r4ds/strings.qmd

# Strings {#sec-strings}

```{r}
#| results: "asis"
#| echo: false
source("_common.R")
status("restructuring")
```

## Introduction

So far, you've used a bunch of strings without learning much about the details.
Now it's time to dive into them, learning what makes strings tick, and mastering some of the powerful string manipulation tool you have at your disposal.

We'll begin with the details of creating strings and character vectors.
You'll then dive into creating strings from data, then the opposite; extracting strings from data.
The chapter finishes up with functions that work with individual letters and a brief discussion of where your expectations from English might steer you wrong when working with other languages.

### Prerequisites

In this chapter, we'll use functions from the stringr package which is part of the core tidyverse.
We'll also use the babynames data since it provides some fun strings to manipulate.

```{r}
#| label: 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

We've created strings in passing earlier in the book, but didn't discuss the details.
Firstly, you can create a string using either single quotes (`'`) or double quotes (`"`).
There's no difference in behavior between the two so in the interests of consistency the [tidyverse style guide](https://style.tidyverse.org/syntax.html#character-vectors) recommends using `"`, unless the string contains multiple `"`.

```{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, and 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 "'"
```

And 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 (in other words, when you print a string, you can copy and paste the output to recreate that string).
To see the raw contents of the string, use `str_view()`[^strings-1]:

[^strings-1]: Or use the base R function `writeLines()`.

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

### Raw strings {#sec-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 code block where we 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!
(This is sometimes called [leaning toothpick syndrome](https://en.wikipedia.org/wiki/Leaning_toothpick_syndrome).) To eliminate the escaping you can instead use a **raw string**[^strings-2]:

[^strings-2]: 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. 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. You can see the complete list of other special characters in `?'"'`.

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

Note that `str_view()` uses a blue background for tabs to make them easier to spot.
One of the challenges of working with text is that there's a variety of ways that white space can end up in text, so this background helps you recognize that something strange is going on.

### Exercises

1.  Create strings that contain the following values:

    1.  `He said "That's amazing!"`

    2.  `\a\b\c\d`

    3.  `\\\\\\`

2.  Create the string in your R session and print it.
    What happens to the special "\\u00a0"?
    How does `str_view()` display it?
    Can you do a little googling to figure out what this special character is?

    ```{r}
    x <- "This\u00a0is\u00a0tricky"
    ```

## Creating many strings from data

Now that you've learned the basics of creating a string or two by "hand", we'll go into the details of creating strings from other strings.
This will help you solve the common problem where you have some text that you wrote that you want to combine with strings from a data frame.
For example, to create a greeting you might combine "Hello" with a `name` variable.
We'll show you how to do this with `str_c()` and `str_glue()` and how you might use them with `mutate()`.
That naturally raises the question of what functions you might use with `summarise()`, so we'll finish this section with a discussion of `str_flatten()` which is a summary function for strings.

### `str_c()`

`str_c()`[^strings-3] takes any number of vectors as arguments and returns a character vector:

[^strings-3]: `str_c()` is very similar to the base `paste0()`.
    There are two main reasons we recommend: it obeys the usual rules for propagating `NA`s and it uses the tidyverse recycling rules.

```{r}
str_c("x", "y")
str_c("x", "y", "z")
str_c("Hello ", c("John", "Susan"))
```

`str_c()` is designed to be used with `mutate()` so it obeys the usual rules for recycling and missing values:

```{r}
set.seed(1410)
df <- tibble(name = c(wakefield::name(3), NA))
df |> mutate(greeting = str_c("Hi ", name, "!"))
```

If you want missing values to display in some other way, use `coalesce()` either inside or outside of `str_c()`:

```{r}
df |> mutate(
  greeting1 = str_c("Hi ", coalesce(name, "you"), "!"),
  greeting2 = coalesce(str_c("Hi ", name, "!"), "Hi!")
)
```

### `str_glue()` {#sec-glue}

If you are mixing many fixed and variable strings with `str_c()`, you'll notice that you have to type `""` repeatedly, and this can make it hard to see the overall goal of the code.
An alternative approach is provided by the [glue package](https://glue.tidyverse.org) via `str_glue()`[^strings-4] .
You give it a single string containing `{}`; anything inside `{}` will be evaluated like it's outside of the string:

[^strings-4]: If you're not using stringr, you can also access it directly with `glue::glue()`.

```{r}
df |> mutate(greeting = str_glue("Hi {name}!"))
```

As you can see, `str_glue()` currently converts missing values to the string "NA" making it inconsistent with `str_c()`.
We'll hopefully have fixed that by the time you're reading this[^strings-5].

[^strings-5]: Track our progress at <https://github.com/tidyverse/glue/issues/246>.

You also might wonder what happens if you need to include a regular `{` or `}` in your string.
If you guess that you'll need to somehow escape it, you're on the right track.
The trick is that glue uses a slightly different escaping technique; instead of prefixing with special character like `\`, you double up the special characters:

```{r}
df |> mutate(greeting = str_glue("{{Hi {name}!}}"))
```

### `str_flatten()`

`str_c()` and `glue()` work well with `mutate()` because their output is the same length as their inputs.
What if you want a function that works well with `summarise()`, i.e. something that always returns a single string?
That's the job of `str_flatten()`[^strings-6]: it takes a character vector and combines each element of the vector into a single string:

[^strings-6]: The base R equivalent is `paste()` used with the `collapse` argument.

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

This makes it work well 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(letters[1:2], letters[1:3])
    ```

2.  Convert the following expressions from `str_c()` to `str_glue()` or vice versa:

    a.  `str_c("The price of ", food, " is ", price)`

    b.  `glue("I'm {age} years old and live in {country}")`

    c.  `str_c("\\section{", title, "}")`

## Extracting data from strings

Working from <https://github.com/tidyverse/tidyr/pull/1304>.

It's very common for multiple variables to be crammed together into a single string.
In this section you'll learn how to use four tidyr to extract them:

-   `df |> separate_by_longer(col, sep)`
-   `df |> separate_at_longer(col, width)`
-   `df |> separate_by_wider(col, sep, names)`
-   `df |> separate_at_wider(col, widths)`

If you look closely you can see there's a common pattern here: `separate` followed by `by` or `at`, followed by longer or `wider`.
`by` splits up a string with a separator like `", "` or `" "`.
`at` splits at given locations, like 5, 10, and 17.
`longer` makes input data frame longer, making new rows; `wider` makes the input data frame wider, add new columns.

There's one more member of this family, `separate_regex_wider()`, that we'll come back in @sec-regular-expressions.
It's the most flexible of the `at` forms but you need to know a bit about regular expression in order to use it.

```{r}
#| include: false
has_dev_tidyr <- packageVersion("tidyr") >= "1.2.1.9001"
```

The next two sections will give you the basic idea behind these separate functions, and then we'll work through a few case studies that require mutliple uses.

### Splitting into rows

`separate_by_longer()` and `separate_at_longer()` are most useful when the number of components varies from row to row.
`separate_by_longer()` arises most commonly:

```{r}
#| eval: !expr has_dev_tidyr

df1 <- tibble(x = c("a,b,c", "d,e", "f"))
df1 |> 
  separate_by_longer(x, sep = ",")
```

(If the separators have some variation you can use a regular expression instead, if you know about it.)

It's rarer to see `separate_at_longer()` in the wild, but some older datasets can adopt a very compact format where each character is used to record a value:

```{r}
#| eval: !expr has_dev_tidyr

df2 <- tibble(x = c("1211", "131", "21"))
df2 |> 
  separate_at_longer(x, width = 1)
```

### Splitting into columns

`separate_by_wider()` and `separate_at_wider()` are most useful when there are a fixed number of components in each string, and you want to spread them into columns.
They are more complicated that their `by` equivalents because you need to name the columns.

```{r}
#| eval: !expr has_dev_tidyr

df3 <- tibble(x = c("a,1,2022", "b,2,2011", "e,5,2015"))
df3 |> 
  separate_by_wider(x, sep = ",", names = c("letter", "number", "year"))
```

If a specific value is not useful you can use `NA` to omit it from the results:

```{r}
#| eval: !expr has_dev_tidyr

df3 <- tibble(x = c("a,1,2022", "b,2,2011", "e,5,2015"))
df3 |> 
  separate_by_wider(x, sep = ",", names = c("letter", NA, "year"))
```

Alternatively, you can provide `names_sep` and `separate_by_wider()` will use that separator to name automatically:

```{r}
#| eval: !expr has_dev_tidyr

df3 |> 
  separate_by_wider(x, sep = ",", names_sep = "_")
```

`separate_at_wider()` works a little differently, because you typically want to specify the width of each column.
So you give it a named integer vector, where the name gives the name of the new column and the value is the number of characters it occupies.
You can omit values from the output by not naming them:

```{r}
#| eval: !expr has_dev_tidyr

df4 <- tibble(x = c("202215TX", "202122LA", "202325CA")) 
df4 |> 
  separate_at_wider(x, c(year = 4, age = 2, state = 2))
```

### Case studies

## Letters

This section discusses string function that work with individual characters.
In English, characters are easy to understand because they're correspond to the 26 letters of the alphabet (plus a handful of punctuation characters).
Things get complicated quickly when you move beyond English.
Even languages that use the same alphabet, but add additional accents (like å, é, ï, ô, ū) are non-trivial because these extra letters might be represented as an individual character or by composing an unaccented letter with a diacritic mark.
Things get more complicated still as you move further away.
To give just a few examples in Japanese each "letter" is a syllable, in Chinese each "letter" is a complex logogram, and in Arabic, letters look radically different depending on where in the word they fail.

In this section, we'll you're using English (or a nearby language); if you're working with another language, these examples either may not applty or need radically different approaches.

### Length

`str_length()` tells you the number of letters in the string:

```{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-7]:

[^strings-7]: Looking at these entries, we'd guess that 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)
  )
```

### Long strings

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

-   `str_trunc(x, 30)` ensures that no string is longer than 20 characters, replacing any thing too long with `…`.

-   `str_wrap(x, 30)` wraps a string introducing new lines so that each line is at most 30 characters (it doesn't hyphenate, however, so any word longer than 30 characters will make a longer line)

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

TODO: add example with a plot.

### 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?
2.  Are there any major trends in the length of babynames over time? What about the popularity of first and last letters?

## Locale dependent {#sec-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 we wanted to give a quick outline of the functions who's behavior differs based on your **locale**, the set of settings that vary from country to country.

Locale is specified with lower-case language abbreviation, optionally followed by a `_` and a upper-case region identifier.
For example, "en" is English, "en_GB" is British English, and "en_US" is American English.
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.
This means that string manipulation code works the way you expect when you're working with text in your native language, but it might work differently when you share it with someone who lives in another country.
To avoid this problem, stringr defaults to the "en" locale, and requires you to specify the `locale` argument to override it.
This also makes it easy to tell if a function might have different behavior in different locales.

Fortunately there are three sets of functions where the locale matters:

-   **Changing case**: while only relatively few languages have upper and lower case (Latin, Greek, and Cyrillic, plus a handful of lessor known languages).
    The rules are not the 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 capitalizing them:

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

-   **Comparing strings**: `str_equal()` lets you compare if two strings are equal, optionally ignoring case:

    ```{r}
    str_equal("i", "I", ignore_case = TRUE)
    str_equal("i", "I", ignore_case = TRUE, locale = "tr")
    ```

-   **Sorting strings**: `str_sort()` and `str_order()` sort vectors alphabetically, but the alphabet is not the same in every language[^strings-8]!
    Here's an example: in Czech, "ch" is a compound letter that appears after `h` in the alphabet.

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

    Danish has a similar problem.
    Normally, characters with diacritics (e.g. à, á, â) sort after the plain character (e.g. a).
    But in Danish ø and å are their own letters that come at the end of the alphabet:

    ```{r}
    str_sort(c("a", "å", "o", "ø", "z"))
    str_sort(c("a", "å", "o", "ø", "z"), locale = "da")
    ```

    This also comes up when sorting strings with `dplyr::arrange()` which is why it also has a `locale` argument.

[^strings-8]: Sorting in languages that don't have an alphabet (like Chinese) is more complicated still.

## Summary

In this chapter you've learned a wide of tools for working with strings, but you haven't learned one of the most important and powerful tools: regular expressions.
Regular expressions are very concise, but very expressive, language for describing patterns within strings, and are the topic of the next chapter.