Strings

Introduction

This chapter introduces you to string manipulation in R. You'll learn the basics of how strings work and how to create them by hand, but 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.

Prerequisites

This chapter will focus on the stringr package for string manipulation, which is part of the core tidyverse.

 library(tidyverse)

Source: H. Wickham and G. Grolemund, https://r4ds.had.co.nz/strings.html
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

String basics

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 ".

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:

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():

x <- c("\"", "\\")
x
#> [1] "\"" "\\"
writeLines(x)
#> "
#> \

There are a handful of other special characters. The most common are "\n", newline, and "\t", tab, but you can see the complete list by requesting help on ": ?'"', or ?"'". You'll also sometimes see strings like "\u00b5", this is a way of writing non-English characters that works on all platforms:

x <- "\u00b5"
x
#> [1] "µ"

Multiple strings are often stored in a character vector, which you can create with c():

c("one", "two", "three")
#> [1] "one"   "two"   "three"

String length

Base R contains many functions to work with strings but we'll avoid them because they can be inconsistent, which makes them hard to remember. Instead we'll use functions from stringr. These have more intuitive names, and all start with str_. For example, str_length() tells you the number of characters in a string:

str_length(c("a", "R for data science", NA))
#> [1]  1 18 NA

The common str_ prefix is particularly useful if you use RStudio, because typing str_ will trigger autocomplete, allowing you to see all stringr functions:

Combining strings

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

str_c("x", "y")
#> [1] "xy"
str_c("x", "y", "z")
#> [1] "xyz"

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

str_c("x", "y", sep = ", ")
#> [1] "x, y"

Like most other functions in R, missing values are contagious. If you want them to print as "NA", use str_replace_na():

x <- c("abc", NA)
str_c("|-", x, "-|")
#> [1] "|-abc-|" NA
str_c("|-", str_replace_na(x), "-|")
#> [1] "|-abc-|" "|-NA-|"

As shown above, str_c() is vectorised, and it automatically recycles shorter vectors to the same length as the longest:

str_c("prefix-", c("a", "b", "c"), "-suffix")
#> [1] "prefix-a-suffix" "prefix-b-suffix" "prefix-c-suffix"

Objects of length 0 are silently dropped. This is particularly useful in conjunction with if:

name <- "Hadley"
time_of_day <- "morning"
birthday <- FALSE

str_c(
  "Good ", time_of_day, " ", name,
  if (birthday) " and HAPPY BIRTHDAY",
  "."
)
#> [1] "Good morning Hadley."

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

str_c(c("x", "y", "z"), collapse = ", ")
#> [1] "x, y, z"

Subsetting strings

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:

x <- c("Apple", "Banana", "Pear")
str_sub(x, 1, 3)
#> [1] "App" "Ban" "Pea"
# negative numbers count backwards from end
str_sub(x, -3, -1)
#> [1] "ple" "ana" "ear"

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

str_sub("a", 1, 5)
#> [1] "a"

You can also use the assignment form of str_sub() to modify strings:

str_sub(x, 1, 1) <- str_to_lower(str_sub(x, 1, 1))
x
#> [1] "apple"  "banana" "pear"

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:

# 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"))
#> [1] "I" "I"
str_to_upper(c("i", "ı"), locale = "tr")
#> [1] "İ" "I"

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 has a good list. If you leave the locale blank, it will use the current locale, as provided by your operating system.

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:

x <- c("apple", "eggplant", "banana")

str_sort(x, locale = "en")  # English
#> [1] "apple"    "banana"   "eggplant"

str_sort(x, locale = "haw") # Hawaiian
#> [1] "apple"    "eggplant" "banana"

Matching patterns with regular expressions

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.

Basic matches

The simplest patterns match exact strings:

x <- c("apple", "banana", "pear")
str_view(x, "an")

• apple
• banana
• pear

The next step up in complexity is ., which matches any character (except a newline):

str_view(x, ".a.")

• apple
• banana
• pear

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 "\\.".

# 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")

• abc
• a.c
• bef

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!

x <- "a\\b"
writeLines(x)
#> a\b

str_view(x, "\\\\")

• a\b

In this book, I'll write regular expression as \. and strings that represent the regular expression as "\\.".

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.

x <- c("apple", "banana", "pear")
str_view(x, "^a")

• apple
• banana
• pear

str_view(x, "a$")

• apple
• banana
• pear

To remember which is which, try this mnemonic which I learned from Evan Misshula: 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 $:

x <- c("apple pie", "apple", "apple cake")
str_view(x, "apple")

• apple pie
• apple
• apple cake

str_view(x, "^apple$")

• apple pie
• apple
• apple cake

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.

Character classes and alternatives

There are a number of special patterns that match more than one character. You've already seen ., which matches any character apart from a newline. There are four other useful tools:

• \d: matches any digit.
• \s: matches any whitespace (e.g. space, tab, newline).
• [abc]: matches a, b, or c.
• [^abc]: matches anything except a, b, or c.

Remember, to create a regular expression containing \d or \s, you'll need to escape the \ for the string, so you'll type "\\d" or "\\s".

A character class containing a single character is a nice alternative to backslash escapes when you want to include a single metacharacter in a regex. Many people find this more readable.

# Look for a literal character that normally has special meaning in a regex
str_view(c("abc", "a.c", "a*c", "a c"), "a[.]c")

• abc
• a.c
• a*c
• a c

str_view(c("abc", "a.c", "a*c", "a c"), ".[*]c")

str_view(c("abc", "a.c", "a*c", "a c"), "a[ ]")

str_view(c("grey", "gray"), "gr(e|a)y")

x <- "1888 is the longest year in Roman numerals: MDCCCLXXXVIII"
str_view(x, "CC?")

str_view(x, "CC+")

str_view(x, 'C[LX]+')

str_view(x, "C{2}")

str_view(x, "C{2,}")

str_view(x, "C{2,3}")

str_view(x, 'C{2,3}?')
 

str_view(x, 'C[LX]+?')
 

str_view(fruit, "(..)\\1", match = TRUE)