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# General Design Principles

> 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].

The API of the early releases of stringi has been designed to be fairly compatible with that of the 0.6.2 version of the stringr package [11] (dated 2012[1]), with some fixes in the consistency of the handling of missing values and zero-length vectors, amongst others. However, instead of being merely thin wrappers around base R[2] functions, which we have identified as not necessarily portable across platforms and not really suitable for natural language processing tasks, all the functionality has been implemented from the ground up, with the use of ICU services wherever applicable. Since the initial release, an abundance of new features has been added, and the package can now be considered a complete workhorse for text data processing.

Note that the stringi API is stable. Future releases will aim for as much backward compatibility as possible so that other software projects can safely rely on it.

## Naming

Function and argument names use a combination of lowercase letters and underscores (and no dots). To avoid namespace clashes, all function names feature the “`stri_`” prefix. Names are quite self-explanatory, e.g., `stri_locate_first_regex` and `stri_locate_all_fixed` find, respectively, the first match to a regular expression and all occurrences of a substring as-is.

## Vectorisation

Individual character (or code point) strings can be entered using double quotes or apostrophes:

    "spam"  # or 'spam'
    ## [1] "spam"

However, as the R language does not feature any classical scalar types, strings are wrapped around atomic vectors of type “`character`”:

    typeof("spam")  # object type; see also is.character() and is.vector()
    ## [1] "character"
    length("spam")  # how many strings are in this vector?
    ## [1] 1

Hence, we will be using the terms “string” and “character vector of length 1” interchangeably.

Not having a separate scalar type is very convenient; the so-called vectorisation strategy encourages writing code that processes whole collections of objects, all at once, regardless of their size.

For instance, given the following character vector:

    pythons <- c("Graham Chapman", "John Cleese", "Terry Gilliam",
      "Eric Idle", "Terry Jones", "Michael Palin")

we can separate the first and the last names from each other (assuming for simplicity that no middle names are given), using just a single function call:

    (pythons <- stri_split_fixed(pythons, " ", simplify=TRUE))
    ##      [,1]      [,2]
    ## [1,] "Graham"  "Chapman"
    ## [2,] "John"    "Cleese"
    ## [3,] "Terry"   "Gilliam"
    ## [4,] "Eric"    "Idle"
    ## [5,] "Terry"   "Jones"
    ## [6,] "Michael" "Palin"

Due to vectorisation, we can generally avoid using the `for`- and `while`-loops (“for each string in a vector…”). This can make the code much more readable, maintainable, and faster to execute.

## Acting Elementwise with Recycling

Binary and higher-arity operations in R are oftentimes vectorised with respect to all arguments (or at least to the crucial, non-optional ones). As a prototype, let’s consider the binary arithmetic, logical, or comparison operators (and, to some extent, `paste()`, `strrep()`, and more generally `mapply()`), for example the multiplication:

    c(10, -1) * c(1, 2, 3, 4)  # == c(10, -1, 10, -1) * c(1, 2, 3, 4)
    ## [1] 10 -2 30 -4

Calling “`x * y`” multiplies the corresponding components of the two vectors elementwisely. As one operand happens to be shorter than the other, the former is recycled as many times as necessary to match the length of the latter (there would be a warning if partial recycling occurred). Also, acting on a zero-length input always yields an empty vector.

All functions in stringi follow this convention (with some obvious exceptions, such as the `collapse` argument in `stri_join()`, `locale` in `stri_datetime_parse()`, etc.). In particular, all string search functions are vectorised with respect to both the `haystack` and the `needle` arguments (and, e.g., the replacement string, if applicable).

Some users, unaware of this rule, might find this behaviour unintuitive at the beginning and thus miss out on how powerful it is. Therefore, let’s enumerate the most noteworthy scenarios that are possible thanks to the arguments’ recycling, using the call to `stri_count_fixed(haystack, needle)` (which looks for a needle in a haystack) as an illustration:

- many strings – one pattern:

      stri_count_fixed(c("abcd", "abcabc", "abdc", "dab", NA), "abc")
      ## [1]  1  2  0  0 NA

  (there is 1 occurrence of `"abc"` in `"abcd"`, 2 in `"abcabc"`, and so forth);

- one string – many patterns:

      stri_count_fixed("abcdeabc", c("def", "bc", "abc", NA))
      ## [1]  0  2  2 NA

  (`"def"` does not occur in `"abcdeabc"`, `"bc"` can be found therein twice, etc.);

- each string – its own corresponding pattern:

      stri_count_fixed(c("abca", "def", "ghi"), c("a", "z", "h"))
      ## [1] 2 0 1

  (there are two `"a"`s in `"abca"`, no `"z"` in `"def"`, and one `"h"` in `"ghi"`);

- each row in a matrix – its own corresponding pattern:

      (haystack <- matrix(  # example input
      do.call(stri_join,
          expand.grid(
          c("a", "b", "c"), c("a", "b", "c"), c("a", "b", "c")
          )), nrow=3))
      ##      [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9]
      ## [1,] "aaa" "aba" "aca" "aab" "abb" "acb" "aac" "abc" "acc"
      ## [2,] "baa" "bba" "bca" "bab" "bbb" "bcb" "bac" "bbc" "bcc"
      ## [3,] "caa" "cba" "cca" "cab" "cbb" "ccb" "cac" "cbc" "ccc"
      needle <- c("a", "b", "c")
      matrix(stri_count_fixed(haystack, needle),  # call to stringi
      nrow=3, dimnames=list(needle, NULL))
      ##   [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
      ## a    3    2    2    2    1    1    2    1    1
      ## b    1    2    1    2    3    2    1    2    1
      ## c    1    1    2    1    1    2    2    2    3

  (this looks for `"a"` in the 1st row of `haystack`, `"b"` in the 2nd row, and `"c"` in the 3rd; in particular, there are 3 `"a"`s in `"aaa"`, 2 in `"aba"`, and 1 `"b"` in `"baa"`; this is possible because matrices are represented as “flat” vectors of length `nrow*ncol`, whose elements are read in a column-major (Fortran) order; therefore, here, the pattern `"a"` is being sought in the 1st, 4th, 7th, … string in `haystack`, i.e., `"aaa"`, `"aba"`, `"aca"`, …; pattern `"b"` in the 2nd, 5th, 8th, … string; and `"c"` in the 3rd, 6th, 9th, … one);

  On a side note, to match different patterns with respect to each column, we can (amongst others) apply matrix transpose twice (`t(stri_count_fixed(t(haystack), needle))`).

- all strings – all patterns:

      haystack <- c("aaa", "bbb", "ccc", "abc", "cba", "aab", "bab", "acc")
      needle <- c("a", "b", "c")
      structure(
      outer(haystack, needle, stri_count_fixed),
      dimnames=list(haystack, needle))  # add row and column names
      ##     a b c
      ## aaa 3 0 0
      ## bbb 0 3 0
      ## ccc 0 0 3
      ## abc 1 1 1
      ## cba 1 1 1
      ## aab 2 1 0
      ## bab 1 2 0
      ## acc 1 0 2

  (which computes the counts over the Cartesian product of the two arguments)

  This is equivalent to:

      matrix(
      stri_count_fixed(rep(haystack, each=length(needle)), needle),
      byrow=TRUE, ncol=length(needle), dimnames=list(haystack, needle))
      ##     a b c
      ## aaa 3 0 0
      ## bbb 0 3 0
      ## ccc 0 0 3
      ## abc 1 1 1
      ## cba 1 1 1
      ## aab 2 1 0
      ## bab 1 2 0
      ## acc 1 0 2

## Missing Values

Some base R string processing functions, e.g., `paste()`, treat missing values as literal `"NA"` strings. stringi, however, does enforce the consistent propagation of missing values (like arithmetic operations):

    paste(c(NA_character_, "b", "c"), "x", 1:2)  # base R
    ## [1] "NA x 1" "b x 2"  "c x 1"
    stri_join(c(NA_character_, "b", "c"), "x", 1:2)  # stringi
    ## Warning in stri_join(c(NA_character_, "b", "c"), "x", 1:2): longer object
    ##     length is not a multiple of shorter object length
    ## [1] NA    "bx2" "cx1"

For dealing with missing values, we may rely on the convenience functions such as `stri_omit_na()` or `stri_replace_na()`.

## Data Flow

All vector-like arguments (including factors and objects) in stringi are treated in the same manner. For example, if a function expects a character vector on input and an object of another type is provided, `as.character()` is called first (we see that in the example above, “`1:2`” is treated as `c("1", "2")`).

Following [11], stringi makes sure the output data types are consistent and that different functions are interoperable. This makes operation chaining easier and less error-prone.

For example, `stri_extract_first_regex()` finds the first occurrence of a pattern in each string. Therefore, the output is a character of the same length as the input (with the recycling rule in place if necessary).

    haystack <- c("bacon", "spam", "jam, spam, bacon, and spam")
    stri_extract_first_regex(haystack, "\\b\\w{1,4}\\b")
    ## [1] NA     "spam" "jam"

Note that a no-match (here, we have been looking for words of at most 4 characters) is marked with a missing string. This makes the output vector size consistent with the length of the inputs.

On the other hand, `stri_extract_all_regex()` identifies all occurrences of a pattern, whose counts may differ from input to input, therefore it yields a list of character vectors.

    stri_extract_all_regex(haystack, "\\b\\w{1,4}\\b", omit_no_match=TRUE)
    ## [[1]]
    ## character(0)
    ##
    ## [[2]]
    ## [1] "spam"
    ##
    ## [[3]]
    ## [1] "jam"  "spam" "and"  "spam"

If the 3rd argument were not specified, a no-match would be represented by a missing value (for consistency with the previous function).

Also, care is taken so that the “data” or “`x`” argument is most often listed as the first one (e.g., in base R we have `grepl(needle, haystack)` vs `stri_detect(haystack, needle)` here). This makes the functions more intuitive to use, but also more forward pipe operator-friendly (either when using “`|>`” introduced in R 4.1 or “`%>%`” from magrittr).

Furthermore, for increased convenience, some functions have been added despite the fact that they can trivially be reduced to a series of other calls. In particular, writing:

    stri_sub_all(haystack,
      stri_locate_all_regex(haystack, "\\b\\w{1,4}\\b", omit_no_match=TRUE))

yields the same result as in the previous example, but refers to `haystack` twice.

## Further Deviations from Base R

stringi can be used as a replacement for the existing string processing functions. Also, it offers many facilities not available in base R. Except for being fully vectorised with respect to all crucial arguments, propagating missing values and empty vectors consistently, and following coherent naming conventions, our functions deviate from their classic counterparts even further.

Following Unicode Standards. Thanks to the comprehensive coverage of the most important services provided by ICU, its users gain access to collation, pattern searching, normalisation, transliteration, etc., that follow the current Unicode standards for text processing in any locale. Due to this, as we state in Character Encodings, all inputs are converted to Unicode. Furthermore, all outputs are always in UTF-8.

Portability Issues in Base R. As mentioned in the introduction, base R string operations have traditionally been limited in scope. There also might be some issues with regards to their portability, reasons for which may be plentiful. For instance, varied versions of the PCRE (8.x or 10.x) pattern matching libraries may be linked to during the compilation of R. On Windows, there is a custom implementation of iconv that has a set of character encoding IDs not fully compatible with that on GNU/Linux: to select the Polish locale, we are required to pass `"Polish_Poland"` to `Sys.setlocale()` on Windows whereas `"pl_PL"` on Linux. Interestingly, R can be built against the system ICU so that it uses its Collator for comparing strings (e.g., using the “`<=`” operator). However, this is only optional and does not provide access to any other Unicode services.

For example, let us consider the matching of “all letters” by means of the built-in `gregexpr()` function and the TRE (`perl=FALSE`) and PCRE (`perl=TRUE`) libraries using a POSIX-like and Unicode-style character set (see Regular Expressions for more details):

    x <- "AEZaezĄĘŻąęż"  # "AEZaez\u0104\u0118\u017b\u0105\u0119\u017c"
    stri_sub(x, gregexpr("[[:alpha:]]", x, perl=FALSE)[[1]], length=1)
    stri_sub(x, gregexpr("[[:alpha:]]", x, perl=TRUE)[[1]],  length=1)
    stri_sub(x, gregexpr("\\p{L}", x, perl=TRUE)[[1]],       length=1)

On Ubuntu Linux 20.04 (UTF-8 locale), the respective outputs are:

    ## [1] "A" "E" "Z" "a" "e" "z" "Ą" "Ę" "Ż" "ą" "ę" "ż"
    ## [1] "A" "E" "Z" "a" "e" "z"
    ## [1] "A" "E" "Z" "a" "e" "z" "Ą" "Ę" "Ż" "ą" "ę" "ż"

On Windows, when `x` is marked as UTF-8 (see Character Encodings), the current author obtained:

    ## [1] "A" "E" "Z" "a" "e" "z"
    ## [1] "A" "E" "Z" "a" "e" "z"
    ## [1] "A" "E" "Z" "a" "e" "z" "Ą" "Ę" "Ż" "ą" "ę" "ż"

And again on Windows using the Polish locale but `x` marked as natively-encoded (CP-1250 in this case):

    ## [1] "A" "E" "Z" "a" "e" "z" "Ę" "ę"
    ## [1] "A" "E" "Z" "a" "e" "z" "Ą" "Ę" "Ż" "ą" "ę" "ż"
    ## [1] "A" "E" "Z" "a" "e" "z" "Ę" "ę"

As we mention in Collation, when stringi links to ICU built from sources (`install.packages("stringi", configure.args="--disable-pkg-config")`), we are always guaranteed to get the same results on every platform.

High Performance of stringi. Because of the aforementioned reasons, functions in stringi do not refer to their base R counterparts. The operations that do not rely on ICU services have been rewritten from scratch with speed and portability in mind. For example, here are some timings of string concatenation:

    x <- stri_rand_strings(length(LETTERS) * 1000, 1000)
    microbenchmark::microbenchmark(
      join2=stri_join(LETTERS, x, sep="", collapse=", "),
      join3=stri_join(x, LETTERS, x, sep="", collapse=", "),
      r_paste2=paste(LETTERS, x, sep="", collapse=", "),
      r_paste3=paste(x, LETTERS, x, sep="", collapse=", ")
    )
    ## Unit: milliseconds
    ##      expr     min      lq    mean  median      uq    max neval
    ##     join2  39.153  40.157  54.064  41.688  52.681 109.94   100
    ##     join3  84.818  88.149  92.345  90.330  94.587 138.78   100
    ##  r_paste2  83.995  87.088 104.517  90.834 105.171 183.10   100
    ##  r_paste3 176.490 182.296 228.608 243.960 258.631 343.04   100

Another example – timings of fixed pattern searching:

    x <- stri_rand_strings(100, 100000, "[actg]")
    y <- "acca"
    microbenchmark::microbenchmark(
      fixed=stri_locate_all_fixed(x, y),
      regex=stri_locate_all_regex(x, y),
      coll=stri_locate_all_coll(x, y),
      r_tre=gregexpr(y, x),
      r_pcre=gregexpr(y, x, perl=TRUE),
      r_fixed=gregexpr(y, x, fixed=TRUE)
    )
    ## Unit: milliseconds
    ##     expr      min       lq     mean  median       uq      max neval
    ##    fixed   4.9861   5.0658   5.1497   5.088   5.1666   5.4805   100
    ##    regex  98.5586  99.1162  99.5049  99.342  99.6874 109.1632   100
    ##     coll 261.6454 262.9504 263.4606 263.463 263.9660 265.3722   100
    ##    r_tre 117.0878 117.4128 117.8794 117.561 117.7429 127.0573   100
    ##   r_pcre  70.8115  71.2062  71.8814  71.410  71.5560  89.8478   100
    ##  r_fixed  23.7709  23.9305  24.0572  24.061  24.1899  24.3638   100

Different Default Arguments and Greater Configurability. Some functions in stringi have different, more natural default arguments, e.g., `paste()` has `sep=" "` but `stri_join()` has `sep=""`. Also, as there is no one-fits-all solution to all problems, many arguments have been introduced for more detailed tuning.

Preserving Attributes. Generally, stringi preserves no object attributes whatsoever, but a user can make sure themself that this is the case, e.g., by calling “`x[] <- stri_...(x, ...)`” or “``attributes<-```(stri_...(x, ...), attributes(x))`”.

[1]

Interestingly, in 2015 the aforementioned stringr package was rewritten as a set of wrappers around some of the stringi functions instead of the base R ones. In Section 14.7 of R for Data Science [12] we read: “stringr is useful when you’re learning because it exposes a minimal set of functions, which have been carefully picked to handle the most common string manipulation functions. stringi, on the other hand, is designed to be comprehensive. It contains almost every function you might ever need: stringi has 250 functions to stringr’s 49”.

[2]

See also the new stringx package which supplies a set of portable and efficient replacements for and enhancements of the base R functions (based on stringi).