str_match_variable: First match from multiple subjects, variable argument syntax
In namedCapture: Named Capture Regular Expressions

Description Usage Arguments Value Author(s) Examples

Extract the first match of a named capture regex pattern from each of several subject strings. This function uses variable_args_list to analyze the arguments and str_match_named to perform the matching. For the first match in every row of a data.frame, using a different regex for each column, use df_match_variable. For all matches in one character subject use str_match_all_variable; for all matches in several character subjects use str_match_all_named.

1 2	str_match_variable(subject.vec, ..., nomatch.error = FALSE)

`subject.vec`	The subject character vector.
`...`	name1=pattern1, fun1, etc, which creates the regex (?P<name1>pattern1) and uses fun1 for conversion. These other arguments specify the regular expression pattern and must be character/function/list. All patterns must be character vectors of length 1. If the pattern is a named argument in R, we will add a named capture group (?P<name>pattern) in the regex. All patterns are pasted together to obtain the final pattern used for matching. Each named pattern may be followed by at most one function which is used to convert the previous named pattern. Lists are parsed recursively for convenience.
`nomatch.error`	if TRUE, stop with an error if any subject does not match; otherwise (default), subjects that do not match are reported as missing/NA rows of the result.

matrix or data.frame with one row for each subject, and one column for each named group, see str_match_named for details.

Toby Dylan Hocking

named.subject.vec <- c(
  ten="chr10:213,054,000-213,055,000",
  M="chrM:111,000",
  one="chr1:110-111 chr2:220-222") # two possible matches.
## str_match_variable finds the first match in each element of the
## subject character vector. Named arguments are used to create
## named capture groups, which become column names in the
## result. Since the subject is named, those names are used for the
## rownames of the result.
(mat.subject.names <- namedCapture::str_match_variable(
  named.subject.vec,
  chrom="chr.*?",
  ":",
  chromStart="[0-9,]+",
  list( # un-named list becomes non-capturing group.
    "-",
    chromEnd="[0-9,]+"
  ), "?")) # chromEnd is optional.

## When no type conversion functions are specified, the result is a
## character matrix.
str(mat.subject.names)

## Conversion functions are used to convert the previously named
## group, and patterns may be saved in lists for re-use.
keep.digits <- function(x)as.integer(gsub("[^0-9]", "", x))
int.pattern <- list("[0-9,]+", keep.digits)
range.pattern <- list(
  name="chr.*?", # will be used for rownames when subject is un-named.
  ":",
  chromStart=int.pattern,
  list(
    "-",
    chromEnd=int.pattern
  ), "?")

## Rownames taken from subject if it has names.
(df.subject.names <- namedCapture::str_match_variable(
  named.subject.vec, range.pattern))

## Conversion functions used to create non-char columns.
str(df.subject.names)

## Rownames taken from name group if subject is un-named.
namedCapture::str_match_variable(
  unname(named.subject.vec), range.pattern)

## NA used to indicate no match or missing subject.
na.vec <- c(
  nomatch="this will not match",
  missing=NA, # neither will this.
  named.subject.vec)
namedCapture::str_match_variable(
  na.vec, range.pattern)