# Strings

## Introduction

This chapter introduces you to strings in R.
You'll learn the basics of how strings work and how to create them by hand.
Big topic so spread over three chapters.

Base R contains many functions to work with strings but we'll generally avoid them here because they can be inconsistent, which makes them hard to remember.
Instead, we'll use stringr which is designed to be as consistent as possible, and all of its functions start with `str_`.
The common `str_` prefix is particularly useful if you use RStudio, because typing `str_` will trigger autocomplete, allowing you to see all stringr functions:

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

### 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)
library(babynames)
```

## Creating a string

You can create strings with either single quotes or double quotes.
Unlike other languages, there is no difference in behaviour.
I recommend always using `"`, unless you want to create a string that 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, press Escape and try again!

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

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

That means if you want to include a literal backslash, you'll need to double it up: `"\\"`.

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 `writeLines()`:

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

As shown above, you can combine strings into a (character) vector with `c()`:

```{r}
c("one", "two", "three")
```

### Raw strings

Creating a string with multiple quotes or backslashes gets confusing quickly.
For example, lets create a string that contains the contents of the chunk above:

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

In R 4.0.0 and above, you can use a **raw** string to reduce the amount of escaping:

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

A raw string starts with `r"(` and finishes with `)"`.
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: `` `r"--()--" ``.

### 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 by requesting help on `"` with `?'"'` or `?"'"`.

You'll also sometimes see strings containing Unicode escapes like `"\u00b5"`.
This is a way of writing non-English characters that works on all platforms:

```{r}
x <- "\u00b5"
x
```

## Combining strings

To combine two or more strings, use `str_c()`:

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

Use the `sep` argument to control how they're separated:

```{r}
str_c("x", "y", sep = ", ")
```

Like most other functions in R, missing values are contagious.
As usual, if you want to show a different value, use `coalesce()`:

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

`mutate()`

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

```{r}
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"
)
```

## Length and subsetting

For example, `str_length()` tells you the length of a 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:

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

You can extract parts of a string using `str_sub()`.
As well as the string, `str_sub()` takes `start` and `end` arguments which give the (inclusive) position of the substring:

```{r}
x <- c("Apple", "Banana", "Pear")
str_sub(x, 1, 3)
# negative numbers count backwards from end
str_sub(x, -3, -1)
```

We could use this 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)
  )
```

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)
```

Note that the idea of a "letter" isn't a natural fit to every language, so you'll need to take care if you're working with text from other languages.
We'll briefly talk about some of the issues in Section \@ref(other-languages).

TODO: `separate()`

### Exercises

1.  In code that doesn't use stringr, you'll often see `paste()` and `paste0()`.
    What's the difference between the two functions?
    What stringr function are they equivalent to?
    How do the functions differ in their handling of `NA`?

2.  In your own words, describe the difference between the `sep` and `collapse` arguments to `str_c()`.

3.  Use `str_length()` and `str_sub()` to extract the middle character from a string.
    What will you do if the string has an even number of characters?

4.  Write a function that turns (e.g.) a vector `c("a", "b", "c")` into the string `a, b, and c`.
    Think carefully about what it should do if given a vector of length 0, 1, or 2.

## String summaries

You can perform the opposite operation with `summarise()` and `str_flatten()`:

To collapse a vector of strings into a single string, use `collapse`:

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

This is a great tool for `summarise()`ing character data.
Later we'll come back to the inverse of this, `separate_rows()`.

## Long strings

`str_wrap()`

`str_trunc()`

## Introduction to regular expressions

Opting out by using `fixed()`

## 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")
```

Remember that when you use a logical vector in a numeric context, `FALSE` becomes 0 and `TRUE` becomes 1.
That makes `sum()` and `mean()` useful if you want to answer questions about matches across a larger vector:

```{r}
# How many common words start with t?
sum(str_detect(words, "^t"))
# What proportion of common words end with a vowel?
mean(str_detect(words, "[aeiou]$"))
```

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.

A common use of `str_detect()` is to select the elements that match a pattern.
This makes it a natural pairing with `filter()`.
The following regexp finds all names with repeated pairs of letters (you'll learn how that regexp works in the next chapter)

```{r}
babynames %>% 
  filter(n > 100) %>% 
  count(name, wt = n) %>% 
  filter(str_detect(name, "(..).*\\1"))
```

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}
x <- c("apple", "banana", "pear")
str_count(x, "a")

# On average, how many vowels per word?
mean(str_count(words, "[aeiou]"))
```

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

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

### Exercises

1.  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?

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

## 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"))
```

Use in `mutate()`

#### Exercises

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

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

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

## Extract full matches

To extract the actual text of a match, use `str_extract()`.
To show that off, we're going to need a more complicated example.
I'm going to use the [Harvard sentences](https://en.wikipedia.org/wiki/Harvard_sentences), which were designed to test VOIP systems, but are also useful for practicing regexps.
These are provided in `stringr::sentences`:

```{r}
length(sentences)
head(sentences)
```

Imagine we want to find all sentences that contain a colour.
We first create a vector of colour names, and then turn it into a single regular expression:

```{r}
colours <- c("red", "orange", "yellow", "green", "blue", "purple")
colour_match <- str_c(colours, collapse = "|")
colour_match
```

Now we can select the sentences that contain a colour, and then extract the colour to figure out which one it is:

```{r}
has_colour <- str_subset(sentences, colour_match)
matches <- str_extract(has_colour, colour_match)
head(matches)
```

Note that `str_extract()` only extracts the first match.
We can see that most easily by first selecting all the sentences that have more than 1 match:

```{r}
more <- sentences[str_count(sentences, colour_match) > 1]
str_view_all(more, colour_match)

str_extract(more, colour_match)
```

This is a common pattern for stringr functions, because working with a single match allows you to use much simpler data structures.
To get all matches, use `str_extract_all()`.
It returns a list, so we'll come back to this later on.

### Exercises

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.

## Extract part of matches

If your data is in a tibble, it's often easier to use `tidyr::extract()`.
It works like `str_match()` but requires you to name the matches, which are then placed in new columns:

```{r}
tibble(sentence = sentences) %>% 
  tidyr::extract(
    sentence, c("article", "noun"), "(a|the) ([^ ]+)", 
    remove = FALSE
  )
```

#### Exercises

1.  Find all words that come after a "number" like "one", "two", "three" etc.
    Pull out both the number and the word.

2.  Find all contractions.
    Separate out the pieces before and after the apostrophe.

## Strings -\> Columns

## Separate

`separate()` pulls apart one column into multiple columns, by splitting wherever a separator character appears.
Take `table3`:

```{r}
table3
```

The `rate` column contains both `cases` and `population` variables, and we need to split it into two variables.
`separate()` takes the name of the column to separate, and the names of the columns to separate into, as shown in Figure \@ref(fig:tidy-separate) and the code below.

```{r}
table3 %>%
  separate(rate, into = c("cases", "population"))
```

```{r tidy-separate, echo = FALSE, out.width = "75%", fig.cap = "Separating `rate` into `cases` and `population` to make `table3` tidy", fig.alt = "Two panels, one with a data frame with three columns (country, year, and rate) and the other with a data frame with four columns (country, year, cases, and population). Arrows show how the rate variable is separated into two variables: cases and population."}
knitr::include_graphics("images/tidy-17.png")
```

By default, `separate()` will split values wherever it sees a non-alphanumeric character (i.e. a character that isn't a number or letter).
For example, in the code above, `separate()` split the values of `rate` at the forward slash characters.
If you wish to use a specific character to separate a column, you can pass the character to the `sep` argument of `separate()`.
For example, we could rewrite the code above as:

```{r eval = FALSE}
table3 %>%
  separate(rate, into = c("cases", "population"), sep = "/")
```

`separate_rows()`

## Strings -\> Rows

```{r}
starwars %>% 
  select(name, eye_color) %>% 
  filter(str_detect(eye_color, ", ")) %>% 
  separate_rows(eye_color)
```

### Exercises

1.  Split up a string like `"apples, pears, and bananas"` into individual components.

2.  Why is it better to split up by `boundary("word")` than `" "`?

3.  What does splitting with an empty string (`""`) do?
    Experiment, and then read the documentation.

## Other languages {#other-languages}

Encoding, and why not to trust `Encoding`.
As a general rule, we recommend using UTF-8 everywhere, converting as a early as possible (i.e. by using the `encoding` argument to `readr::locale()`).

### Length and subsetting

This seems like a straightforward computation if you're only familiar with English, but things get complex quick when working with other languages.
Include some examples from <https://gankra.github.io/blah/text-hates-you/>.

This is a problem even with European problem because Unicode provides two ways of representing characters with accents: many common characters have a special codepoint, but others can be built up from individual components.

```{r}
x <- c("\u00e1", "a\u0301")
x
str_length(x)
str_sub(x, 1, 1)
```

### Locales

Above I used `str_to_lower()` to change the text to lower case.
You can also use `str_to_upper()` or `str_to_title()`.
However, changing case is more complicated than it might at first appear because different languages have different rules for changing case.
You can pick which set of rules to use by specifying a locale:

```{r}
# Turkish has two i's: with and without a dot, and it
# has a different rule for capitalising them:
str_to_upper(c("i", "ı"))
str_to_upper(c("i", "ı"), locale = "tr")
```

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.
If you leave the locale blank, it will use English.

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:

```{r}
x <- c("apple", "eggplant", "banana")

str_sort(x, locale = "en")  # English

str_sort(x, locale = "haw") # Hawaiian
```

TODO: add connection to `arrange()`

### `coll()`

Beware using `fixed()` with non-English data.
It is problematic because there are often multiple ways of representing the same character.
For example, there are two ways to define "á": either as a single character or as an "a" plus an accent:

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

They render identically, but because they're defined differently, `fixed()` doesn't find a match.
Instead, you can use `coll()`, defined next, to respect human character comparison rules:

```{r}
str_detect(a1, fixed(a2))
str_detect(a1, coll(a2))
```

-   

`coll()`: compare strings using standard **coll**ation rules.
This is useful for doing case insensitive matching.
Note that `coll()` takes a `locale` parameter that controls which rules are used for comparing characters.
Unfortunately different parts of the world use different rules!

```{r}
# That means you also need to be aware of the difference
# when doing case insensitive matches:
i <- c("I", "İ", "i", "ı")
i

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

Both `fixed()` and `regex()` have `ignore_case` arguments, but they do not allow you to pick the locale: they always use the default locale.
You can see what that is with the following code; more on stringi later.

```{r}
stringi::stri_locale_info()
```

The downside of `coll()` is speed; because the rules for recognising which characters are the same are complicated, `coll()` is relatively slow compared to `regex()` and `fixed()`.
