capture_first_df: Capture first match in columns of a data frame

In nc: Named Capture to Data Tables

Capture first match in columns of a data frame

Description

Capture first matching text from one or more character columns of a data frame, using a different regular expression for each column.

Usage

capture_first_df(..., 
    nomatch.error = getOption("nc.nomatch.error", 
        TRUE), existing.error = getOption("nc.existing.error", 
        TRUE), engine = getOption("nc.engine", 
        "PCRE"))

Arguments

...	subject data frame, colName1=list(groupName1=pattern1, fun1, etc), colName2=list(etc), etc. First argument must be a data frame with one or more character columns of subjects for matching. If the first argument is a data table then it will be modified using set (for memory efficiency, to avoid copying the whole data table); otherwise the input data frame will be copied to a new data table. Each other argument must be named using a column name of the subject data frame, e.g. colName1, colName2. Each other argument value must be a list that specifies the regex/conversion to use (in string/function/list format as documented in capture_first_vec, which is used on each named column), and possibly a column-specific engine to use.
nomatch.error	if TRUE (default), stop with an error if any subject does not match; otherwise subjects that do not match are reported as missing/NA rows of the result.
existing.error	if TRUE (default to avoid data loss), stop with an error if any capture groups have the same name as an existing column of subject.
engine	character string, one of PCRE, ICU, RE2. This engine will be used for each column, unless another engine is specified for that column in ...

Value

data.table with same number of rows as subject, with an additional column for each named capture group specified in ...

Author(s)

Toby Dylan Hocking

Examples

## The JobID column can be match with a complicated regular
## expression, that we will build up from small sub-pattern list
## variables that are easy to understand independently.
(sacct.df <- data.frame(
  JobID = c(
    "13937810_25", "13937810_25.batch",
    "13937810_25.extern", "14022192_[1-3]", "14022204_[4]"),
  Elapsed = c(
    "07:04:42", "07:04:42", "07:04:49",
    "00:00:00", "00:00:00"),
  stringsAsFactors=FALSE))

## Just match the end of the range.
int.pattern <- list("[0-9]+", as.integer)
end.pattern <- list(
  "-",
  task.end=int.pattern)
nc::capture_first_df(sacct.df, JobID=list(
  end.pattern, nomatch.error=FALSE))

## Match the whole range inside square brackets.
range.pattern <- list(
  "[[]",
  task.start=int.pattern,
  end.pattern, "?", #end is optional.
  "[]]")
nc::capture_first_df(sacct.df, JobID=list(
  range.pattern, nomatch.error=FALSE))

## Match either a single task ID or a range, after an underscore.
task.pattern <- list(
  "_",
  list(
    task.id=int.pattern,
    "|",#either one task(above) or range(below)
    range.pattern))
nc::capture_first_df(sacct.df, JobID=task.pattern)

## Match type suffix alone.
type.pattern <- list(
  "[.]",
  type=".*")
nc::capture_first_df(sacct.df, JobID=list(
  type.pattern, nomatch.error=FALSE))

## Match task and optional type suffix.
task.type.pattern <- list(
  task.pattern,
  type.pattern, "?")
nc::capture_first_df(sacct.df, JobID=task.type.pattern)

## Match full JobID and Elapsed columns.
nc::capture_first_df(
  sacct.df,
  JobID=list(
    job=int.pattern,
    task.type.pattern),
  Elapsed=list(
    hours=int.pattern,
    ":",
    minutes=int.pattern,
    ":",
    seconds=int.pattern))

## If input is data table then it is modified for memory efficiency,
## to avoid copying entire table.
sacct.DT <- data.table::as.data.table(sacct.df)
nc::capture_first_df(sacct.df, JobID=task.pattern)
sacct.df #not modified.
nc::capture_first_df(sacct.DT, JobID=task.pattern)
sacct.DT #modified!

nc documentation built on Sept. 1, 2023, 1:07 a.m.