Regular Expressions#

This tutorial is based on the paper on stringi that was published in the Journal of Statistical Software; see [2]. To learn more about R, check out Marek’s open-access (free!) textbook Deep R Programming [3].

Regular expressions (regexes) provide concise grammar for defining systematic patterns that can be sought in character strings. Examples of such patterns include: specific fixed substrings, emojis of any kind, stand-alone sequences of lower-case Latin letters (“words”), substrings that can be interpreted as real numbers (with or without fractional parts, also in scientific notation), telephone numbers, email addresses, or URLs.

Theoretically, the concept of regular pattern matching dates back to the so-called regular languages and finite state automata [6], see also [5] and [9]. Regexes in the form as we know it today have already been present in one of the pre-Unix implementations of the command-line text editor qed ([10]; the predecessor of the well-known sed).

Base R gives access to two different regex matching engines (via functions such as gregexpr() and grep()):

  • ERE (extended regular expressions that conform to the POSIX.2-1992 standard; via the TRE library); used by default,

  • PCRE (Perl-compatible regular expressions; via the PCRE2 library); activated when perl=TRUE is set.

Other matchers are implemented in the ore, re2r, and re2 packages.

Stringi, on the other hand, provides access to the regex engine implemented in ICU, which was inspired by Java’s util.regex in JDK 1.4. Their syntax is mostly compatible with that of PCRE, although certain more advanced facets might not be supported (e.g., recursive patterns). On the other hand, ICU regexes fully conform to the Unicode Technical Standard #18 and hence provide comprehensive support for Unicode.

It is worth noting that most programming languages and advanced text editors and development environments (including Kate, Eclipse, VSCode, and RStudio) support finding or replacing patterns with regexes. Therefore, they should be amongst the instruments at every data scientist’s disposal. One general introduction to regexes is [1]. The ICU flavour is summarised at

Below we provide a concise yet comprehensive introduction to the topic from the perspective of the stringi package users. This time we will use the pattern search routines whose names end with the *_regex() suffix. Apart from stri_detect_regex(), stri_locate_all_regex(), and so forth, in Capture Groups and References Thereto we introduce stri_match_all_regex(). Moreover, the table below lists the available options for the regex engine.




logical; defaults to FALSE; whether to enable (full) case-insensitive matching


logical; defaults to FALSE; whether to allow white spaces and comments within patterns


logical; defaults to FALSE; if set, “.” matches line terminators; otherwise its matching stops at a line end


logical; defaults to FALSE; whether to treat the entire pattern as a literal string; note that in most cases the code-pointwise string search facilities (*_fixed() functions described in Code-Pointwise Comparing are faster


logical; defaults to FALSE; if set, “$” and “^” recognise line terminators within a string; otherwise, they match only at start and end of the input


logical; defaults to FALSE; when enabled, only the Unix line ending, i.e., U+000A, is honoured as a terminator by “.”, “$”, and “^


logical; defaults to FALSE; whether to use the Unicode definition of word boundaries (see Analysing Text Boundaries), which are quite different from the traditional regex word boundaries


logical; defaults to FALSE; whether unrecognised backslash-escaped characters trigger an error; by default, unknown backslash-escaped ASCII letters represent themselves


integer; processing time limit for match operations in ~milliseconds (depends on the CPU speed); 0 for no limit (the default)


integer; maximal size, in bytes, of the heap storage available for the matcher’s backtracking stack; setting a limit is desirable if poorly written regexes are expected on input; 0 for no limit (the default)

Matching Individual Characters#

We begin by discussing different ways to define character sets. In this part, determining the length of all matching substrings will be quite straightforward.

The following characters have special meaning to the regex engine:

. \ | ( ) [ { } ^ $ * + ?

Any regular expression that doesn’t contain the above behaves like a fixed pattern:

stri_count_regex("spam, eggs, spam, bacon, sausage, and spam", "spam")
## [1] 3

There are hence 3 occurrences of a pattern that is comprised of 4 code points, “s” followed by “p”, then by “a”, and ending with “m”.

However, this time the case insensitive mode fully supports Unicode matching[1]:

stri_detect_regex("groß", "GROSS", case_insensitive=TRUE)
## [1] TRUE

If we wish to include a special character as part of a regular expression – so that it is treated literally – we’ll need to escape it with a backslash, “". Yet, the backlash itself has a special meaning to R, see help("Quotes"). Therefore it needs to be preceded by another backslash.

stri_count_regex("spam...", "\\.")   # "\\" is a way to input a single \
## [1] 3

In other words, the R string "\\." is seen by the regex engine as “\.” and interpreted as the dot character (literally). Alternatively, since R 4.0 we can also input the so-called literal strings like r"(\.)".

Matching Any Character#

The (unescaped) dot, “.”, matches any code point except the newline.

x <- "Spam, ham,\njam, SPAM, eggs, and spam"
stri_extract_all_regex(x, "", case_insensitive=TRUE)
## [[1]]
## [1] "Spam" " ham" "SPAM" "spam"

The above matches non-overlapping length-4 substrings that end with “am”.

The dot’s insensitivity to the newline character is motivated by the need to maintain compatibility with tools such as grep (when searching within text files in a line-by-line manner). This behaviour can be altered by setting the dot_all option to TRUE.

stri_extract_all_regex(x, "", dot_all=TRUE, case_insensitive=TRUE)
## [[1]]
## [1] "Spam"  " ham"  "\njam" "SPAM"  "spam"

Defining Character Sets#

Sets of characters can be introduced by enumerating their members within a pair of square brackets. For instance, “[abc]” denotes the set \(\{\mathtt{a},\mathtt{b},\mathtt{c}\}\) – such a regular expression matches one (and only one) symbol from this set. Moreover, in:

stri_extract_all_regex(x, "[hj]am")
## [[1]]
## [1] "ham" "jam"

the “[hj]am” regex matches: “h” or “j”, followed by “a”, followed by “m”. In other words, "ham" and "jam" are the only two strings that are matched by this pattern (unless matching is done case-insensitively).

The following characters, if used within square brackets, may be treated non-literally:

\ [ ] ^ - &

Therefore, to include them as-is in a character set, the backslash-escape must be used. For example, “[\[\]\\]” matches a backslash or a square bracket.

Complementing Sets#

Including “^” after the opening square bracket denotes the set complement. Hence, “[^abc]” matches any code point except “a”, “b”, and “c”. Here is an example where we seek any substring that consists of 3 non-spaces.

x <- "Nobody expects the Spanish Inquisition!"
stri_extract_all_regex(x, "[^ ][^ ][^ ]")
## [[1]]
##  [1] "Nob" "ody" "exp" "ect" "the" "Spa" "nis" "Inq" "uis" "iti" "on!"

Defining Code Point Ranges#

Each Unicode code point can be referenced by its unique numeric identifier; see Dealing with Unicode Code Points for more details. For instance, “a” is assigned code U+0061, and “z” is mapped to U+007A. In the pre-Unicode era (mostly with regards to the ASCII codes, ≤ U+007F, representing English letters, decimal digits, some punctuation characters, and a few control characters), we were used to relying on specific code ranges; e.g., “[a-z]” denotes the set comprised of all characters with codes between U+0061 and U+007A, i.e., lowercase letters of the English (Latin) alphabet.

stri_extract_all_regex("In 2020, Gągolewski had fun once.", "[0-9A-Za-z]")
## [[1]]
##  [1] "I" "n" "2" "0" "2" "0" "G" "g" "o" "l" "e" "w" "s" "k" "i" "h" "a" "d"
## [19] "f" "u" "n" "o" "n" "c" "e"

The above pattern denotes a union of 3 code ranges: digits and ASCII upper- and lowercase letters.

Nowadays, when processing text in natural languages, this notation should be avoided. Note the missing “ą” (Polish “a” with ogonek) in the result.

Using Predefined Character Sets#

Each code point is assigned a unique general category, which can be thought of as a character’s class, see Unicode Standard Annex #44: {U}nicode Character Database. Sets of characters from each category can be referred to, amongst others, by using the “\p{category}” (or, equivalently, “[\p{category}]”) syntax:

x <- "aąbßÆAĄB你123,.;'! \t-+=[]©←→”„²³¾"
p <- c("\\p{L}", "\\p{Ll}", "\\p{Lu}", "\\p{N}", "\\p{P}", "\\p{S}")
structure(stri_extract_all_regex(x, p), names=p)
## $`\\p{L}`
## [1] "a"  "ą"  "b"  "ß"  "Æ"  "A"  "Ą"  "B"  "你"
## $`\\p{Ll}`
## [1] "a" "ą" "b" "ß"
## $`\\p{Lu}`
## [1] "Æ" "A" "Ą" "B"
## $`\\p{N}`
## [1] "1" "2" "3" "²" "³" "¾"
## $`\\p{P}`
##  [1] "," "." ";" "'" "!" "-" "[" "]" "”" "„"
## $`\\p{S}`
## [1] "+" "=" "©" "←" "→"

The above yield a match to: arbitrary letters, lowercase letters, uppercase letters, numbers, punctuation marks, and symbols, respectively.

Characters’ binary properties and scripts can also be referenced in a similar manner. Some other noteworthy classes include:

p <- c("\\w", "\\d", "\\s")
structure(stri_extract_all_regex(x, p), names=p)
## $`\\w`
##  [1] "a"  "ą"  "b"  "ß"  "Æ"  "A"  "Ą"  "B"  "你" "1"  "2"  "3" 
## $`\\d`
## [1] "1" "2" "3"
## $`\\s`
## [1] " "  "\t"

These give: word characters, decimal digits (”\p{Nd}”), and spaces (”[\t\n\f\r\p{Z}]”), in this order.

Moreover, e.g., the upper-cased “\P{category}” and “\W” are equivalent to “[^\p{category}]” and “[^\w]”, respectively, i.e., denote their complements.

Avoiding POSIX Classes#

The use of the POSIX-like character classes should be avoided because they are generally not well-defined.

In particular, in POSIX-like regex engines, “[:punct:]” stands for the character class corresponding to the ispunct() function in C (see “man 3 ispunct” on Unix-like systems). According to ISO/IEC 9899:1990 (ISO C90), ispunct() tests for any printing character except for the space or a character for which isalnum() is true.

Base R with PCRE yields on the current author’s machine:

x <- ",./|\\<>?;:'\"[]{}-=_+()*&^%$€#@!`~×‒„”"
regmatches(x, gregexpr("[[:punct:]]", x, perl=TRUE))  # base R
## [[1]]
##  [1] ","  "."  "/"  "|"  "\\" "<"  ">"  "?"  ";"  ":"  "'"  "\"" "["  "]" 
## [15] "{"  "}"  "-"  "="  "_"  "+"  "("  ")"  "*"  "&"  "^"  "%"  "$"  "#" 
## [29] "@"  "!"  "`"  "~"

However, the details of the characters’ belongingness to this class depend on the current locale. Therefore, the reader might obtain different results when calling the above.

ICU, on the other hand, always gives:

stri_extract_all_regex(x, "[[:punct:]]")    # equivalently: \p{P}
## [[1]]
##  [1] ","  "."  "/"  "\\" "?"  ";"  ":"  "'"  "\"" "["  "]"  "{"  "}"  "-" 
## [15] "_"  "("  ")"  "*"  "&"  "%"  "#"  "@"  "!"  "‒"  "„"  "”"

Here, [:punct:] is merely a synonym for \p{P}. Further, \p{S} captures symbols:

stri_extract_all_regex(x, "\\p{S}")         # symbols
## [[1]]
##  [1] "|" "<" ">" "=" "+" "^" "$" "€" "`" "~" "×"

We strongly recommend, wherever possible, using the portable “[\p{P}\p{S}]” as an alternative to the PCRE’s “[:punct:]”.

Alternating and Grouping Subexpressions#

Alternation Operator#

The alternation operator, “|”, matches either its left or its right branch, for instance:

x <- "spam, egg, ham, jam, algae, and an amalgam of spam, all al dente"
stri_extract_all_regex(x, "spam|ham")
## [[1]]
## [1] "spam" "ham"  "spam"

|” has very low precedence. Therefore, if we wish to introduce an alternative of subexpressions, we need to group them, e.g., between round brackets. For instance, “(sp|h)am” matches either “spam” or “ham”.

Note that this has the side-effect of creating new capturing groups; see Capture Groups and References Thereto.

Grouping Subexpressions#

Also, matching is always done left-to-right, on a first-come, first-served basis. So, if the left branch is a subset of the right one, the latter will never be matched. In particular, “(al|alga|algae)” can only match “al”. To fix this, we can write “(algae|alga|al)”.

Non-grouping Parentheses#

Some parenthesised subexpressions – those in which the opening bracket is followed by the question mark – have a distinct meaning. In particular, “(?#...)” denotes a free-format comment that is ignored by the regex parser:

  "(?# match 'sp' or 'h')(sp|h)(?# and 'am')am|(?# or match 'egg')egg")
## [[1]]
## [1] "spam" "egg"  "ham"  "spam"

Nevertheless, constructing more sophisticated regexes by concatenating subfragments thereof may sometimes be more readable:

      "(sp|h)",   # match either 'sp' or 'h'
      "am",       # followed by 'am'
    "|",            # ... or ...
      "egg"       # just match 'egg'
## [[1]]
## [1] "spam" "egg"  "ham"  "spam"

What is more, e.g., “(?i)” enables the case_insensitive mode.

stri_count_regex("Spam spam SPAMITY spAm", "(?i)spam")
## [1] 4


More often than not, a variable number of instances of the same subexpression needs to be captured, or its presence should be optional. This can be achieved by means of the following quantifiers:

  • ?” matches 0 or 1 times;

  • *” matches 0 or more times;

  • +” matches 1 or more times;

  • {n,m}” matches between n and m times;

  • {n,}” matches at least n times;

  • {n}” matches exactly n times.

These operators are applied to the preceding atoms. For example, “ba+” captures "ba", "baa", "baaa", etc., but neither "b" alone nor "bababa" altogether.

By default, the quantifiers are greedy – they match the repeated subexpression as many times as possible. The “?” suffix (hence, quantifiers such as “??”, “*?”, “+?”, and so forth) tries with as few occurrences as possible (to obtain a match still).

x <- "sp(AM)(maps)(SP)am"
  c("\\(.+\\)",    # [[1]] greedy
    "\\(.+?\\)",   # [[2]] lazy
    "\\([^)]+\\)"  # [[3]] greedy (but clever)
## [[1]]
## [1] "(AM)(maps)(SP)"
## [[2]]
## [1] "(AM)"   "(maps)" "(SP)"  
## [[3]]
## [1] "(AM)"   "(maps)" "(SP)"

The first regex is greedy: it matches an opening bracket, then as many characters as possible (including “)”) that are followed by a closing bracket. The two other patterns terminate as soon as the first closing bracket is found.

  c("sp(am|nom)+",             "sp(am|nom)+?",
    "sp(am|nom)+?m*",          "sp(am|nom)+?m+"))
## [1] "spamamamnomnomnomam"         "spam"                       
## [3] "spam"                        "spamamamnomnomnomammmmmmmmm"

Let’s stress that the quantifier is applied to the subexpression that stands directly before it. Grouping parentheses can be used in case they are needed.

stri_extract_all_regex("12, 34.5, 678.901234, 37...629, ...",
  c("\\d+\\.\\d+", "\\d+(\\.\\d+)?"))
## [[1]]
## [1] "34.5"       "678.901234"
## [[2]]
## [1] "12"         "34.5"       "678.901234" "37"         "629"

Here, the first regex matches digits, a dot, and another series of digits. The second one finds digits that are possibly (but not necessarily) followed by a dot and a digit sequence.

Performance Notes#

ICU, just like PCRE, uses a nondeterministic finite automaton-type algorithm. Hence, due to backtracking, some ill-defined regexes can lead to exponential matching times (e.g., “(a+)+b” applied on "aaaa...aaaaac"). If such patterns are expected, setting the time_limit or stack_limit option is recommended.

  stri_detect_regex("a" %s*% 1000 %s+% "c", "(a+)+b", time_limit=1e5)
}, error=function(e) cat("stopped.")))
## stopped.
##    user  system elapsed 
##  11.135   0.000  11.135

Nevertheless, oftentimes such regexes can be naturally reformulated to fix the underlying issue. The ICU User Guide on Regular Expressions also recommends using possessive quantifiers (”?+”, “*+”, “++”, and so on), which match as many times as possible but, contrary to the plain-greedy ones, never backtrack when they happen to consume too much data.

See also the documentation of the re2r and re2 (wrappers around the RE2 library) packages and the references therein for a discussion.

Capture Groups and References Thereto#

Round-bracketed subexpressions carry one additional characteristic: they form the so-called capture groups that can be extracted separately or be referred to in other parts of the same regex.

Extracting Capture Group Matches#

The above is evident when we use the versions of stri_extract() that are sensitive to the presence of capture groups:

x <- "name='Sir Launcelot', quest='Seek the Grail', favecolour='blue'"
stri_match_all_regex(x, "(\\w+)='(.+?)'")
## [[1]]
##      [,1]                     [,2]         [,3]            
## [1,] "name='Sir Launcelot'"   "name"       "Sir Launcelot" 
## [2,] "quest='Seek the Grail'" "quest"      "Seek the Grail"
## [3,] "favecolour='blue'"      "favecolour" "blue"

The findings are presented in a matrix form. The first column gives the complete matches, the second column stores the matches to the first capture group, and so forth.

If we just need the grouping part of “(...)”, i.e., without the capturing feature, “(?:...)” can be applied. Also, named capture groups defined like “(?<name>...)” are fully supported since version 1.7.1 of our package (for historical notes see [4]).

stri_match_all_regex(x, "(?:\\w+)='(?<value>.+?)'")
## [[1]]
##                               value           
## [1,] "name='Sir Launcelot'"   "Sir Launcelot" 
## [2,] "quest='Seek the Grail'" "Seek the Grail"
## [3,] "favecolour='blue'"      "blue"

Locating Capture Group Matches#

The capture_groups attribute in stri_locate__regex enables us to pinpoint the matches to the parenthesised subexpressions as well:

stri_locate_all_regex(x, "(?<key>\\w+)='(?<value>.+?)'",
  capture_groups=TRUE, get_length=TRUE)
## [[1]]
##      start length
## [1,]     1     20
## [2,]    23     22
## [3,]    47     17
## attr(,"capture_groups")
## attr(,"capture_groups")$key
##      start length
## [1,]     1      4
## [2,]    23      5
## [3,]    47     10
## attr(,"capture_groups")$value
##      start length
## [1,]     7     13
## [2,]    30     14
## [3,]    59      4

Note that each item in the resulting list is equipped with a "capture_groups" attribute. For instance, attr(result[[1]], "capture_groups")[[2]] extracts the locations of the matches to the 2nd capture group in the first input string.

Replacing with Capture Group Matches#

Matches to particular capture groups can be recalled in replacement strings when using stri_replace(). Here, the match in its entirety is denoted with “$0”, then “$1” stores whatever was caught by the first capture group, “$2” is the match to the second capture group, etc. Moreover, “\$” gives the dollar-sign.

stri_replace_all_regex(x, "(\\w+)='(.+?)'", "$2 is a $1")
## [1] "Sir Launcelot is a name, Seek the Grail is a quest, blue is a favecolour"

Named capture groups can be referred to too:

stri_replace_all_regex(x, "(?<key>\\w+)='(?<value>.+?)'",
  "${value} is a ${key}")
## [1] "Sir Launcelot is a name, Seek the Grail is a quest, blue is a favecolour"


Matches to capture groups can also be part of the regexes themselves. For example, “\1” denotes whatever has been consumed by the first capture group.

Even though, in general, parsing HTML code with regexes is not recommended, let us consider the following examples:

  c("<[a-z]+>.*?</[a-z]+>", "<([a-z]+)>.*?</\\1>"))
## [[1]]
## [1] "<strong><em>spam</em>" "<code>eggs</code>"    
## [[2]]
## [1] "<strong><em>spam</em></strong>" "<code>eggs</code>"

The second regex guarantees that the match will include all characters between the opening <tag> and the corresponding (not: any) closing </tag>. Named capture groups can be referenced using the \k<name> syntax (the angle brackets are part of the token), as in, e.g., “<(?<tagname>[a-z]+)>.*?</\k<tagname>>”.


Lastly, let’s mention how to match a pattern at a given abstract position within a string.

Matching at the Beginning or End of a String#

^” and “$” match, respectively, the start and the end of the string (or each line within a string, if the multi_line option is set to TRUE).

x <- c("spam egg", "bacon spam", "spam", "egg spam bacon", "sausage")
p <- c("spam", "^spam", "spam$", "spam$|^spam", "^spam$")
structure(outer(x, p, stri_detect_regex), dimnames=list(x, p))
##                 spam ^spam spam$ spam$|^spam ^spam$
## spam egg        TRUE  TRUE FALSE        TRUE  FALSE
## bacon spam      TRUE FALSE  TRUE        TRUE  FALSE
## spam            TRUE  TRUE  TRUE        TRUE   TRUE
## egg spam bacon  TRUE FALSE FALSE       FALSE  FALSE
## sausage        FALSE FALSE FALSE       FALSE  FALSE

The 5 regular expressions match “spam”, respectively, anywhere within the string, at the beginning, at the end, at the beginning or end, and in strings that are equal to the pattern itself.

Matching at Word Boundaries#

Furthermore, “\b” matches at a “word boundary”, e.g., near spaces, punctuation marks, or at the start/end of a string (i.e., wherever there is a transition between a word, “\w”, and a non-word character, “\W”, or vice versa).

In the following example, we match all stand-alone numbers (this regular expression is provided for didactic purposes only):

stri_extract_all_regex("12, 34.5, J23, 37.629cm", "\\b\\d+(\\.\\d+)?+\\b")
## [[1]]
## [1] "12"   "34.5"

Note the possessive quantifier, “?+”: try matching a dot and a sequence of digits, and if it’s present but not followed by a word boundary, do not retry by matching a word boundary only.

Looking Behind and Ahead#

There are also ways to guarantee that a pattern occurrence begins or ends with a match to some subexpression: “(?<=...)...” is the so-called look-behind, whereas “...(?=...)” denotes the look-ahead. Moreover, “(?<!...)...” and “...(?!...)” are their negated (“negative look-behind/ahead”) versions.

stri_extract_all_regex("I like spam, spam, eggs, and spam.",
  c("\\w+(?=[,.])", "\\w++(?![,.])"))
## [[1]]
## [1] "spam" "spam" "eggs" "spam"
## [[2]]
## [1] "I"    "like" "and"

The first regex captures words that end with “,” or “.”. The second one matches words that end neither with “,” nor “.”.