This vignette compares stringr functions to their base R equivalents to help users transitioning from using base R to stringr.
Overall differences
We’ll begin with a lookup table between the most important stringr functions and their base R equivalents.
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Overall the main differences between base R and stringr are:
stringr functions start with
str_prefix; base R string functions have no consistent naming scheme.The order of inputs is usually different between base R and stringr. In base R, the
patternto match usually comes first; in stringr, thestringto manupulate always comes first. This makes stringr easier to use in pipes, and withlapply()orpurrr::map().Functions in stringr tend to do less, where many of the string processing functions in base R have multiple purposes.
The output and input of stringr functions has been carefully designed. For example, the output of
str_locate()can be fed directly intostr_sub(); the same is not true ofregpexpr()andsubstr().Base functions use arguments (like
perl,fixed, andignore.case) to control how the pattern is interpreted. To avoid dependence between arguments, stringr instead uses helper functions (likefixed(),regex(), andcoll()).
Next we’ll walk through each of the functions, noting the similarities and important differences. These examples are adapted from the stringr documentation and here they are contrasted with the analogous base R operations.
Detect matches
str_detect(): Detect the presence or absence of a
pattern in a string
Suppose you want to know whether each word in a vector of fruit names contains an “a”.
fruit <- c("apple", "banana", "pear", "pineapple")
# base
grepl(pattern = "a", x = fruit)
#> [1] TRUE TRUE TRUE TRUE
# stringr
str_detect(fruit, pattern = "a")
#> [1] TRUE TRUE TRUE TRUEIn base you would use grepl() (see the “l” and think
logical) while in stringr you use str_detect() (see the
verb “detect” and think of a yes/no action).
str_which(): Find positions matching a pattern
Now you want to identify the positions of the words in a vector of fruit names that contain an “a”.
# base
grep(pattern = "a", x = fruit)
#> [1] 1 2 3 4
# stringr
str_which(fruit, pattern = "a")
#> [1] 1 2 3 4In base you would use grep() while in stringr you use
str_which() (by analogy to which()).
str_count(): Count the number of matches in a
string
How many “a”s are in each fruit?
# base
loc <- gregexpr(pattern = "a", text = fruit, fixed = TRUE)
sapply(loc, function(x) length(attr(x, "match.length")))
#> [1] 1 3 1 1
# stringr
str_count(fruit, pattern = "a")
#> [1] 1 3 1 1This information can be gleaned from gregexpr() in base,
but you need to look at the match.length attribute as the
vector uses a length-1 integer vector (-1) to indicate no
match.
str_locate(): Locate the position of patterns in a
string
Within each fruit, where does the first “p” occur? Where are all of the “p”s?
fruit3 <- c("papaya", "lime", "apple")
# base
str(gregexpr(pattern = "p", text = fruit3))
#> List of 3
#> $ : int [1:2] 1 3
#> ..- attr(*, "match.length")= int [1:2] 1 1
#> ..- attr(*, "index.type")= chr "chars"
#> ..- attr(*, "useBytes")= logi TRUE
#> $ : int -1
#> ..- attr(*, "match.length")= int -1
#> ..- attr(*, "index.type")= chr "chars"
#> ..- attr(*, "useBytes")= logi TRUE
#> $ : int [1:2] 2 3
#> ..- attr(*, "match.length")= int [1:2] 1 1
#> ..- attr(*, "index.type")= chr "chars"
#> ..- attr(*, "useBytes")= logi TRUE
# stringr
str_locate(fruit3, pattern = "p")
#> start end
#> [1,] 1 1
#> [2,] NA NA
#> [3,] 2 2
str_locate_all(fruit3, pattern = "p")
#> [[1]]
#> start end
#> [1,] 1 1
#> [2,] 3 3
#>
#> [[2]]
#> start end
#>
#> [[3]]
#> start end
#> [1,] 2 2
#> [2,] 3 3Subset strings
str_sub(): Extract and replace substrings from a
character vector
What if we want to grab part of a string?
hw <- "Hadley Wickham"
# base
substr(hw, start = 1, stop = 6)
#> [1] "Hadley"
substring(hw, first = 1)
#> [1] "Hadley Wickham"
# stringr
str_sub(hw, start = 1, end = 6)
#> [1] "Hadley"
str_sub(hw, start = 1)
#> [1] "Hadley Wickham"
str_sub(hw, end = 6)
#> [1] "Hadley"In base you could use substr() or
substring(). The former requires both a start and stop of
the substring while the latter assumes the stop will be the end of the
string. The stringr version, str_sub() has the same
functionality, but also gives a default start value (the beginning of
the string). Both the base and stringr functions have the same order of
expected inputs.
In stringr you can use negative numbers to index from the right-hand side string: -1 is the last letter, -2 is the second to last, and so on.
str_sub(hw, start = 1, end = -1)
#> [1] "Hadley Wickham"
str_sub(hw, start = -5, end = -2)
#> [1] "ckha"Both base R and stringr subset are vectorized over their parameters. This means you can either choose the same subset across multiple strings or specify different subsets for different strings.
al <- "Ada Lovelace"
# base
substr(c(hw,al), start = 1, stop = 6)
#> [1] "Hadley" "Ada Lo"
substr(c(hw,al), start = c(1,1), stop = c(6,7))
#> [1] "Hadley" "Ada Lov"
# stringr
str_sub(c(hw,al), start = 1, end = -1)
#> [1] "Hadley Wickham" "Ada Lovelace"
str_sub(c(hw,al), start = c(1,1), end = c(-1,-2))
#> [1] "Hadley Wickham" "Ada Lovelac"stringr will automatically recycle the first argument to the same
length as start and stop:
str_sub(hw, start = 1:5)
#> [1] "Hadley Wickham" "adley Wickham" "dley Wickham" "ley Wickham"
#> [5] "ey Wickham"Whereas the base equivalent silently uses just the first value:
substr(hw, start = 1:5, stop = 15)
#> [1] "Hadley Wickham"
str_sub() <-: Subset assignment
substr() behaves in a surprising way when you replace a
substring with a different number of characters:
# base
x <- "ABCDEF"
substr(x, 1, 3) <- "x"
x
#> [1] "xBCDEF"str_sub() does what you would expect:
# stringr
x <- "ABCDEF"
str_sub(x, 1, 3) <- "x"
x
#> [1] "xDEF"
str_subset(): Keep strings matching a pattern, or find
positions
We may want to retrieve strings that contain a pattern of interest:
# base
grep(pattern = "g", x = fruit, value = TRUE)
#> character(0)
# stringr
str_subset(fruit, pattern = "g")
#> character(0)
str_extract(): Extract matching patterns from a
string
We may want to pick out certain patterns from a string, for example, the digits in a shopping list:
shopping_list <- c("apples x4", "bag of flour", "10", "milk x2")
# base
matches <- regexpr(pattern = "\\d+", text = shopping_list) # digits
regmatches(shopping_list, m = matches)
#> [1] "4" "10" "2"
matches <- gregexpr(pattern = "[a-z]+", text = shopping_list) # words
regmatches(shopping_list, m = matches)
#> [[1]]
#> [1] "apples" "x"
#>
#> [[2]]
#> [1] "bag" "of" "flour"
#>
#> [[3]]
#> character(0)
#>
#> [[4]]
#> [1] "milk" "x"
# stringr
str_extract(shopping_list, pattern = "\\d+")
#> [1] "4" NA "10" "2"
str_extract_all(shopping_list, "[a-z]+")
#> [[1]]
#> [1] "apples" "x"
#>
#> [[2]]
#> [1] "bag" "of" "flour"
#>
#> [[3]]
#> character(0)
#>
#> [[4]]
#> [1] "milk" "x"Base R requires the combination of regexpr() with
regmatches(); but note that the strings without matches are
dropped from the output. stringr provides str_extract() and
str_extract_all(), and the output is always the same length
as the input.
str_match(): Extract matched groups from a string
We may also want to extract groups from a string. Here I’m going to use the scenario from Section 14.4.3 in R for Data Science.
head(sentences)
#> [1] "The birch canoe slid on the smooth planks."
#> [2] "Glue the sheet to the dark blue background."
#> [3] "It's easy to tell the depth of a well."
#> [4] "These days a chicken leg is a rare dish."
#> [5] "Rice is often served in round bowls."
#> [6] "The juice of lemons makes fine punch."
noun <- "([A]a|[Tt]he) ([^ ]+)"
# base
matches <- regexec(pattern = noun, text = head(sentences))
do.call("rbind", regmatches(x = head(sentences), m = matches))
#> [,1] [,2] [,3]
#> [1,] "The birch" "The" "birch"
#> [2,] "the sheet" "the" "sheet"
#> [3,] "the depth" "the" "depth"
#> [4,] "The juice" "The" "juice"
# stringr
str_match(head(sentences), pattern = noun)
#> [,1] [,2] [,3]
#> [1,] "The birch" "The" "birch"
#> [2,] "the sheet" "the" "sheet"
#> [3,] "the depth" "the" "depth"
#> [4,] NA NA NA
#> [5,] NA NA NA
#> [6,] "The juice" "The" "juice"As for extracting the full match base R requires the combination of two functions, and inputs with no matches are dropped from the output.
Manage lengths
str_length(): The length of a string
To determine the length of a string, base R uses nchar()
(not to be confused with length() which gives the length of
vectors, etc.) while stringr uses str_length().
# base
nchar(letters)
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# stringr
str_length(letters)
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1There are some subtle differences between base and stringr here.
nchar() requires a character vector, so it will return an
error if used on a factor. str_length() can handle a factor
input.
# stringr
str_length(factor("abc"))
#> [1] 3Note that “characters” is a poorly defined concept, and technically
both nchar() and str_length() returns the
number of code points. This is usually the same as what you’d consider
to be a charcter, but not always:
x <- c("\u00fc", "u\u0308")
x
#> [1] "ü" "ü"
nchar(x)
#> [1] 1 2
str_length(x)
#> [1] 1 2
str_pad(): Pad a string
To pad a string to a certain width, use stringr’s
str_pad(). In base R you could use sprintf(),
but unlike str_pad(), sprintf() has many other
functionalities.
# base
sprintf("%30s", "hadley")
#> [1] " hadley"
sprintf("%-30s", "hadley")
#> [1] "hadley "
# "both" is not as straightforward
# stringr
rbind(
str_pad("hadley", 30, "left"),
str_pad("hadley", 30, "right"),
str_pad("hadley", 30, "both")
)
#> [,1]
#> [1,] " hadley"
#> [2,] "hadley "
#> [3,] " hadley "
str_trunc(): Truncate a character string
The stringr package provides an easy way to truncate a character
string: str_trunc(). Base R has no function to do this
directly.
str_trim(): Trim whitespace from a string
Similarly, stringr provides str_trim() to trim
whitespace from a string. This is analogous to base R’s
trimws() added in R 3.3.0.
# base
trimws(" String with trailing and leading white space\t")
#> [1] "String with trailing and leading white space"
trimws("\n\nString with trailing and leading white space\n\n")
#> [1] "String with trailing and leading white space"
# stringr
str_trim(" String with trailing and leading white space\t")
#> [1] "String with trailing and leading white space"
str_trim("\n\nString with trailing and leading white space\n\n")
#> [1] "String with trailing and leading white space"The stringr function str_squish() allows for extra
whitespace within a string to be trimmed (in contrast to
str_trim() which removes whitespace at the beginning and/or
end of string). In base R, one might take advantage of
gsub() to accomplish the same effect.
# stringr
str_squish(" String with trailing, middle, and leading white space\t")
#> [1] "String with trailing, middle, and leading white space"
str_squish("\n\nString with excess, trailing and leading white space\n\n")
#> [1] "String with excess, trailing and leading white space"
str_wrap(): Wrap strings into nicely formatted
paragraphs
strwrap() and str_wrap() use different
algorithms. str_wrap() uses the famous Knuth-Plass
algorithm.
gettysburg <- "Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal."
# base
cat(strwrap(gettysburg, width = 60), sep = "\n")
#> Four score and seven years ago our fathers brought forth on
#> this continent, a new nation, conceived in Liberty, and
#> dedicated to the proposition that all men are created
#> equal.
# stringr
cat(str_wrap(gettysburg, width = 60), "\n")
#> Four score and seven years ago our fathers brought forth
#> on this continent, a new nation, conceived in Liberty, and
#> dedicated to the proposition that all men are created equal.Note that strwrap() returns a character vector with one
element for each line; str_wrap() returns a single string
containing line breaks.
Mutate strings
str_replace(): Replace matched patterns in a
string
To replace certain patterns within a string, stringr provides the
functions str_replace() and str_replace_all().
The base R equivalents are sub() and gsub().
Note the difference in default input order again.
fruits <- c("apple", "banana", "pear", "pineapple")
# base
sub("[aeiou]", "-", fruits)
#> [1] "-pple" "b-nana" "p-ar" "p-neapple"
gsub("[aeiou]", "-", fruits)
#> [1] "-ppl-" "b-n-n-" "p--r" "p-n--ppl-"
# stringr
str_replace(fruits, "[aeiou]", "-")
#> [1] "-pple" "b-nana" "p-ar" "p-neapple"
str_replace_all(fruits, "[aeiou]", "-")
#> [1] "-ppl-" "b-n-n-" "p--r" "p-n--ppl-"case: Convert case of a string
Both stringr and base R have functions to convert to upper and lower case. Title case is also provided in stringr.
dog <- "The quick brown dog"
# base
toupper(dog)
#> [1] "THE QUICK BROWN DOG"
tolower(dog)
#> [1] "the quick brown dog"
tools::toTitleCase(dog)
#> [1] "The Quick Brown Dog"
# stringr
str_to_upper(dog)
#> [1] "THE QUICK BROWN DOG"
str_to_lower(dog)
#> [1] "the quick brown dog"
str_to_title(dog)
#> [1] "The Quick Brown Dog"In stringr we can control the locale, while in base R locale distinctions are controlled with global variables. Therefore, the output of your base R code may vary across different computers with different global settings.
# stringr
str_to_upper("i") # English
#> [1] "I"
str_to_upper("i", locale = "tr") # Turkish
#> [1] "İ"Join and split
str_flatten(): Flatten a string
If we want to take elements of a string vector and collapse them to a
single string we can use the collapse argument in
paste() or use stringr’s str_flatten().
# base
paste0(letters, collapse = "-")
#> [1] "a-b-c-d-e-f-g-h-i-j-k-l-m-n-o-p-q-r-s-t-u-v-w-x-y-z"
# stringr
str_flatten(letters, collapse = "-")
#> [1] "a-b-c-d-e-f-g-h-i-j-k-l-m-n-o-p-q-r-s-t-u-v-w-x-y-z"The advantage of str_flatten() is that it always returns
a vector the same length as its input; to predict the return length of
paste() you must carefully read all arguments.
str_dup(): duplicate strings within a character
vector
To duplicate strings within a character vector use
strrep() (in R 3.3.0 or greater) or
str_dup():
fruit <- c("apple", "pear", "banana")
# base
strrep(fruit, 2)
#> [1] "appleapple" "pearpear" "bananabanana"
strrep(fruit, 1:3)
#> [1] "apple" "pearpear" "bananabananabanana"
# stringr
str_dup(fruit, 2)
#> [1] "appleapple" "pearpear" "bananabanana"
str_dup(fruit, 1:3)
#> [1] "apple" "pearpear" "bananabananabanana"
str_split(): Split up a string into pieces
To split a string into pieces with breaks based on a particular
pattern match stringr uses str_split() and base R uses
strsplit(). Unlike most other functions,
strsplit() starts with the character vector to modify.
fruits <- c(
"apples and oranges and pears and bananas",
"pineapples and mangos and guavas"
)
# base
strsplit(fruits, " and ")
#> [[1]]
#> [1] "apples" "oranges" "pears" "bananas"
#>
#> [[2]]
#> [1] "pineapples" "mangos" "guavas"
# stringr
str_split(fruits, " and ")
#> [[1]]
#> [1] "apples" "oranges" "pears" "bananas"
#>
#> [[2]]
#> [1] "pineapples" "mangos" "guavas"The stringr package’s str_split() allows for more
control over the split, including restricting the number of possible
matches.
str_glue(): Interpolate strings
It’s often useful to interpolate varying values into a fixed string.
In base R, you can use sprintf() for this purpose; stringr
provides a wrapper for the more general purpose glue package.
name <- "Fred"
age <- 50
anniversary <- as.Date("1991-10-12")
# base
sprintf(
"My name is %s my age next year is %s and my anniversary is %s.",
name,
age + 1,
format(anniversary, "%A, %B %d, %Y")
)
#> [1] "My name is Fred my age next year is 51 and my anniversary is Saturday, October 12, 1991."
# stringr
str_glue(
"My name is {name}, ",
"my age next year is {age + 1}, ",
"and my anniversary is {format(anniversary, '%A, %B %d, %Y')}."
)
#> My name is Fred, my age next year is 51, and my anniversary is Saturday, October 12, 1991.Order strings
str_order(): Order or sort a character vector
Both base R and stringr have separate functions to order and sort strings.
# base
order(letters)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
#> [24] 24 25 26
sort(letters)
#> [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
#> [18] "r" "s" "t" "u" "v" "w" "x" "y" "z"
# stringr
str_order(letters)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
#> [24] 24 25 26
str_sort(letters)
#> [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
#> [18] "r" "s" "t" "u" "v" "w" "x" "y" "z"Some options in str_order() and str_sort()
don’t have analogous base R options. For example, the stringr functions
have a locale argument to control how to order or sort. In
base R the locale is a global setting, so the outputs of
sort() and order() may differ across different
computers. For example, in the Norwegian alphabet, å comes after z:
The stringr functions also have a numeric argument to
sort digits numerically instead of treating them as strings.