Introduction to stringr


Strings are not glamorous, high-profile components of R, but they do play a big role in many data cleaning and preparations tasks. R provides a solid set of string operations, but because they have grown organically over time, they can be inconsistent and a little hard to learn. Additionally, they lag behind the string operations in other programming languages, so that some things that are easy to do in languages like Ruby or Python are rather hard to do in R. The stringr package aims to remedy these problems by providing a clean, modern interface to common string operations.

More concretely, stringr:

To meet these goals, stringr provides two basic families of functions:

As of version 1.0, stringr is a thin wrapper around stringi, which implements all the functions in stringr with efficient C code based on the ICU library. Compared to stringi, stringr is considerably simpler: it provides fewer options and fewer functions. This is great when you’re getting started learning string functions, and if you do need more of stringi’s power, you should find the interface similar.

These are described in more detail in the following sections.

Basic string operations

There are three string functions that are closely related to their base R equivalents, but with a few enhancements:

Three functions add new functionality:

Pattern matching

stringr provides pattern matching functions to detect, locate, extract, match, replace, and split strings. I’ll illustrate how they work with some strings and a regular expression designed to match (US) phone numbers:

strings <- c(
  "219 733 8965", 
  "Work: 579-499-7527; Home: 543.355.3679"
phone <- "([2-9][0-9]{2})[- .]([0-9]{3})[- .]([0-9]{4})"


Each pattern matching function has the same first two arguments, a character vector of strings to process and a single pattern (regular expression) to match. The replace functions have an additional argument specifying the replacement string, and the split functions have an argument to specify the number of pieces.

Unlike base string functions, stringr offers control over matching not through arguments, but through modifier functions, regex(), coll() and fixed(). This is a deliberate choice made to simplify these functions. For example, while grepl has six arguments, str_detect() only has two.

Regular expressions

To be able to use these functions effectively, you’ll need a good knowledge of regular expressions, which this vignette is not going to teach you. Some useful tools to get you started:

When writing regular expressions, I strongly recommend generating a list of positive (pattern should match) and negative (pattern shouldn’t match) test cases to ensure that you are matching the correct components.

Functions that return lists

Many of the functions return a list of vectors or matrices. To work with each element of the list there are two strategies: iterate through a common set of indices, or use Map() to iterate through the vectors simultaneously. The second strategy is illustrated below:

col2hex <- function(col) {
  rgb <- col2rgb(col)
  rgb(rgb["red", ], rgb["green", ], rgb["blue", ], max = 255)

# Goal replace colour names in a string with their hex equivalent
strings <- c("Roses are red, violets are blue", "My favourite colour is green")

colours <- str_c("\\b", colors(), "\\b", collapse="|")
# This gets us the colours, but we have no way of replacing them
str_extract_all(strings, colours)
#> [[1]]
#> [1] "red"  "blue"
#> [[2]]
#> [1] "green"

# Instead, let's work with locations
locs <- str_locate_all(strings, colours)
Map(function(string, loc) {
  hex <- col2hex(str_sub(string, loc))
  str_sub(string, loc) <- hex
}, strings, locs)
#> $`Roses are red, violets are blue`
#> [1] "Roses are #FF0000, violets are blue"
#> [2] "Roses are red, violets are #0000FF" 
#> $`My favourite colour is green`
#> [1] "My favourite colour is #00FF00"

Another approach is to use the second form of str_replace_all(): if you give it a named vector, it applies each pattern = replacement in turn:

matches <- col2hex(colors())
names(matches) <- str_c("\\b", colors(), "\\b")

str_replace_all(strings, matches)
#> [1] "Roses are #FF0000, violets are #0000FF"
#> [2] "My favourite colour is #00FF00"


stringr provides an opinionated interface to strings in R. It makes string processing simpler by removing uncommon options, and by vigorously enforcing consistency across functions. I have also added new functions that I have found useful from Ruby, and over time, I hope users will suggest useful functions from other programming languages. I will continue to build on the included test suite to ensure that the package behaves as expected and remains bug free.