let's discuss the mechanics of creating a string[^strings-1].
We've created strings in passing earlier in the book, but didn't discuss the details.
First, there are two basic ways to create a string: using either single quotes (`'`) or double quotes (`"`).
Unlike other languages, there is no difference in behaviour, but the [tidyverse style guide](https://style.tidyverse.org/syntax.html#character-vectors) recommends using `"`, unless the string contains multiple `"`

[^strings-1]: A string is a length-1 character vector.

```{r}
string1 <- "This is a string"
string2 <- 'If I want to include a "quote" inside a string, I use single quotes'
```

If you forget to close a quote, you'll see `+`, the continuation character:

    > "This is a string without a closing quote
    + 
    + 
    + HELP I'M STUCK

If this happen to you and you can't figure out which quote you need to close, press Escape to cancel, then try again.

### Escapes

To include a literal single or double quote in a string you can use `\` to "escape" it:

```{r}
double_quote <- "\"" # or '"'
single_quote <- '\'' # or "'"
```

This means if you want to include a literal backslash in your string, you'll need to double it up: `"\\"`:

```{r}
backslash <- "\\"
```

Beware that the printed representation of a string is not the same as string itself, because the printed representation shows the escapes.
To see the raw contents of the string, use `str_view()` [^strings-2]:

[^strings-2]: You can also use the base R function `writeLines()`

```{r}
x <- c(single_quote, double_quote, backslash)
x
str_view(x)
```

### Raw strings

Creating a string with multiple quotes or backslashes gets confusing quickly.
To illustrate the problem, lets create a string that contains the contents of the chunk where I define the `double_quote` and `single_quote` variables:

```{r}
tricky <- "double_quote <- \"\\\"\" # or '\"'
single_quote <- '\\'' # or \"'\""
str_view(tricky)
```

That's a lot of backslashes!

To eliminate the escaping you can instead use a **raw string**[^strings-3]:

[^strings-3]: Available in R 4.0.0 and above.

```{r}
tricky <- r"(double_quote <- "\"" # or '"'
single_quote <- '\'' # or "'"
)"
str_view(tricky)
```

A raw string usually starts with `r"(` and finishes with `)"`.
But if your string contains `)"` you can instead use `r"[]"` or `r"{}"`, and if that's still not enough, you can insert any number of dashes to make the opening and closing pairs unique, e.g. `` `r"--()--" ``, `` `r"---()---" ``, etc. Raw strings are flexible enough to handle any text.

### Other special characters

As well as `\"`, `\'`, and `\\` there are a handful of other special characters that may come in handy. The most common are `\n`, newline, and `\t`, tab, but you can see the complete list in `?'"'`.
You'll also sometimes see strings containing Unicode escapes that start with `\u` or `\U`.
This is a way of writing non-English characters that works on all systems:

```{r}
x <- c("\u00b5", "\U0001f604")
x
str_view(x)
```

Now that you've learned the basics of creating strings by "hand", we'll go into the details of creating strings from other strings, starting with combining strings.

### Vectors

You can combine multiple strings into a character vector by using `c()`:

```{r}
x <- c("first string", "second string", "third string")
x
```

You can create a length zero character vector with `character()`.
This is not usually very useful, but can help you understand the general principle of functions by giving them an unusual input.

### Exercises

## Handy functions

### Length

It's natural to think about the letters that make up an individual string.
(Not every language uses letters, which we'll talk about more in Section \@ref(other-languages)).
For example, `str_length()` tells you the length of a string in characters:

```{r}
str_length(c("a", "R for data science", NA))
```

You could use this with `count()` to find the distribution of lengths of US babynames, and then with `filter()` to look at the longest names[^strings-4]:

[^strings-4]: Looking at these entries, I'd say the babynames data removes spaces or hyphens from names and truncates after 15 letters.

```{r}
babynames %>%
  count(length = str_length(name), wt = n)

babynames %>% 
  filter(str_length(name) == 15) %>% 
  count(name, wt = n, sort = TRUE)
```

### Subsetting

You can extract parts of a string using `str_sub(string, start, end)`.
The `start` and `end` arguments are inclusive, so the length of the returned string will be `end - start + 1`:

```{r}
x <- c("Apple", "Banana", "Pear")
str_sub(x, 1, 3)
```

You can use negative values to count back from the end of the string: -1 is the last character, -2 is the second to last character, etc.

```{r}
str_sub(x, -3, -1)
```

Note that `str_sub()` won't fail if the string is too short: it will just return as much as possible:

```{r}
str_sub("a", 1, 5)
```

We could use `str_sub()` with `mutate()` to find the first and last letter of each name:

```{r}
babynames %>% 
  mutate(
    first = str_sub(name, 1, 1),
    last = str_sub(name, -1, -1)
  )
```

Later, we'll come back to the problem of extracting data from strings.

### Long strings

Sometimes the reason you care about the length of a string is because you're trying to fit it into a label.
stringr provides two useful tools for cases where your string is too long:

-   `str_trunc(x, 20)` ensures that no string is longer than 20 characters, replacing any thing too long with `…`.

-   `str_wrap(x, 20)` wraps a string introducing new lines so that each line is at most 20 characters (it doesn't hyphenate, however, so any word longer than 20 characters will make a longer time)

```{r}
x <- "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat."

str_trunc(x, 30)
str_view(str_wrap(x, 30))
```

### Exercises

1.  Use `str_length()` and `str_sub()` to extract the middle letter from each baby name. What will you do if the string has an even number of characters?

## Combining strings

There are two ways in which you might want to combine strings.
You might have a few character vectors which you want to combine together creating a new vector.
Or you might have a single vector that you want to collapse down into a single string.

### str_c()

Use `str_c()`[^strings-5] to join together multiple character vectors into a single vector:

[^strings-5]: `str_c()` is very similar to the base `paste0()`.
    There are two main reasons I use it here: it obeys the usual rules for handling `NA`, and it uses the tidyverse recycling rules.

```{r}
str_c("x", "y")
str_c("x", "y", "z")
```

`str_c()` obeys the tidyverse recycling rules so any length-1 vectors (aka strings) will be recycled to the length of the longest vector[^strings-6]:

[^strings-6]: If the other vectors don't have the same length, `str_c()` will error.

```{r}
names <- c("Timothy", "Dewey", "Mable")
str_c("Hi ", names, "!")
```

Like most other functions in R, missing values are contagious, so any missing input will cause the output to be missing.
If you don't want this behaviour, use `coalesce()` to replace missing values with something else:

```{r}
x <- c("abc", NA)
str_c("|-", x, "-|")
str_c("|-", coalesce(x, ""), "-|")
```

Since `str_c()` creates a vector, you'll usually use it with `mutate()`:

```{r}
starwars %>% 
  mutate(greeting = str_c("Hi! I'm ", name, "."), .after = name)
```

### `str_dup()`

`str_c(a, a, a)` is like `a + a + a`, what's the equivalent of `3 * a`?
That's `str_dup()`:

```{r}
str_dup(letters[1:3], 3)
str_dup("a", 1:3)
```

### Glue

Another powerful way of combining strings is with the glue package.
You can either use `glue::glue()` directly or call it via the `str_glue()` wrapper that stringr provides for you.
Glue works a little differently to the other methods: you give it a single string then within the string use `{}` to indicate where existing variables should be evaluated:

```{r}
x <- c("abc", NA)
str_glue("|-{x}-|")
```

Like `str_c()`, `str_glue()` pairs well with `mutate()`:

```{r}
starwars %>% 
  mutate(
    intro = str_glue("Hi! My is {name} and I'm a {species} from {homeworld}"),
    .keep = "none"
  )
```

You can use any valid R code inside of `{}`, but it's a good idea to pull complex calculations out into their own variables so you can more easily check your work.

Differences with `NA` handling?

### `str_flatten()`

So far I've shown you vectorised functions that work will with `mutate()`: the output of these functions is the same length as the input.
There's one last important function that's a summary function: the output is always length 1, regardless of the length of the input.
That's `str_flatten()`:[^strings-7] it takes a character vector and always returns a single string:

[^strings-7]: The base R equivalent is `paste()` with the `collapse` argument set.

```{r}
str_flatten(c("x", "y", "z"))
str_flatten(c("x", "y", "z"), ", ")
str_flatten(c("x", "y", "z"), ", ", last = ", and ")
```

Just like `sum()` and `mean()` take a vector of numbers and return a single number, `str_flatten()` takes a character vector and returns a single string.
This makes `str_flatten()` a summary function for strings, so you'll often pair it with `summarise()`:

```{r}
df <- tribble(
  ~ name, ~ fruit,
  "Carmen", "banana",
  "Carmen", "apple",
  "Marvin", "nectarine",
  "Terence", "cantaloupe",
  "Terence", "papaya",
  "Terence", "madarine"
)
df %>%
  group_by(name) %>% 
  summarise(fruits = str_flatten(fruit, ", "))
```

### Exercises

1.  Compare and contrast the results of `paste0()` with `str_c()` for the following inputs:

    ```{r, eval = FALSE}
    str_c("hi ", NA)
    str_c("hi ", character())
    str_c(letters[1:2], letters[1:3])
    ```

2.  What does `str_flatten()` return if you give it a length 0 character vector?

## Splitting apart strings

Common for multiple variables worth of data to be stored in a single string.
In this section you'll learn how to use various functions tidyr to extract them.

Waiting on: <https://github.com/tidyverse/tidyups/pull/15>

## Working with patterns

### Detect matches

To determine if a character vector matches a pattern, use `str_detect()`.
It returns a logical vector the same length as the input:

```{r}
x <- c("apple", "banana", "pear")
str_detect(x, "e")
```

This makes it a logical pairing with `filter()`.
The following example returns all names that contain a lower-case "x":

```{r}
babynames %>% filter(str_detect(name, "x"))
```

Remember that when you use a logical vector in a numeric context, `FALSE` becomes 0 and `TRUE` becomes 1.
That means you can use `summarise()` with `sum()` or `mean()` and `str_detect()` if you want to answer questions about the prevalence of patterns.
For example, the following snippet, gives the proportion of names containing an "x" by year:

```{r, fig.alt = "A timeseries showing the proportion of baby names that contain the letter x. The proportion declines gradually from 8 per 1000 in 1880 to 4 per 1000 in 1980, then increases rapidly to 16 per 1000 in 2019."}
babynames %>% 
  group_by(year) %>% 
  summarise(prop_x = mean(str_detect(name, "x"))) %>% 
  ggplot(aes(year, prop_x)) + 
  geom_line()
```

(Note that this gives us the proportion of names that contain an x; if you wanted the proportion of babies given a name containing an x, you'd need to perform a weighted mean).

### Count matches

A variation on `str_detect()` is `str_count()`: rather than a simple yes or no, it tells you how many matches there are in a string:

```{r}
str_count(x, "p")
```

It's natural to use `str_count()` with `mutate()`:

```{r}
babynames %>% 
  distinct(name) %>% 
  mutate(
    vowels = str_count(name, "[aeiou]"),
    consonants = str_count(name, "[^aeiou]")
  )
```

You also wonder if any names include special characters like periods:

```{r}
babynames %>% 
  distinct(name) %>% 
  head() %>% 
  mutate(
    periods = str_count(name, "."),
  )
```

That's weird!

### Introduction to regular expressions

To understand what's going on, we need to discuss what the second argument to `str_detect()` really is.
It looks like a simple string, but it's pattern actually a much richer tool called a **regular expression**.
A regular expression uses special characters to match string patterns.
For example, `.` will match any character, so `"a."` will match any string that contains an a followed by another character:

```{r}
str_detect(c("a", "ab", "ae", "bd", "ea", "eab"), "a.")
```

`str_view()` shows you regular expressions to help understand what's happening:

```{r}
str_view(c("a", "ab", "ae", "bd", "ea", "eab"), "a.")
```

Regular expressions are a powerful and flexible language which we'll come back to in Chapter \@ref(regular-expressions).
Here we'll use only the most important components of the syntax as you learn the other stringr tools for working with patterns.

There are three useful **quantifiers** that can be applied to other pattern: `?` makes a pattern option (i.e. it matches 0 or 1 times), `+` lets a pattern repeat (ie. it matches at least once), and `*` lets a pattern be optional or repeat (i.e. it matches any number of times, including 0).

-   `ab?` match an "a", optionally followed by a b

-   `ab+` matches an "a", followed by at least one b

-   `ab*` matches an "a", followed by any number of bs

There are various alternatives to `.` that match a restricted set of characters.
One useful operator is the **character class:** `[abcd]` match "a", "b", "c", or "d"; `[^abcd]` matches anything **except** "a", "b", "c", or "d".

You can opt-out of the regular expression rules by using `fixed`:

```{r}
str_view(c("", "a", "."), fixed("."))
```

Note that both fixed strings and regular expressions are case sensitive by default.
You can opt out by setting `ignore_case = TRUE`.

```{r}
str_view_all("x  X  xy", "X")
str_view_all("x  X  xy", fixed("X", ignore_case = TRUE))
str_view_all("x  X  xy", regex(".Y", ignore_case = TRUE))
```

We'll come back to case later, because it's not trivial for many languages.

### Replacing matches

`str_replace_all()` allow you to replace matches with new strings.
The simplest use is to replace a pattern with a fixed string:

```{r}
x <- c("apple", "pear", "banana")
str_replace_all(x, "[aeiou]", "-")
```

With `str_replace_all()` you can perform multiple replacements by supplying a named vector.
The name gives a regular expression to match, and the value gives the replacement.

```{r}
x <- c("1 house", "1 person has 2 cars", "3 people")
str_replace_all(x, c("1" = "one", "2" = "two", "3" = "three"))
```

`str_remove_all()` is a short cut for `str_replace_all(x, pattern, "")` --- it removes matching patterns from a string.

Use in `mutate()`

Using pipe inside mutate.
Recommendation to make a function, and think about testing it --- don't need formal tests, but useful to build up a set of positive and negative test cases as you.

### Exercises

1.  What word has the highest number of vowels?
    What word has the highest proportion of vowels?
    (Hint: what is the denominator?)

2.  For each of the following challenges, try solving it by using both a single regular expression, and a combination of multiple `str_detect()` calls.

    a.  Find all words that start or end with `x`.
    b.  Find all words that start with a vowel and end with a consonant.
    c.  Are there any words that contain at least one of each different vowel?

3.  Replace all forward slashes in a string with backslashes.

4.  Implement a simple version of `str_to_lower()` using `str_replace_all()`.

5.  Switch the first and last letters in `words`.
    Which of those strings are still `words`?

## Locale dependent operations {#other-languages}

So far all of our examples have been using English.
The details of the many ways other languages are different to English are too diverse to detail here, but I wanted to give a quick outline of the functions who's behaviour differs based on your **locale**, the set of settings that vary from country to country.

-   Words are broken up by spaces.
-   Words are composed of individual spaces.
-   All letters in a word are written down.

The locale is specified as a ISO 639 language code, which is a two or three letter abbreviation.
If you don't already know the code for your language, [Wikipedia](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) has a good list, and you can see which are supported with `stringi::stri_locale_list()`.

Base R string functions automatically use your locale current locale, but stringr functions all default to the English locale.
This ensures that your code works the same way on every system, avoiding subtle bugs.
To choose a different locale you'll need to specify the `locale` argument; seeing that a function has a locale argument tells you that its behaviour will differ from locale to locale.

Here are a few places where locale matter:S

-   Upper and lower case: only relatively few languages have upper and lower case (Latin, Greek, and Cyrillic, plus a handful of lessor known languages). The rules are not te same in every language that uses these alphabets. For example, Turkish has two i's: with and without a dot, and it has a different rule for capitalising them:

    ```{r}
    str_to_upper(c("i", "ı"))
    str_to_upper(c("i", "ı"), locale = "tr")
    ```

-   This also affects case insensitive matching with `coll(ignore_case =  TRUE)` which you can control with `coll()`:

    ```{r}
    i <- c("Iİiı")

    str_view_all(i, coll("i", ignore_case = TRUE))
    str_view_all(i, coll("i", ignore_case = TRUE, locale = "tr"))
    ```

-   Many characters with diacritics can be recorded in multiple ways: these will print identically but won't match with `fixed()`.

    ```{r}
    a1 <- "\u00e1"
    a2 <- "a\u0301"
    c(a1, a2)
    a1 == a2

    str_view(a1, fixed(a2))
    str_view(a1, coll(a2))
    ```

-   Another important operation that's affected by the locale is sorting. The base R `order()` and `sort()` functions sort strings using the current locale. If you want robust behaviour across different computers, you may want to use `str_sort()` and `str_order()` which take an additional `locale` argument. Here's an example: in Czech, "ch" is a digraph that appears after `h` in the alphabet.

    ```{r}
    str_sort(c("a", "ch", "c", "h"))
    str_sort(c("a", "ch", "c", "h"), locale = "cs")
    ```

    TODO after dplyr 1.1.0: discuss `arrange()`
