Overview of nc functionality"
In nc: Named Capture to Data Tables

Overview of nc functionality

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
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Here is an index of topics which are explained in the different vignettes, along with an overview of functionality using simple examples.

Capture first match in several subjects

Capture first is for the situation when your input is a character vector (each element is a different subject), you want find the first match of a regex to each subject, and your desired output is a data table (one row per subject, one column per capture group in the regex).

subject.vec <- c(
  "chr10:213054000-213,055,000",
  "chrM:111000",
  "chr1:110-111 chr2:220-222")
nc::capture_first_vec(
  subject.vec, chrom="chr.*?", ":", chromStart="[0-9,]+", as.integer)

A variant is doing the same thing, but with input subjects coming from a data table/frame with character columns.

subject.dt <- data.table::data.table(
  JobID = c("13937810_25", "14022192_1"),
  Elapsed = c("07:04:42", "07:04:49"))
int.pat <- list("[0-9]+", as.integer)
nc::capture_first_df(
  subject.dt,
  JobID=list(job=int.pat, "_", task=int.pat),
  Elapsed=list(hours=int.pat, ":", minutes=int.pat, ":", seconds=int.pat))

Capture all matches in a single subject

Capture all is for the situation when your input is a single character string or text file subject, you want to find all matches of a regex to that subject, and your desired output is a data table (one row per match, one column per capture group in the regex).

nc::capture_all_str(
  subject.vec, chrom="chr.*?", ":", chromStart="[0-9,]+", as.integer)

Reshape a data table with regularly named columns

Capture melt is for the situation when your input is a data table/frame that has regularly named columns, and your desired output is a data table with those columns reshaped into a taller/longer form. In that case you can use a regex to identify the columns to reshape.

(one.iris <- data.frame(iris[1,]))
nc::capture_melt_single  (one.iris, part  =".*", "[.]", dim   =".*")
nc::capture_melt_multiple(one.iris, column=".*", "[.]", dim   =".*")
nc::capture_melt_multiple(one.iris, part  =".*", "[.]", column=".*")

Helper functions for defining complex pattterns

Helpers describes various functions that simplify the definition of complex regex patterns. For example nc::field helps avoid repetition below,

subject.vec <- c("sex_child1", "age_child1", "sex_child2")
pattern <- list(
  variable="age|sex", "_",
  nc::field("child", "", "[12]", as.integer))
nc::capture_first_vec(subject.vec, pattern)

It also explains how to define common sub-patterns which are used in several different alternatives.

subject.vec <- c("mar 17, 1983", "26 sep 2017", "17 mar 1984")
pattern <- nc::alternatives_with_shared_groups(
  month="[a-z]{3}", day="[0-9]{2}", year="[0-9]{4}",
  list(month, " ", day, ", ", year),
  list(day, " ", month, " ", year))
nc::capture_first_vec(subject.vec, pattern)