Wenhu Next Generation Bioinformatician



Fundamental introduction to regular expression (regex or regexp for short) in R. Repost by indicating the source please!

Why I talk about regex

One week ago, I encoutered a problem when I was learning The Art of R Programming (page 35), I would like to use pattern match, that is regex, however, something unusual happened. I sought for help on stackoverflow, you could find the question here (unfortunately duplicated). The answer by akrun invokes my interest on regex!

Regex is kind of independent but important concept that is almost implemented by every programming language. Although this post is titled with “01”, I suggest, you could come back learning this when you come across some concrete problems related, that would make you understand more clearly!

Regular Expression

Have you ever thought about the questions concerning about known patterns when you face bunch of biological data, most probably sequence information (such as basepairs and amino acids infos): Is there any TATA box inside the DNA sequence? May there be some lncRNA contained in the RNA? Or how many zinc finger motifs are there in the peptide chain? On the other hand, you would also probably need to solve the problem from time to time, like replace some texts compliant with fixed patterns with other new texts.

Regex is nearly an invincible sword stabbing down all the pattern matching problems!

Regular expression (normally regex or regexp for short) is a series of consecutive characters that defines specific patterns for matching. It complies with certain syntax rules, when you use regex, the programming language will invoke related engines (built-in or third-party softwares) to search for pattern matched text.


Based on the concept before, you may think of regex as some pattern writing specification, be one and only, adopted by most programming languages. However, shit happens, there are dozens of different regex syntax (we call them flavors), what’s more, several languages implement more than one out there, like R! The most two popular syntax flavors are BRE (Basic Regular Expression) / ERE (Extended Regular Expression) compliant with POSIX (Portable Operating System Interface for uniX) standards and PCRE (Perl Compatible Regular Expression), details on regular expression in wiki.

In R (version > 2.10.0), it adopts two flavors: Ville Laurikari’s TRE as default, PCRE as option.

In this tutorial, I will only introduce PCRE flavor, it is very popular and efficient, I will also strongly recommend you would also try it, simply by adding perl = TRUE in the pattern matching functions, details below.

Common syntax

Fortunately, people are lazy! Although there are several flavors, people would never like to build the wheel from scratch, that would cause most part of these flavors share common syntax. Below I will introduce several frequently used syntax in PCRE. (adapted from learn regex the easy way)

Basic matching

“the” => The fat cat sat on the mat.

“The” => The fat cat sat on the mat. (case-sensitive as default)

Meta characters

  1. .(period):wildcard character, it would match all single character except newline.

    ".ar" => The car parked in the garage.

  2. [...](character set):You could use any character inside to match a single position, and hyphen could be used to indicate range, e.g. [a-z] means each character between a and z, including them.

    "[Tt]he" => The car parked in the garage.

  3. [^...](negated character set):Every character inside would not be matched.

    "[^c]ar" => The car parked in the garage.

  4. +, *, ?(repetition):
    • +:repeat for 1 or more times "c.+t" => The fat cat sat on the mat.
    • *:repeat for 0 or more times "[a-z]*" => The car parked in the garage #21.
    • ?:repeat for 1 or 0 times "[T]?he" => The car is parked in the garage.
  5. {n, m}:Like Point 4, it restrain the repetition among n and m, including them. NOTE: {n} means n times only, and {n, } means greater than or equal to n times.

    "[0-9]{2,3}" => The number was 9.9997 but we rounded it off to 10.0.

  6. ()(subpattern):The pattern not characters inside should be matched exactly.

    "(ar\si)" => The car is parked in the garage. \s means whitespace)

  7. |(alternation):any one is OK.

    "(T|t)he|car" => The car is parked in the garage.

  8. Anchors: ^ means the character followed must be the first in input, and $ indicates the character before must be the last in input:

    "^(T|t)he" => The car is parked in the garage.

    "(at\.)$" => The fat cat. sat. on the mat.

Shorthand character sets

shorthand definition
\w match all alphanumeric character: [a-zA-Z0-9]
\W Contrary to the above: [^\w]
\d match all digits: [0-9]
\D Contrary to the above: [^\d]
\s match all whitespace character, such as [\t\n\r]
\S Contrary to the above: [^\s]


This is a very interesting and useful syntax you should know about.

Just suppose, if we want to locate the pattern X with subpattern Y around, how could we do that? You may directly match the pattern X, but how would you exclude the X without Y nearby? This is the show time for “lookaround”, we would look ahead and behind for pattern X, with eyes fixed on Y!

  • X(?=Y)(positive lookahead):match the X before Y.

    "(T|t)he(?=\sfat)" => The fat cat sat on the mat.

  • X(?!Y)(negative lookahead):Not match the X before Y.

    "(T|t)he(?!\sfat)" => The fat cat sat on the mat.

  • (?<=Y)X(positive lookbehind):match the X behind Y.

    "(?<=(T|t)he\s)(fat|mat)" => The fat cat sat on the mat.

  • (?<!Y)X(negative lookbehind):Not match the X behind Y.

    "(?<!(T|t)he\s)(cat)" => The cat sat on cat.

Note: the pattern Y in “lookaround” would not be captured by the searching engines, this could be beneficial under some circumstances, refer to my question.

Above are the commonly used PCRE syntax, if you want to know the details, please go to PCRE syntax.

How to use regex in R

There are normally two ways doing pattern match in R, one is using the functions in base package, another is using a series of functions in stringr package, I will only introduce the former, however, I strongly suggest you to discover the beauty of the latter, you could get a quick start in R for data science - Strings. (adapted from Regular Expressions with The R Language)


  • grep function:* The meaning of “grep” is “global search for regular expression and print matching lines”, it is the most important function and the model for others. *The first argument is pattern, the second in the input string, waiting for being matched. When you specify value=FALSE, it will return the position of each matched element, value=TRUE would return you exactly the elements matched.
> grep("a+", c("abc", "def", "cba a", "aa"), perl=TRUE, value=FALSE)
[1] 1     3       4
> grep("a+", c("abc", "def", "cba a", "aa"), perl=TRUE, value=TRUE)
[1] "abc" "cba a" "aa"
  • grepl function:”l” stands for “logical”, you could then guess what will be returned.
> grepl("a+", c("abc", "def", "cba a", "aa"), perl=TRUE)
  • regexpr function:The above two function will only tell you which element has been matched, but how could we know the exact matching location inside the element? You need to use regexpr or gregexpr. They have the same arguments setting as grep. regexpr will return the first matching location when searching from left to right with the pattern you give! -1 stands for no match.
> regexpr("a+", c("abc", "def", "cba a", "aa"), perl=TRUE)
[1]  1 -1  3  1
[1]  1 -1  1  2
[1] TRUE

This fuction will return a vector which has the same length with input, containing the first matching locations, the vector has two attributes, the first means the length of matched strings, like the fourth matched text here is "aa", so the length is 2, you don’t need to know the second right now.

  • gregexpr function:It has an additional “g” compared to regexpr, yes, it is a globally matching function, it will return a list, each element inside contain the overall matched location for each input.
> gregexpr("a+", c("abc", "def", "cba a", "aa"), perl=TRUE)
[1] 1
[1] 1
[1] TRUE

[1] -1
[1] -1
[1] TRUE

[1] 3 5
[1] 1 1
[1] TRUE

[1] 1
[1] 2
[1] TRUE
  • regmatches function:This one is used for showing the exact matched part of each input elements. It needs two arguments: one is the pattern, another is the value returned by regexpr or gregexpr, because it contains the matched location and matched length.
> x <- c("abc", "def", "cba a", "aa")
> m <- regexpr("a+", x, perl=TRUE)
> regmatches(x, m)
[1] "a"  "a"  "aa"

> m <- gregexpr("a+", x, perl=TRUE)
> regmatches(x, m)
[1] "a"


[1] "a" "a"

[1] "aa"


  • sub and gsub functions:Obviously, they need three arguments: pattern, replacement and input. Please check examples below to figure out the differences between sub and gsub.
> sub("(a+)", "z", c("abc", "def", "cba a", "aa"), perl=TRUE)
[1] "zbc"   "def"   "cbz a" "z"    
> gsub("(a+)", "z", c("abc", "def", "cba a", "aa"), perl=TRUE)
[1] "zbc"   "def"   "cbz z" "z"
  • One more efficient way for replacement is to use regmatches function, you could assign a vector containing the replacements to the regmatches, then it will change the original texts automatically.
> x <- c("abc", "def", "cba a", "aa")
> m <- gregexpr("a+", x, perl=TRUE)
> regmatches(x, m) <- list(c("one"), character(0), c("two", "three"), c("four"))
> x
[1] "onebc"       "def"         "cbtwo three" "four"


Although regex has been used widely, you still need a while to get used to this scattered syntax, after you pass this period, you will enjoy most of your time using it! This tutorial is just a brief introduction to some important part of regex, if you want more, and you should, I recommend the following resources:

  1. regex-info
  2. regex-online-learning
  3. Regular-Expressions-Cookbook

Hope you will find a match with regex!

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